Reviewerly/exHarmony huggingface dataset:
An automated earthquake detection algorithm by combining pair-input deep learning and migration location methods

Modern seismology can benefit from a rapid and reliable earthquake catalog preparation process. In recent years Deep Learning (DL)-based methods attracted seismologists&amp;#8217; attention to keep up with the constantly increasing seismic data for maximally processing and locating the recorded events. This study focuses on deploying an optimized workflow that integrates DL and waveform migration algorithms to achieve a comprehensive automated phase detection and earthquake location workflow. The goal is to deeply scan the seismic datasets for Pand S- phases, associate and locate the detected events, and improve the performance of DL algorithms in processing out-of-distribution and low signal-to-noise ratio data. The workflow consists of six steps, including the preparation of one-minute data segments by employing the framework of ObsPy, deep investigation of the recorded data for P- and S- phases by a low threshold EQTransformer (EQT), and a pair-input Siamese EQTransformer (S-EQT), phase association by Rapid Earthquake Association and Location (REAL) method, applying MIgration Location (MIL) to accurately locate the outputs of REAL, and calculating the local magnitude of the located earthquakes. Eighteen months of the Ghana Digital Seismic Network (GHDSN) dataset (2012-2014), is processed by this integrated and automatic workflow, and a catalog of 461 earthquakes is acquired. Although S-EQT and EQT, with the respective number of 758 and 423 earthquakes, show a figurative superiority in the number of detected events, they are scattered with inaccurate hypo-central depth. Conversely, the compiled catalog show high accordance with the previously interpreted seismogenic sources by Mohammadigheymasi et al. (2023), and a new seismogenic source is also delineated. This workflow significantly enhanced the seismic catalog compilation process and lowered the computational costs while increasing the accuracy of phase detection, association, and location processes. This work was supported by the European Union and the Instituto Dom Luiz(IDL) Project under Grant UIDB/50019/2020, and it uses computational resources provided by C4G (Collaboratory for Geosciences) (Ref. PINFRA/22151/2016). P. S. is supported by the DEEP project (http://deepgeothermal.org) funded through the ERANET CofundGEOTHERMICA (Project No. 200320-4001) from the European Commission. The DEEP project benefits from an exploration subsidy of the Swiss federal office of energy for the EGSgeothermal project in Haute-Sorne, canton of Jura (contract number MF-021-GEO-ERK), which is gratefully acknowledged.
A Technology Analysis of Wearable Sensors for Monitoring Healthcare System

Access to quality health care is fundamental to maintaining physical and mental well-being in modern society. If you want to enjoy life's luxuries, you need to take care of your body. It's a fact of life that everyone will, by pure chance, need medical attention. The value of health care cannot be overstated. Recent research has shown that the cost of health care using conventional methods is prohibitive for the average person. Those who cannot afford proper medical attention either have to suffer through their conditions or make sacrifices in their health by choosing less expensive therapies. Wearable sensors have the potential to revolutionise the health care industry by allowing for convenient and inexpensive health monitoring. This article depicts the shift from the standard method of monitoring vital healthcare indicators to the use of wearable sensors. Wearable sensors are attempting to set the groundwork for hopeful results in the medical industry thanks to technological improvements.
Primary School Teachers’ Perspectives on Emergency Remote Teaching of Mathematics: Challenges and Opportunities for the Post-COVID-19 Era

In this study, we aimed to determine Cypriot primary mathematics teachers’ perspectives and lived experiences during the transition to emergency remote teaching (ERT) in the early stage of the COVID-19 pandemic. An in-depth online survey combining closed-ended and open-ended questions was administered to sixty-two (n = 62) educators teaching mathematics in public primary schools during the first lockdown in spring, 2020. The data from closed-ended questions were analyzed using descriptive and inferential statistics, whereas, for the open-ended questions, a thematic analysis approach was employed. Our findings provide useful insights regarding teachers’ self-reported technology backgrounds and levels of instruction regarding the use of technology in mathematics prior to the pandemic, as well their level of preparedness for ERT and the main challenges they faced in implementing ERT of mathematics. Our findings also indicate teachers’ levels of satisfaction with their ERT practices and their beliefs concerning the extent of achievement of the curriculum learning objectives through ERT, and how these varied based on teachers’ self-reported levels of familiarity with technology, their self-reported levels of preparedness for teaching at a distance, and their engagement (or non-engagement) in synchronous instruction during ERT. Teachers’ suggestions, based on their experiences from the lockdown period, regarding how to transform mathematics teaching and learning in the post-COVID-19 era are also presented.
Guilty of hiding a data breach

What does the criminal conviction of Uber's former CISO mean for the information security sector? The case highlights the need for CISOs to ensure that they have a clear line to the board, and a robust plan for how to deal with security incidents ahead of time.
Securing IIoT sensors communication using blockchain technology

Industrial IoT (IIoT) advances organisations to the next level by tracing and managing all entity activities. Moreover, the deployment, dependency, and interaction of these wireless devices, often known as IoT (Internet of Things) devices, creates a number of privacy and security problems. The usage of sensors in industries helps and decreases man power while increasing quality and lowering production costs. Several attacks might be encountered by different attackers by hacking various sensors activities. In this research, a secure wireless scheme based on Blockchain technology which saves the extracted collections of all data into a number of blocks to preserve transparency and secure all the activities of smart sensors is proposed. Furthermore, the proposed blockchain mechanism's simulation results are tested against different security transmission processes. Furthermore, the obtained results are scrutinised and analyzed over certain metrics like ease of attack detection, probability of attack success and falsification attack to assess the authenticity of IoT devices.
A Unified Forensics Analysis Approach to Digital Investigation

Digital forensics is now essential in addressing cybercrime and cyber-enabled crime but potentially it can have a role in almost every other type of crime. Given technology's continuous development and prevalence, the widespread adoption of technologies among society and the subsequent digital footprints that exist, the analysis of these technologies can help support investigations. The abundance of interconnected technologies and telecommunication platforms has significantly changed the nature of digital evidence. Subsequently, the nature and characteristics of digital forensic cases involve an enormous volume of data heterogeneity, scattered across multiple evidence sources, technologies, applications, and services. It is indisputable that the outspread and connections between existing technologies have raised the need to integrate, harmonise, unify and correlate evidence across data sources in an automated fashion. Unfortunately, the current state of the art in digital forensics leads to siloed approaches focussed upon specific technologies or support of a particular part of digital investigation. Due to this shortcoming, the digital investigator examines each data source independently, trawls through interconnected data across various sources, and often has to conduct data correlation manually, thus restricting the digital investigator’s ability to answer high-level questions in a timely manner with a low cognitive load. Therefore, this research paper investigates the limitations of the current state of the art in the digital forensics discipline and categorises common investigation crimes with the necessary corresponding digital analyses to define the characteristics of the next-generation approach. Based on these observations, it discusses the future capabilities of the next-generation unified forensics analysis tool (U-FAT), with a workflow example that illustrates data unification, correlation and visualisation processes within the proposed method.
robslopes: Efficient Computation of the (Repeated) Median Slope

Modern use of slope estimation often involves the (repeated) estimation of a large number of slopes on a large number of data points. Some of the most popular non-parametric and robust alternatives to the least squares estimator are the Theil-Sen and Siegel's repeated median slope estimators. The [robslopes](https://CRAN.R-project.org/package=robslopes) package contains fast algorithms for these slope estimators. The implemented randomized algorithms run in $\mathcal{O}(n\log(n))$ and $\mathcal{O}(n\log^2(n))$ expected time respectively and use $\mathcal{O}(n)$ space. They achieve speedups up to a factor $10^3$ compared with existing implementations for common sample sizes, as illustrated in a benchmark study, and they allow for the possibility of estimating the slopes on samples of size $10^5$ and larger thanks to the limited space usage. Finally, the original algorithms are adjusted in order to properly handle duplicate values in the data set.
Multi-Factor Authentication Protocol Based on Electrocardiography Signals for a Mobile Cloud Computing Environment

Mobile Cloud Computing (MCC) is a highly complex topic that encompasses several information security issues. The authentication area of the various entities involved has been extensively discussed in recent years and shown a wide range of possibilities. The use of inadequate authentication processes leads to several problems, which range from financial damage to users or providers of Mobile Commerce (M-Commerce) services to the death of patients who depend on Mobile Healthcare (M-Health) services.&lt;br&gt;&lt;br&gt;The design of reliable authentication processes that minimize such issues involves the use of non-intrusive authentication techniques and continuous authentication of users by MCC service providers. In this sense, biometrics may satisfy such needs in various scenarios.&lt;br&gt;&lt;br&gt;This research has explored some conceptual bases and presents a continuous authentication protocol for MCC environments. Such a protocol is part of a cyberphysical system (CPS) and is based on the monitoring of physiological information interpreted from users’ electrocardiograms (ECG). Machine learning techniques based on the Adaptative Boost (Adaboost) and Random Undersampling Boost (RUSBoost) were used for the classification of the cardiac cycles recognized in such ECGs.&lt;br&gt;&lt;br&gt;The two ML techniques applied to electrocardiography were compared by a random subsampling technique that considers four analysis metrics, namely accuracy, precision, sensitivity, and F1-score. The experimental results showed better performance of RUSBoost regarding accuracy (97.4%), precision (98.7%), sensitivity (96.1%), and F1- score (97.4%).&lt;br&gt;
Translator of Indonesian Sign Language Video using Convolutional Neural Network with Transfer Learning

&#x0D; Sign language is a language used to communicate by utilizing gestures and facial expressions. This study focuses on classification of Bahasa Isyarat Indonesia (BISINDO). There are still many people who have difficulty communicating with the deaf people. This study builds video-based translator system using Convolutional Neural Network (CNN) with transfer learning which is commonly used in computer vision especially in image classification. Transfer learning used in this study are a MobileNetV2, ResNet50V2, and Xception. This study uses 11 different commonly used vocabularies in BISINDO. Predictions will be made in real-time scenario using a webcam. In addition, the system given good results in the experiment with an interaction approach between one pair of deaf and normal people. From all the experiments, it was found that the Xception architectures has the best F1 Score of 98.5%.&#x0D;
Open world recognition of communication jamming signals

To improve the recognition ability of communication jamming signals, Siamese Neural Network-based Open World Recognition (SNN-OWR) is proposed. The algorithm can recognize known jamming classes, detect new (unknown) jamming classes, and unsupervised cluseter new classes. The network of SNN-OWR is trained supervised with paired input data consisting of two samples from a known dataset. On the one hand, the network is required to have the ability to distinguish whether two samples are from the same class. On the other hand, the latent distribution of known class is forced to approach their own unique Gaussian distribution, which is prepared for the subsequent open set testing. During the test, the unknown class detection process based on Gaussian probability density function threshold is designed, and an unsupervised clustering algorithm of the unknown jamming is realized by using the prior knowledge of known classes. The simulation results show that when the jamming-to-noise ratio is more than 0dB, the accuracy of SNN-OWR algorithm for known jamming classes recognition, unknown jamming detection and unsupervised clustering of unknown jamming is about 95%. This indicates that the SNN-OWR algorithm can make the effect of the recognition of unknown jamming be almost the same as that of known jamming.
[Research progress on medical image dataset expansion methods].

Computer-aided diagnosis (CAD) systems play a very important role in modern medical diagnosis and treatment systems, but their performance is limited by training samples. However, the training samples are affected by factors such as imaging cost, labeling cost and involving patient privacy, resulting in insufficient diversity of training images and difficulty in data obtaining. Therefore, how to efficiently and cost-effectively augment existing medical image datasets has become a research hotspot. In this paper, the research progress on medical image dataset expansion methods is reviewed based on relevant literatures at home and abroad. First, the expansion methods based on geometric transformation and generative adversarial networks are compared and analyzed, and then improvement of the augmentation methods based on generative adversarial networks are emphasized. Finally, some urgent problems in the field of medical image dataset expansion are discussed and the future development trend is prospected.计算机辅助诊断（CAD）系统对现代医学诊疗体系具有非常重要的作用，但其性能受训练样本的限制。而训练样本受成像成本、标记成本和涉及患者隐私等因素的影响，导致训练图像多样性不足且难以获取。因此，如何高效且以较低成本扩充现有医学图像数据集成为研究的热点。本文结合国内外的相关文献，对医学图像数据集扩充方法的研究进展进行综述，首先对比分析基于几何变换和基于生成对抗网络的扩充方法，其次重点介绍基于生成对抗网络扩充方法的改进及其适用场景，最后讨论医学图像数据集扩充领域的一些亟待解决的问题并对其未来发展趋势进行展望。.
Study on Multi-source Data-Driven Static Security Risk Assessment of Power Grids

The increasingly complex power grid structure and the volatility caused by the high proportion of renewable energy have brought great challenges to the traditional static security assessment of power grids. To solve this problem, a multi-source data-driven power grid static security risk assessment method is proposed in this paper. Firstly, the historical operation data of the power grid, including the grid structure of the system, load power, generator power and weather information, are used to build a multi-source data set. Then, a static security assessment model is constructed using long-term and short-term memory neural network and deep neural networks, and the multi-source data sets are used for off-line training. According to the output results of the assessment model, a three-level static security risk assessment index system is then developed. Finally, the proposed method is tested through a provincial 500kV power grid. The example results show that the method proposed in this paper can effectively realize the assessment of power grid static security risks, which can be used to assist the system operators for future intelligent dispatch and control.
Multi-Ideology, Multiclass Online Extremism Dataset, and Its Evaluation Using Machine Learning

Social media platforms play a key role in fostering the outreach of extremism by influencing the views, opinions, and perceptions of people. These platforms are increasingly exploited by extremist elements for spreading propaganda, radicalizing, and recruiting youth. Hence, research on extremism detection on social media platforms is essential to curb its influence and ill effects. A study of existing literature on extremism detection reveals that it is restricted to a specific ideology, binary classification with limited insights on extremism text, and manual data validation methods to check data quality. In existing research studies, researchers have used datasets limited to a single ideology. As a result, they face serious issues such as class imbalance, limited insights with class labels, and a lack of automated data validation methods. A major contribution of this work is a balanced extremism text dataset, versatile with multiple ideologies verified by robust data validation methods for classifying extremism text into popular extremism types such as propaganda, radicalization, and recruitment. The presented extremism text dataset is a generalization of multiple ideologies such as the standard ISIS dataset, GAB White Supremacist dataset, and recent Twitter tweets on ISIS and white supremacist ideology. The dataset is analyzed to extract features for the three focused classes in extremism with TF-IDF unigram, bigrams, and trigrams features. Additionally, pretrained word2vec features are used for semantic analysis. The extracted features in the proposed dataset are evaluated using machine learning classification algorithms such as multinomial Naïve Bayes, support vector machine, random forest, and XGBoost algorithms. The best results were achieved by support vector machine using the TF-IDF unigram model confirming 0.67 F1 score. The proposed multi-ideology and multiclass dataset shows comparable performance to the existing datasets limited to single ideology and binary labels.
Blockchain-based auditing with data self-repair: From centralized system to distributed storage

Cloud storage provides data owners with massive storage and computing resources. To release from heavy data management and maintenance, more and more data owners are willing to outsource data to cloud. However, the data stored in cloud are out of the control of data owners, and may be corrupted and unretrievable. To solve the above problem, this paper proposes a decentralized auditing scheme BVA for the single cloud storage server, and an extended batch auditing scheme E-BVA for distributed storage. Firstly, BVA and E-BVA utilize vector commitment technology to realize lightweight data integrity auditing for outsourced data. Secondly, to avoid privacy and performance risks caused by the third party auditor, both BVA and E-BVA employ a smart contract on the blockchain as an auditor to perform data integrity auditing. Thirdly, both schemes support the verifiability between outsourced data and their corresponding authenticators, which prevents malicious data owners framing cloud storage servers and avoid subsequent dispute arbitration. Finally, based on regenerating code, E-BVA supports data self-repair of corrupted servers, while protecting the privacy of outsourced data and realizing the lightweight computation. The security and performance analyses demonstrate the security and efficiency of BVA and E-BVA.
Conditional Generative Adversarial Network for Monocular Image Depth Map Prediction

Deep map prediction plays a crucial role in comprehending the three-dimensional structure of a scene, which is essential for enabling mobile robots to navigate autonomously and avoid obstacles in complex environments. However, most existing depth estimation algorithms based on deep neural networks rely heavily on specific datasets, resulting in poor resistance to model interference. To address this issue, this paper proposes and implements an optimized monocular image depth estimation algorithm based on conditional generative adversarial networks. The goal is to overcome the limitations of insufficient training data diversity and overly blurred depth estimation contours in current monocular image depth estimation algorithms based on generative adversarial networks. The proposed algorithm employs an enhanced conditional generative adversarial network model with a generator that adopts a network structure similar to UNet and a novel feature upsampling module. The discriminator uses a multi-layer patchGAN conditional discriminator and incorporates the original depth map as input to effectively utilize prior knowledge. The loss function combines the least squares loss function and the L1 loss function. Compared to traditional depth estimation algorithms, the proposed optimization algorithm can effectively restore image contour information and enhance the visualization capability of depth prediction maps. Experimental results demonstrate that our method can expedite the convergence of the model on NYU-V2 and Make3D datasets, and generate predicted depth maps that contain more details and clearer object contours.
Evaluation of GAN-Based Model for Adversarial Training

Deep learning has been successfully utilized in many applications, but it is vulnerable to adversarial samples. To address this vulnerability, a generative adversarial network (GAN) has been used to train a robust classifier. This paper presents a novel GAN model and its implementation to defend against L∞ and L2 constraint gradient-based adversarial attacks. The proposed model is inspired by some of the related work, but it includes multiple new designs such as a dual generator architecture, four new generator input formulations, and two unique implementations with L∞ and L2 norm constraint vector outputs. The new formulations and parameter settings of GAN are proposed and evaluated to address the limitations of adversarial training and defensive GAN training strategies, such as gradient masking and training complexity. Furthermore, the training epoch parameter has been evaluated to determine its effect on the overall training results. The experimental results indicate that the optimal formulation of GAN adversarial training must utilize more gradient information from the target classifier. The results also demonstrate that GANs can overcome gradient masking and produce effective perturbation to augment the data. The model can defend PGD L2 128/255 norm perturbation with over 60% accuracy and PGD L∞ 8/255 norm perturbation with around 45% accuracy. The results have also revealed that robustness can be transferred between the constraints of the proposed model. In addition, a robustness–accuracy tradeoff was discovered, along with overfitting and the generalization capabilities of the generator and classifier. These limitations and ideas for future work will be discussed.