Reviewerly/exHarmony huggingface dataset:
Earthquake Seismic Moment, Rupture Radius, and Stress Drop From P-Wave Displacement Amplitude Versus Time Curves

The reliable determination of earthquake source parameters is a relevant task of seismological investigations which ground nowadays on high-quality seismic waveforms collected by near-source dense arrays of ground motion sensors. Here, we propose a parametric modeling technique which analyzes the time-domain P-wave signal recorded in the near-source range of small-to-large-size earthquakes. Assuming a triangular moment rate function, a uniform speed, and circular rupture model, we develop the equations to estimate the seismic moment, rupture radius, and stress drop from the corner time and plateau level of the average logarithm of the P-wave displacement versus time curves (LPDT). The constant- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> , anelastic attenuation effect, is accounted by a postprocessing procedure that evaluates the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -unperturbed moment rate triangular shape. The methodology has been validated through application to the acceleration records of the 2016–2017 Central Italy and 2007–2019 Japan earthquake sequences covering a wide moment magnitude range (Mw 2.5–6.5) and recording distance <100 km. After correcting for the anelastic attenuation function, the estimated average stress drop and the confidence interval ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\langle \Delta \sigma \rangle =0.60$ </tex-math></inline-formula> (0.42–0.87) MPa and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\langle \Delta \sigma \rangle =1.53$ </tex-math></inline-formula> (1.01–2.31) for crustal and subcrustal events of Japan and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\langle \Delta \sigma \rangle =0.36$ </tex-math></inline-formula> (0.30–0.44) MPa for Central Italy) show for both regions a self-similar, constant stress drop scaling of the rupture duration/radius with seismic moment. The smaller sensitivity of the spatially averaged, time-varying peak displacement amplitude to the radiation from localized high slip patch on the fracture surface could explain the retrieved smaller average stress drops for subcrustal earthquakes in Japan and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M &gt;5.5$ </tex-math></inline-formula> events in Central Italy relative to previous estimates using spectral methods.
Uncertainty-aware credit card fraud detection using deep learning

Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models. Quantifying uncertainty accompanied by point estimation is essential because it mitigates model unfairness and permits practitioners to develop trustworthy systems which abstain from suboptimal decisions due to low confidence. Explicitly, assessing uncertainties associated with DNNs predictions is critical in real-world card fraud detection settings for characteristic reasons, including (a) fraudsters constantly change their strategies, and accordingly, DNNs encounter observations that are not generated by the same process as the training distribution, (b) owing to the time-consuming process, very few transactions are timely checked by professional experts to update DNNs. Therefore, this study proposes three uncertainty quantification (UQ) techniques named Monte Carlo dropout, ensemble, and ensemble Monte Carlo dropout for card fraud detection applied on transaction data. Moreover, to evaluate the predictive uncertainty estimates, UQ confusion matrix and several performance metrics are utilized. Through experimental results, we show that the ensemble is more effective in capturing uncertainty corresponding to generated predictions. Additionally, we demonstrate that the proposed UQ methods provide extra insight to the point predictions, leading to elevate the fraud prevention process.
The severity and effects of Cyber-breaches in SMEs: a machine learning approach

In this paper, we investigate cyber breaches and their effects on small and medium enterprises (SMEs), considering the role that cybersecurity plays in SMEs, and the importance that SMEs have in the economy. Using the Cyber Security Breaches Survey data, the first contribution extends previous works confirming that SMEs receive a wide variety of breaches. Secondly, we have characterized the degree of severity of breaches in SMEs, based on disruption time and their cost. Our last contribution consists of determining the effect and severity of breaches in SMEs in terms of economic, financial and management impacts.
A Track Donation System Using Blockchain

Charities face a difficult funding environment, mainly because charities are not highly transparent. Day over day It became more difficult to know whether donations are reaching the right place, or taking another course, such as financing suspicious campaigns, terrorist projects, etc. As a result, donors lose faith in these organizations, leading to a halt to interactions with charitable organizations. This makes it more difficult for charities to raise money at a great cost. This research proposed a solution to the mentioned problem using Blockchain technology, a decentralized database that provides security, transparency, and lower funding cost by eliminating the presence of third parties between donors and charities. A new track donation model was proposed which introduced several new participants that control the process of donation and remove suspicion of the charity. All donations can be tracked in Blockchain, allowing donors to know where and how their money is being used. The proposed model was implemented using Hyperledger composer then it was tested to obtain a live full track of a donation process. All donations are made using smart contracts, which allow donors to know exactly when and how their donations will be received. The Blockchain track donation system results in time-saving, reducing the cost of donation, and reduces the risk of reaching suspicious campaign donations or terrorist projects. A website was created as a facilitator for users for ease of use of the proposed system.
A Systematic Overview of Blockchain Research

Abstract Blockchain has been receiving growing attention from both academia and practices. This paper aims to investigate the research status of blockchain-related studies and to analyze the development and evolution of this latest hot area via bibliometric analysis. We selected and explored 2451 papers published between 2013 and 2019 from the Web of Science Core Collection database. The analysis considers different dimensions, including annual publications and citation trends, author distribution, popular research themes, collaboration of countries (regions) and institutions, top papers, major publication journals (conferences), supportive funding agencies, and emerging research trends. The results show that the number of blockchain literature is still increasing, and the research priorities in blockchain-related research shift during the observation period from bitcoin, cryptocurrency, blockchain, smart contract, internet of thing, to the distributed ledger, and challenge and the inefficiency of blockchain. The findings of this research deliver a holistic picture of blockchain research, which illuminates the future direction of research, and provides implications for both academic research and enterprise practice.
Machine-Learning-Enhanced Blockchain Consensus With Transaction Prioritization for Smart Cities

In the given technology-driven era, smart cities are the next frontier of technology, and these smart cities aim to improve the quality of people’s lives. In this article, we introduce such future Internet of Things (IoT)-based smart cities that leverage blockchain technology. Particularly, when there are multiple parties involved, blockchain helps in improving the security and transparency of the system in an efficient manner. However, if a current fee-based or first-come–first-serve-based processing is used, emergency events may get delayed and even threaten people’s lives. Thus, there is a need for transaction prioritization based on the priority of information and a dynamic block creation mechanism for efficient data recording and faster event response. Also, our system focuses on the consortium blockchain maintained by a group of members working across different organizations to provide more efficiency. The leader election procedure in such a consortium blockchain becomes more important for the transaction prioritization process to take place honestly. Hence, in our proposed consensus protocol, we deploy a machine-learning (ML) algorithm to achieve efficient leader election, based on which a novel dynamic block creation algorithm is designed. Also, to ensure the honest block generation behavior of the leader, a peer-prediction-based verification mechanism is proposed. Both security analysis and simulation experiments are carried out to demonstrate the robustness, accuracy, and efficiency of our proposed scheme.
Passive and active training approaches for critical infrastructure protection

In order to strengthen Critical Infrastructure's protection and resilience, it is central to invest in training and simulations, to spread a security culture and develop the awareness among all personnel involved in the Critical Infrastructure security. Nowadays, attackers represent a major threat due to the combination of both cyber and kinetic operations, targeting human factors vulnerabilities. It is also critical to develop and straighten a “human firewall” inside critical organizations through the enhancement of Security Education, Training and Awareness (SETA) and stresses the need for the development of a security culture inside organizations. In such scenarios, today, the awareness within organizations, both in public and private sector, is achieved through passive and active training techniques. A hybrid approach is proposed as a powerful compromise between the two that can best deliver the desired level of awareness and meet the needs and satisfaction of employees. Adopting a balanced mix of techniques that comprise engagement-based and less interactive methods seems to be the best way to attain security awareness.
CrowdSR

The prevalence of personal devices motivates the rapid development of crowdsourced livecast in recent years. However, there exists huge diversity of upstream bandwidth among amateur broadcasters. Moreover, the highest video quality that can be streamed is limited by the hardware configuration of broadcaster devices (e.g., 540p for low-end mobile devices). The above factors pose significant challenges to the ingestion of high-resolution live video streams, and result in poor quality-of-experience (QoE) for viewers. In this paper, we propose a novel live video ingest approach called CrowdSR for crowdsourced livecast. CrowdSR can transform a low-resolution video stream uploaded by weak devices into a high-resolution video stream via super-resolution, and then deliver the stream to viewers. CrowdSR can exploit crowdsourced high-resolution video patches from similar broadcasters to speedup model training. Different from previous work, our approach does not require any modification at the client side, and thus is more practical and easy to implement. Finally, we implement and evaluate CrowdSR by conducting a series of real-world experiments. The results show that CrowdSR significantly outperforms the baseline approaches by 0.42-1.09 dB in terms of PSNR and 0.006-0.014 in terms of SSIM.
Communicating with the Deaf and Hard of Hearing through Automatic Arabic Sign Language Translator

Abstract The needs of persons with disabilities are still being undermined even though persons with disabilities have proven to be productive members of the community when given the right education, support, and tools. Assistive devices are being designed, developed, and sold to provide persons with disabilities the tools to be able to become productive members. With the advent of new technologies, software development has also concentrated on the needs of persons with disabilities. With the widespread of software’s especially on mobile devices, researchers can now develop tools for the community to communicate, work, collaborate and deal normally with persons with disabilities. In this paper, an automatic Artificial Intelligence (AI) based Arabic Sign Language Translator is proposed. The system is able to capture the picture of the sign language performed by a person who is deaf or hard of hearing and provide real-time translation of the hand gestures. Novel features are proposed to be extracted from the images to be input to the four different classifiers namely; Random Forest (RF), Bagging classifier, Regression, and Random Tree (RT). The result shows that the Random Forest classifier with proposed extract features performs better by achieving 92.15% accuracy as compared using input image directly with classifiers.
A Two-Stage Density-Aware Single Image Deraining Method

Although advanced single image deraining methods have been proposed, one main challenge remains: the available methods usually perform well on specific rain patterns but can hardly deal with scenarios with dramatically different rain densities, especially when the impacts of rain streaks and the veiling effect caused by rain accumulation are heavily coupled. To tackle this challenge, we propose a two-stage density-aware single image deraining method with gated multi-scale feature fusion. In the first stage, a realistic physics model closer to real rain scenes is leveraged for initial deraining, and a network branch is also trained for rain density estimation to guide the subsequent refinement. The second stage of model-independent refinement is realized using conditional Generative Adversarial Network (cGAN), aiming to eliminate artifacts and improve the restoration quality. In particular, dilated convolutions are applied to extract rain features at multiple scales and gated feature fusion is exploited to better aggregate multi-level contextual information in both stages. Extensive experiments have been conducted on representative synthetic rain datasets and real rain scenes. Quantitative and qualitative results demonstrate the superiority of our method in terms of effectiveness and generalization ability, which outperforms the state-of-the-art.
E-Unet++: A Semantic Segmentation Method for Remote Sensing Images

Semantic segmentation can distinguish objects in remote sensing images at the pixel level. However, traditional semantic segmentation algorithms are more and more difficult to meet people's needs. With the rapid development of deep learning, especially its application in remote sensing images has greatly improved the parsing ability and efficiency. But, the complexity and diversity of remote sensing image content make the accuracy of semantic segmentation still need to be improved. Thus, a semantic segmentation method that combines the characteristics of EfficientNet and UNet++ is proposed in this paper. The method can make the segmentation boundary clearer and improve the segmentation effect of densely distributed objects. The results show that the proposed method achieves good performance in the Vaihingen dataset.
A Comparative Study of Different Face Shape Classification Techniques

Throughout the last years, there has been an increasing interest in developing useful computer vision techniques that help in many fields. Face shape classification is considered a common task in beauty and fashion purposes. The aim of this paper is to represent a comparative study of different supervised learning algorithms used in face shape classification. The classification was based on extracted facial features for the 5 different face shapes: Heart, Square, Long, Oval and Round as labels. Different classification algorithms that use landmark distance ratios and angles as features were compared: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multilayer Perception (MLP), Random Forest (RF), AdaBoost, and Naive Bayes. Around five thousand female celebrities’ images were used for training and testing the different classifiers. The results showed that the SVM classifier with radial basis function kernel achieved the highest overall accuracy of 82%.
GeoForge: investigating integrated virtual reality and personalized websites for collaboration in middle school science

Purpose This paper aims to describe the design and user testing of GeoForge , a multiple-player digital learning experience for middle school that leverages virtual reality (VR) and individualized websites for learning concepts in planetary science. This paper investigates how specific instructional design choices and features of the technology fostered collaborative behaviors. Design/methodology/approach GeoForge was implemented in 3 middle school classrooms with a total of 220 students. Learners used GeoForge in class in groups of 3–4 to learn about planetary science. A mixed-methods approach examined collaboration using classroom observations, teacher interviews, student surveys and student artifacts. Using Jeong and Hmelo-Silver’s (2016) seven affordances of technology for collaborative learning, this paper identifies ways in which features of GeoForge supported collaborative behaviors. Findings Instructional design which combined VR and the digital science journal (DSJ) helped foster collaboration. Some collaborative behaviors were especially notable in classrooms that did not regularly practice these skills. Segmenting tasks in the DSJ, clarifying instructions to articulate ideas, showing other group members’ responses onscreen and enabling multiuser VR environments contributed to collaborative behaviors and a satisfying learning experience as observed and documented through multiple methods. Originality/value GeoForge successfully integrated VR and personalized websites in a classroom planetary science lesson, an approach which balanced instructional design and logistical challenges while creating opportunities for collaboration.
An Echo State Network Imparts a Curve Fitting

Recurrent neural networks (RNNs) are successfully employed in processing information from temporal data. Approaches to training such networks are varied and reservoir computing-based attainments, such as the echo state network (ESN), provide great ease in training. Akin to many machine learning algorithms rendering an interpolation function or fitting a curve, we observe that a driven system, such as an RNN, renders a continuous curve fitting if and only if it satisfies the echo state property. The domain of the learned curve is an abstract space of the left-infinite sequence of inputs and the codomain is the space of readout values. When the input originates from discrete-time dynamical systems, we find theoretical conditions under which a topological conjugacy between the input and reservoir dynamics can exist and present some numerical results relating the linearity in the reservoir to the forecasting abilities of the ESNs.
Accurate and efficient image segmentation and bias correction model based on entropy function and level sets

With the development of society, image segmentation plays a pivotal role in real-world life. However, the images we obtain are often distorted or contaminated by noise and shade, causing low construct and weak boundaries. In this paper, we propose a new model suitable for segmenting and correcting images with inhomogeneous intensity simultaneously. According to the characteristics of the entropy function, we use it as the coefficient of the global and local terms of the energy functional, related to the intensity of the image. Most importantly, the compression function is added to control the global item and the local item in the same range, reducing parameters to be tuned. In addition, our model can be extended to the multi-objective model and the colour model to deal with a more difficult situation. Furthermore, by utilizing the split Bregman method to solve the energy functional, we reduce computational cost and maintain the convergence of the algorithm. A large number of experimental results demonstrate the superiority of our model over previous models. Our model has promising performance for the segmentation and correction of inhomogeneous medical images and colour images.
Simulation study and PID Tune of Automated Guided Vehicles (AGV)

Automated Guided Vehicles (AGV) is a famous robot group which is used in various applications with different shapes. These robot applications start with industries carrying and end with a hospital helping the elderly or carrying medicines. Even recently, AGV usage in libraries has been reported. AGVs mainly consist of fixed terms, and in different applications, they used some addon designed to increase the robot's ability. One of the main research contents of AGV is the control part of the system. Also, the strategies and methods among scholars are different. Some simulation work about AGV control and design has been reported, but most of them missed the complete simulation concept, making the work replication hard. This paper, after a detailed description of AGV SIMULATION description, the PID Tune process with the help of Ziegler Nichols and the empirical method described. Then the Simulation experiments are carried out on Four different paths of the circle, eclipse, spiral, and 8-shape to observe and test the Tuned PID parameters. Finally, after success tracking, the value of the PID controller shows the least and maximum of the following error signal of each applied path belongs to the Circle path and the Spiral path.
Blockchain-Anchored Disaggregated Optical Networks

Optical network disaggregation is attracting significant consensus to avoid vendor lock-in solutions. However, the presence of network controllers, nodes, and hardware/software components potentially provided by different entities and manufacturers may lead to remarkable responsibility issues in case of service level degradation. We propose the use of the blockchain technology to provide reliable and trusted accountability of events and interactions among disaggregated network elements. Three levels of interactions are specifically considered: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> among SDN controllers, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> between each SDN controller and the underlying network nodes, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii)</i> within disaggregated network nodes. The proposed solutions have been implemented and experimentally validated in a disaggregated network testbed. Results show the effectiveness of the method even in case of controversial service level degradation upon failure events. Results also show good scalability performance to retrieve/add/validate blocks recorded in the blockchain even in case of large optical network scenarios.