Reviewerly/exHarmony huggingface dataset:
Quantifying nuisance ground motion thresholds for induced earthquakes

Hazards from induced earthquakes are a growing concern with a need for effective management. One aspect of that concern is the “nuisance” from unexpected ground motions, which have the potential to cause public alarm and discontent. In this article, we borrow earthquake engineering concepts to quantify the chance of building damage states and adapt them to quantify felt thresholds for induced earthquakes in the Central and Eastern United States. We compare binary data of felt or not-felt reports from the “Did You Feel It” database with ShakeMap ground motion intensity measures (IM) for ∼360 earthquakes. We use a Monte Carlo logistic regression to discern the likelihood of perceiving various degrees of felt intensity, given a particular IM. These best-fit nuisance functions are reported in this article and are readily transferable. Of the shaking types considered, we find that peak ground velocity tends to be the best predictor of a felt earthquake. We also find that felt thresholds tended to decrease with increasing earthquake magnitude, after M ∼3.9. We interpret this effect as related to the duration of the event, where events smaller than M 3.9 are perceived as “impulsive” to the human senses. Improved quantification of the nuisance from induced earthquake ground motions could be utilized in management of the public perception of their causal operations. Although aimed at anthropogenic earthquakes, thresholds we derive could be useful in other realms, such as establishing best practices and protocols for earthquake early warning.
Medical image segmentation algorithm based on positive scaling invariant-self encoding CCA

The quality of medical image segmentation results directly affect disease analysis and diagnosis. Although traditional medical image segmentation methods have achieved certain results, this type of method is not only computationally inefficient, but also difficult to achieve fully automatic segmentation. Deep learning has a good generalization ability, which provides a new technical approach to solve the above problems. However, the following problems exist in the application of deep learning in medical image segmentation: (1) Optimization of deep learning models. The parameters of the existing deep learning model have not been well optimized; it is easy to cause wrong medical image segmentation. (2) Overfitting problem. As the number of deep learning model layers increases, it makes the deep model fit the training data easily, but its generalization performance on the test set will be poor. In view of this, in order to solve the problem of deep learning model optimization, this paper first reveals that the weight space of deep learning is not equivalent to its parameter space, and explains in detail how to optimize the deep learning model in a positive scaling invariant space. The method of mapping the derivative of the base path back to the weight, and analyzed the stochastic optimization algorithm in F space. In addition, to solve the overfitting problem of deep learning models, this paper first gives a method of self-encoding Canonical Correlation Analysis (Self-encoding CCA) to optimize both self-coding loss and correlation loss in the fine-tuning stage. Based on the above ideas, this paper proposes a medical image segmentation algorithm based on positive scaling invariant-self encoding CCA. The experimental results show that the index obtained by the method proposed in this paper is not only improved by a large margin compared with traditional machine learning methods, but also improved to a certain extent compared with other deep learning methods.
ALBA: a model-driven framework for the automatic generation of android location-based apps

In recent years, the number of smartphone users has increased dramatically. These users download millions of apps and use them for various services. Due to the significant demand for mobile apps, developers often seek faster development methods and more effective tools and techniques to generate these apps. Many of these apps are location-based apps in which users receive services based on their geographical location. In this paper, we propose a model-driven approach for the automatic generation of Android location-based mobile apps. Our framework, called ALBA, consists of a domain-specific modeling language, a modeling tool, and a plugin which includes model to code transformations. The modeling tool enables a novice designer to model a location-based app. The model is validated against the predefined constraints and the editor prevents creating invalid models. The designer uses the plugin to generate the Android code of the app. The evaluation of our work is two fold. First, to evaluate the generalizability of the ALBA framework, we conducted an experiment which includes the generation of four industrial location-based apps. Second, to evaluate the usability and quality of both the framework and the generated apps, we conducted a case study consists of three experiments. The results of the evaluation are promising both in terms of the applicability of the framework and the quality of the generated apps.
Parataxis or hypotaxis? Choices of taxis in Chinese–English translation

Adopting a Systemic Functional Linguistic approach, the article investigates choices of taxis in Chinese-to-English translation. Previous studies have proposed that Chinese texts tend to favor parataxis more than hypotaxis, and translators are often advised to change the paratactic relation to hypotactic ones in the process of translation. Against such a background, two research questions are proposed: (1) what choices do professional translators actually make when translating Chinese paratactic clauses into English; and (2) how do professional translators translate Chinese hypotactic clauses into English? The data comprises texts on how China fought against COVID-19 and scientific texts extracted from a translation textbook approved by the editing committee of CATTI. Analysis reveals that there are a variety of choices available to the translators, which are discussed and consolidated into a network. Paratactic relations in the Chinese source text can be removed, retained, or changed into hypotactic relations. Hypotactic relations in the Chinese text tend to be retained, although they may also be removed. Different from the popular belief that parataxis in Chinese should be translated into hypotaxis in English, the analysis demonstrates that the choices of removing and retaining paratactic relations are more frequently made than simply changing parataxis into hypotaxis.
Continuity in intuition and insight: from real to naturalistic virtual environment

Abstract Intuition and insight can be deployed on the same continuum. Intuition is the unconscious ability to create links between information; insight is a process by which a sudden comprehension and resolution of a situation arises (i.e. euréka). In the present study, real and virtual environments were used to trigger intuition and insight. The study hypothesised that immersion in real primed environments would facilitate the emergence of intuition and insight in a virtual environment. Forty nine healthy participants were randomly assigned to two groups: “primed” and “non primed.” “Primed” participants were immersed in a real environment with olfactory and visual cues; “non primed” participants did not receive any cues. All participants were exposed to a 3D naturalistic virtual environment which represented a district in Paris via a Head Mounted Display (HMD). Locations presented in the virtual scene (i.e. café places) were related to both olfactory and visual primes (i.e. café) and were based on the continuity between real and virtual environments. Once immersed in the virtual environment, all participants were instructed to use their intuition to envision the selected locations during which Skin Conductance Responses (SCRs) and verbal declarations were recorded. When initiation (a) and immersion (b) phases in the virtual environment were considered, “primed” participants had higher SCRs during the immersion phase than the initiation phase in the virtual environment. They showed higher SRCs during the first part of the virtual immersion than “non primed” participants. During the phenomenological interview, “primed” participants reported a higher number of correct intuitive answers than “non primed” participants. Moreover, “primed” participants “with” insight had higher SCRs during real environment immersion than “primed” participants “without” insight. The findings are consistent with the idea that intuitive decisions in various tasks are based on the activation of pre-existing knowledge, which is unconsciously retrieved, but nevertheless can elicit an intuitive impression of coherence and can generate insight.
Smart Visual Surveillance: Proactive Person Re-identification instead of Impulsive Person Search

Simultaneous person detection and re-identification (re-id) on live CCTV camera feed can handle surveillance issues in a proactive manner instead of applying person search on CCTV video archives after occurrence of some incident. Current person re-id solutions do not deal with person detection and depend on off-the-shelf detectors for their practical use. Whereas person search approaches implicitly detect persons but these work for a given set of pre-cropped persons (query set) instead of keeping track of all on-the-spot persons. In this work we propose an autonomous solution for simultaneous person detection and re-id for all people appearing in a surveillance cameras network. A deep backbone network integrates Region proposal network and region of interest pooling to detect locations of persons in given surveillance images. Concurrently, proposed re-id stream emphasizes on mapping step-wise associations among respective local parts of a person and embeds this information into global person representations. The proposed method is evaluated on a public dataset PRW**PRW: http://zheng-lab.cecs.anu.edu.au/Project/project_prw.html, which is the only public dataset available to evaluate simultaneous person detection and re-identification. The proposed method shows superior performance as compared to the existing methods for particular query set in PRW. Additionally, the proposed method spots and identifies the rest of people (visible in given images and not part of the standard query set), with a great success.
Global context guided hierarchically residual feature refinement network for defocus blur detection

As an important pre-processing step, defocus blur detection makes critical role in various computer vision tasks. However, previous methods cannot obtain satisfactory results due to the complex image background clutter, scale sensitivity and miss of region boundary details. In this paper, for addressing these issues, we introduce a global context guided hierarchically residual feature refinement network (HRFRNet) for defocus blur detection from a natural image. In our network, the low-level fine detail features, high-level semantic and global context information are aggregated in a hierarchical manner to boost the final detection performance. In order to reduce the affect of complex background clutter and smooth regions without enough textures on the final results, we design a multi-scale dilation convolution based global context pooling module to capture the global context information from the most deep feature layer of the backbone feature extraction network. Then, a global context guiding module is introduced to add the global context information into different feature refining stages for guiding the feature refining process. In addition, by considering that the defocus blur is sensitive to image scales, we add a deep features guided fusion module to integrate the outputs of different stages for generating the final score map. Extensive experiments with ablation studies on two commonly used datasets are carried out to validate the superiority of our proposed network when compared with other 11 state-of-the-art methods in terms of both efficiency and accuracy.
Vibro-acoustic modeling, numerical and experimental study of the resonator and its contribution to the timbre of Sarasvati veena, a South Indian stringed instrument

This paper is part of a special issue on Modelling of Musical Instruments. The Sarasvati veena is a South Indian plucked string wooden lute, whose unique timbre is characterized by the presence of nearly all harmonics, in the range from 0 to 2800 Hz, the augmentation of the second and third harmonic and the revival of higher harmonics with time. This can be attributed to the shape of its extended bridge and its wooden resonator. In this work, the vibrational modes of the resonator are studied and the corresponding frequencies related to the musical tones generated while playing the veena. Numerical modal analysis is performed by the finite element method on a computer-aided design model of the resonator top plate as well as the air cavity within the dome-shaped structure. The results are compared to the experiment for validation. The resonator modes provide support to the harmonics of the string vibrations and give rise to the typical timbre of the instrument. We also find that other mode frequencies of the resonator support microtones that are used in Indian raga music. This gives scientific basis to the role of the timbre of the veena in supporting the development and sustenance of South Indian classical music.
Energy Trading Web Platform Based on the Ethereum Smart Contracts and Blockchain

The trade of electricity on the free market is realized through energy exchanges. This article describes the Electricity Trading Web Platform, which stores information about Ethereum Blockchain transactions and the rest of the information in a relational database.
Decision Models in Marketing: The role of Sentiment Analysis from bibliometric analysis

With the exponential growth in the use of social networks and the massive generation of information on the internet, by 2020 it is expected that more than half of the world's population will have access to the internet and 84% will use a social network. These networks act as meeting points between people, create links and virtual communities with common interests or activities and allow contact between them, so they can communicate and exchange information; for its range and development, these digital media have great potential as a tool for companies to get to know their customers and with the increase in social media activities, emotions are seen as valuable products from a commercial perspective. By carefully evaluating people's opinion and feelings, companies can reasonably find out what people think about a product or service, and consequently make the most appropriate decision. The tool that support us to extract this information is the Sentiment Analysis and to learn more about it a review was developed based on a Bibliometric Analysis to know more about its evolution, trends, research areas, authors and outstanding publications.
Private Aggregation With Application to Distributed Optimization

This letter presents a fully distributed private aggregation protocol that can be employed in dynamical networks where communication is only assumed on a neighbor-to-neighbor basis. The novelty of the scheme is its low overhead in communication and computation due to a pre-processing phase that can be executed even before the participants know their input to aggregation. Moreover, the scheme is resilient to node drop-outs, and it is defined without introducing any trusted or untrusted third parties. We prove the privacy of the scheme itself and subsequently, we discuss the privacy leakage caused by the output of the scheme. Finally, we discuss implementation of the proposed protocol to solve distributed optimization problems using two versions of the alternating direction method of multipliers (ADMM).
Detecting discriminatory risk through data annotation based on Bayesian inferences

Thanks to the increasing growth of computational power and data availability, the research in machine learning has advanced with tremendous rapidity. Nowadays, the majority of automatic decision making systems are based on data. However, it is well known that machine learning systems can present problematic results if they are built on partial or incomplete data. In fact, in recent years several studies have found a convergence of issues related to the ethics and transparency of these systems in the process of data collection and how they are recorded. Although the process of rigorous data collection and analysis is fundamental in the model design, this step is still largely overlooked by the machine learning community. For this reason, we propose a method of data annotation based on Bayesian statistical inference that aims to warn about the risk of discriminatory results of a given data set. In particular, our method aims to deepen knowledge and promote awareness about the sampling practices employed to create the training set, highlighting that the probability of success or failure conditioned to a minority membership is given by the structure of the data available. We empirically test our system on three datasets commonly accessed by the machine learning community and we investigate the risk of racial discrimination.
A level set method based on domain transformation and bias correction for MRI brain tumor segmentation

Intensity inhomogeneity is one of the common artifacts in image processing. This artifact makes image segmentation more challenging and adversely affects the performance of intensity-based image processing algorithms. In this paper, a novel region-based level set method is proposed for segmenting the images with intensity inhomogeneity with applications to brain tumor segmentation in magnetic resonance imaging (MRI) scans. For this purpose, the inhomogeneous regions are first modeled as Gaussian distributions with different means and variances, and then transferred into a new domain, where preserves the Gaussian intensity distribution of each region but with better separation. Moreover, our method can perform bias field correction. To this end, the bias field is represented by a linear combination of smooth base functions that enables better intensity inhomogeneity modeling. Therefore, level set fundamental formulation and bias field are modified in the proposed approach. To assess the performance of the proposed method, different inhomogeneous images, including synthetic images as well as real brain magnetic resonance images from BraTS 2017 dataset are segmented. Being evaluated by Dice, Jaccard, Sensitivity, and Specificity metrics, the results show that the proposed method suppresses the side effect of the over-smoothing object boundary and it has good accuracy in the segmentation of images with extreme intensity non-uniformity. The mean values of these metrics in brain tumor segmentation are 0.86 ± 0.03, 0.77 ± 0.05, 0.94 ± 0.04, 0.99 ± 0.003, respectively. Our method were compared with six state-of-the-art image segmentation methods: Chan-Vese (CV), Local Intensity Clustering (LIC), Local iNtensity Clustering (LINC), Global inhomogeneous intensity clustering (GINC), Multiplicative Intrinsic Component Optimization (MICO), and Local Statistical Active Contour Model (LSACM) models. We used qualitative and quantitative comparison methods for segmenting synthetic and real images. Experiments indicate that our proposed method is robust to noise and intensity non-uniformity and outperforms other state-of-the-art segmentation methods in terms of bias field correction, noise resistance, and segmentation accuracy. Experimental results show that the proposed model is capable of accurate segmentation and bias field estimation simultaneously. The proposed model suppresses the side effect of the over-smoothing object boundary. Moreover, our model has good accuracy in the segmentation of images with extreme intensity non-uniformity.
Fractal measures of image local features: an application to texture recognition

Here we propose a new method for the classification of texture images combining fractal measures (fractal dimension, multifractal spectrum and lacunarity) with local binary patterns. More specifically we compute the box counting dimension of the local binary codes thresholded at different levels to compose the feature vector. The proposal is assessed in the classification of three benchmark databases: KTHTIPS-2b, UMD and UIUC as well as in a real-world problem, namely the identification of Brazilian plant species (database 1200Tex) using scanned images of their leaves. The proposed method demonstrated to be competitive with other state-of-the-art solutions reported in the literature. Such results confirmed the potential of combining a powerful local coding description with the multiscale information captured by the fractal dimension for texture classification.
3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

Edge computing can provide many key functions without connecting to centralized servers, which enables remote areas to obtain real-time medical diagnoses.The combination of edge computing and Internet of things (IoT) devices can send remote patient data to the hospital, which will help to more effectively address long-term or chronic diseases.CT images are widely used in the diagnosis of clinical diseases, and their characteristics are an important basis for pathological diagnosis.In the CT imaging process, speckle noise is caused by the interference of ultrasound on human tissues, and its component information is complex.To solve these problems, we propose a 3D reconstruction method for noisy CT images in the IoT using edge computing.First, we propose a multi-stage feature extraction generative adversarial network (MF-GAN) denoising algorithm.The generator of MF-GAN adopts the multi-stage feature extraction, which can ensure the reconstruction of the image texture and edges.Second, we apply the denoised images generated from the MF-GAN method to perform the 3D reconstruction.A marching cube (MC) algorithm based on regional growth and trilinear interpolation (RGT-MC) is proposed.With the idea of regional growth, all voxels containing iso-surfaces are selected and calculated, which accelerates the reconstruction efficiency.The intersection point of the voxel and iso-surface is calculated by the trilinear interpolation algorithm, which effectively improves the reconstruction accuracy.The experimental results show that MF-GAN has a better denoising effect than other algorithms.Compared to other representative 3D algorithms, the RGT-MC algorithm greatly improves the efficiency and precision.
Book Recommendation for eLearning Using Collaborative Filtering and Sequential Pattern Mining

Reading is a fundamental skill that every person needs to expand throughout his/her lifetime. Book recommendation for eLearning systems can gain more attention in digital libraries, commercial websites and social media sites. Nowadays, obtaining the preferred book in real-time becomes a challenging task. Because there are too many books available online and offline, this colossal number creates a dilemma for the end-user, especially for ebook learners. The book recommendation helps overcome the information overhead problem and through this, the user can quickly get books according to need within the shortest span of time. This paper proposes a Book recommendation system for eLearning using collaborative filtering and sequential pattern mining to serve individual requirements. Current writing also focuses on various similarity techniques, namely Cosine, Euclidean, Correlation, Manhattan and Jaccard. The accuracy of the Book Recommendation for eLearning is evaluated by applying precision, recall and F1 Score. In the end, results show that correlation-based Sequential pattern mining gives a better F1 Score.