Reviewerly/exHarmony huggingface dataset:
Modeling and Simulation of Radar Sensor Artifacts for Virtual Testing of Autonomous Driving

Automotive radar measurements are prone to noisy sensor readings due to artifacts, such as false detections, clutter, and aliasing. Simulation models of sensors that are deployed in virtual testing pipelines must demonstrate a high degree of ﬁdelity. Capturing artifacts correctly can be seen as a criteria for the trustworthiness of the model. In this work, we outline a comprehensive overview of different types of artifacts in radar along with their characteristics. Next, we allocate their formation process in the radar signal processing chain from which we draw implications about their correct modeling. We also present mathematical models that describe the artifacts along with their implementation in a radar simulation model.
Droplet-Transmitted Infection Risk Ranking Based on Close Proximity Interaction

We propose an automatic method to identify people who are potentially-infected by droplet-transmitted diseases. This high-risk group of infection was previously identified by conducting large-scale visits/interviews, or manually screening among tons of recorded surveillance videos. Both are time-intensive and most likely to delay the control of communicable diseases like influenza. In this paper, we address this challenge by solving a multi-tasking problem from the captured surveillance videos. This multi-tasking framework aims to model the principle of Close Proximity Interaction and thus infer the infection risk of individuals. The complete workflow includes three essential sub-tasks: 1) person re-identification (REID), to identify the diagnosed patient and infected individuals across different cameras, 2) depth estimation, to provide a spatial knowledge of the captured environment, 3) pose estimation, to evaluate the distance between the diagnosed and potentially-infected subjects. Our method significantly reduces the time and labor costs. We demonstrate the advantages of high accuracy and efficiency of our method. Our method is expected to be effective in accelerating the process of identifying the potentially infected group and ultimately contribute to the well-being of public health.
On the simultaneous openness hypothesis: FDI, trade and TFP dynamics in Sub-Saharan Africa

Abstract This study assesses the simultaneous openness hypothesis that trade modulates foreign direct investment (FDI) to induce positive net effects on total factor productivity (TFP) dynamics. Twenty-five countries in Sub-Saharan Africa and data for the period 1980 to 2014 are used. The empirical evidence is based on the Generalized Method of Moments. First, trade imports modulate FDI to overwhelmingly induce positive net effects on TFP, real TFP growth, welfare TFP and real welfare TFP. Second, with exceptions on TFP and welfare TFP where net effects are both positive and negative, trade exports modulate FDI to overwhelmingly induce positive net effects on real TFP growth and welfare real TFP. In summary, the tested hypothesis is valid for the most part. Policy implications are discussed.
Seismicity of the Earth 1900–2018

First posted February 7, 2020 For additional information, contact: Center Director, Geologic Hazards Science CenterU.S. Geological SurveyBox 25046, Mail Stop 966Denver, CO 80225 This map illustrates 119 years of global seismicity in the context of global plate tectonics and the Earth’s physiography. Primarily designed for use by earth scientists, engineers, and educators, this map provides a comprehensive overview of strong (magnitude [M] 5.5 and larger) earthquakes since 1900. The map clearly identifies the locations of the “great” earthquakes (M 8.0 and larger) and the aftershock or rupture area (green fill), if known, of the M 8.3 or larger earthquakes. The circular earthquake symbols are scaled to be proportional to the moment magnitude and therefore to the area of faulting, thus providing a better understanding of the relative sizes and distribution of earthquakes in the magnitude range 5.5 to 9.5. Plotting the known rupture or aftershock areas (which are closely related) of the largest earthquakes also provides a better appreciation of the faulting extent of some of the most famous and damaging instrumentally recorded earthquakes in modern history.
An introduction to state-space modeling of ecological time series

State-space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics and animal movement, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological stochasticity (e.g., in birth processes) and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the introduction of SSMs, a broad range of fitting procedures have been proposed. However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. In addition, many SSM users are unaware of the potential estimation problems they could encounter, and of the model selection and validation tools that can help them assess how well their models fit their data. In this paper, we present a review of SSMs that will provide a strong foundation to ecologists interested in learning about SSMs, introduce new tools to veteran SSM users, and highlight promising research directions for statisticians interested in ecological applications. The review is accompanied by an in-depth tutorial that demonstrates how SSMs models can be fitted and validated in R. Together, the review and tutorial present an introduction to SSMs that will help ecologists to formulate, fit, and validate their models.
Early Regulations of Distributed Ledger Technology/Blockchain Providers: A Comparative Case Study

Distributed Ledger Technologies (DLTs) such as Blockchain have been heralded for their potential to fundamentally disrupt traditional industries and longstanding practices in private and public businesses.In the financial sectors, for example, quite a number of novel financial technology (FinTech) services based on DLT/Blockchain have been introduced with cryptocurrencies representing prominent cases.While the already highly regulated financial sectors have emerged as early targets for DLT/Blockchain induced disruption, a diverse set of other areas, such as healthcare record keeping, insurance record keeping, industrial and retail supply chain management, property registries, citizen identification systems, and voting systems to name a few, has also come into the focus of DLT/Blockchain innovation.These new types of services might be in need of both complementary and novel regulations for DLT/Blockchainbased services.Interestingly, smaller jurisdictions such as Gibraltar, Malta, and Liechtenstein were among the first to provide advice and regulation for DLT/Blockchain service provisions.The study compares these early regulatory approaches to each other and discusses the prospects of DLT/Blockchain service regulation based on the study's findings.DLT/Blockchain service regulation appears to incorporate predominantly principle-based rather than rule-based regulations, which makes the regulation enforcement a uniquely individual case-based task.
A comparison between the performance of DWT and LWT in image watermarking

Image watermarking algorithms can be implemented using time domain or frequency domain-based algorithms. Frequency domain watermarking produces watermarks with higher robustness; hence, many attempts have been proposed in literature using different transformations as DCT, SVD, and DWT. DWT was widely used for many reasons as its spatio-temporal feature, in which the alteration of certain portion will affect only the affected portion. In this paper an experimental comparison between the traditional DWT and the second generation of wavelet which is LWT is initiated. The experimental tests evaluated the performance of both transforms in terms of image quality and watermark robustness.
Vehicle Make and Model Recognition using Bag of Expressions

Vehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.
No-Reference Image Quality Assessment Method Based on Visual Parameters

Recent studies on no-reference image quality assessment (NR-IQA) methods usually learn to evaluate the image quality by regressing from human subjective scores of the training samples. This study presented an NR-IQA method based on the basic image visual parameters without using human scored image databases in learning. We demonstrated that these features comprised the most basic characteristics for constructing an image and influencing the visual quality of an image. In this paper, the definitions, computational method, and relationships among these visual metrics were described. We subsequently proposed a no-reference assessment function, which was referred to as a visual parameter measurement index (VPMI), based on the integration of these visual metrics to assess image quality. It is established that the maximum of VPMI corresponds to the best quality of the color image. We verified this method using the popular assessment database—image quality assessment database (LIVE), and the results indicated that the proposed method matched better with the subjective assessment of human vision. Compared with other image quality assessment models, it is highly competitive. VPMI has low computational complexity, which makes it promising to implement in real-time image assessment systems.
Clustering Provinces in Indonesia based on Community Welfare Indicators

The Preamble of the 1945 Constitution of the Republic of Indonesia explicitly states that the main task of the government of the Republic of Indonesia is to advance general prosperity, to develop the nation's intellectual life, and to realize social justice for all Indonesian people. Social inequality is a problem that is still faced by Indonesian people today. To solve the problem required supporting data analysis as a basis for policy formulation. This research was conducted with the aim of clustering provinces in Indonesia based on community welfare indicators using K-Means cluster analysis. K-Means cluster analysis is chosen based on the variance value (0.101), which is smaller than the variance value in the average linkage cluster analysis (0.152). Based on data analysis, provinces in Indonesia are clustered into three where the first cluster consists of 21 provinces, the second cluster consists of 3 provinces, and the third cluster consists of 10 provinces. Each cluster has different characteristics that can be of concern to the parties concerned to overcome the social welfare gap. Besides, in order cluster results are more easily understood, visualization of results is added with a Geographic Information System (GIS) using Indonesian maps accompanied by differences in color gradations for each cluster
Auto-Suggestive Real-Time Classification of Driller Memos into Activity Codes Using Natural Language Processing

Abstract Activity codes recorded by drillers are very useful for quantifying invisible lost time (ILT). However, classifying more than 100 activity codes accurately and consistently across various rig operations becomes infeasible for human operators. We propose an auto-suggestive system that guides the drillers to the correct codes based on memos they enter into the system. This aims to both eliminate manual classification errors and improve memo entry. The method for extracting activity codes from memos can be broken into the following steps. The first step consists of filtering unnecessary text and vectorizing the memos. The vectors are then re-weighted using the term frequency-inverse document frequency (TFIDF) statistical measure. Next, data resampling helps to create a uniform set of labels for the training data, because there are quite a few important activity codes that appear infrequently with respect to others. Finally, a classifier is trained. It is shown that the finalized model can be used as a real-time auto-suggestive mechanism during the drillers’ data input process. Moreover, its use for cleaning up historical datasets is also explored. This method was implemented on a large historical dataset consisting of 150 wells, and ILT analysis was performed with the original dataset and with the auto-classified dataset. Comparing these results clearly showed that performing analysis on a dataset that has not been properly classified can lead to incorrect and misleading conclusions. Also, this method did not require a manual re-labeling of the dataset for model training. This makes the algorithm readily applicable for any end-user, irrespective of the number of activity codes used. Various classifiers including logistic regression, support vector machine, random forests, naïve Bayes, and multi-layered perceptron were implemented and tested. Given comparable performances, we conclude that a simple and interpretable logistic regression model is best for real-time classification. Tests were also performed to see how many typed words in a memo would be needed before the correct activity code was identified. The results are detailed in this paper. This is the first body of work that has taken drillers’ memos and converted them into activity codes, without the need for a human-classified training dataset. The real-time classifier is very powerful in ensuring clean data at the source and will be particularly useful when implemented on reporting systems for classifying rig activities by IADC activity codes. We further demonstrate the use of the classifier for cleansing historical datasets such that ILT analysis can be done more accurately.
Interpol review of forensic science management literature 2016–2019

This paper reviews and summarizes the forensic management literature from late 2016 to late 2019, covering laboratory decision making, business strategy, and industry identity and transparency. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol%20Review%20Papers%202019.pdf.
Adversarial Test on Learnable Image Encryption

Data for deep learning should be protected for privacy preserving. Researchers have come up with the notion of learnable image encryption to satisfy the requirement. However, existing privacy preserving approaches have never considered the threat of adversarial attacks. In this paper, we ran an adversarial test on learnable image encryption in five different scenarios. The results show different behaviors of the network in the variable key scenarios and suggest learnable image encryption provides certain level of adversarial robustness.
Big data experiments with the archived Web: Methodological reflections on studying the development of a nation's Web

This article outlines how the 'digital geography' of a nation can be studied, that is the online presence of one nation. The entire Danish Web domain and its development from 2006 to 2015 is used as a case, based on the holdings in the Danish national Web archive. The following research questions guide the investigation: What has the Danish Web domain looked like in the past, and how has it developed in the period 2006-2015? Methodologically, we investigate to what extent one can delimit 'a nation' on the Web, and what characterizes the archived Web as a historical source for academic studies, as well as the general characteristics of our specific data source. Analytically, the article introduces a design for how this type of big data analyses of an entire national Web domain can be performed. Our findings show some of the ways in which a nation's digital landscape can be mapped, ie. on size, content types and hyperlinks. On a broader canvas, this study demonstrates that with hard- and software as well as human competencies from different disciplines it is possible to perform large-scale historical studies of one of the biggest media sources of today, the World Wide Web.
Augmented reality mobile application and its influence in Quechua language learning

Augmented reality technology is being a huge support in education standing out the user's attention to interact with virtual world in real space. In this article, the level of influence that would have the use of a mobile application with augmented reality will be evaluated in primary students for Quechua language learning. Likewise, the quasi-experimental design is used and the sample consists of sixty students divided into two classrooms of thirty students respectively. The experimental group improves his grades in a 19.7% unlike the control group and also a reduction of time for understanding the information is seen. In conclusion, the augmented reality mobile application influences significantly in Quechua language learning for fourth-year pupils of primary school.
Soft-Aligned Gradient-Chaining Network for Height Estimation From Single Aerial Images

Extracting elevation information from single aerial images is an important and challenging task in photogrammetry and remote sensing. The difficulty lies in the ambiguity and ill-condition of the 2-D-3-D projection. As neural networks show advantages in solving ill-posed problems, fully convolutional neural networks (FCNs) have been applied to this task and have achieved worthwhile results. However, the previous works simply focus on the application of the neural network structures originally applied to monocular depth estimations or semantic segmentations to single-image elevation estimations. This procedure ignores the task-specific weak correlation problem between the elevation systems of training labels and outputs and the one between heights of adjacent pixels. To address these problems, we use the FCN to generate height differences between adjacent pixels in specific orientations (i.e., oriented elevation gradients), instead of directly generating the elevation values. Subsequently, the elevation gradients in the two orients of XY are combined to form a huge relationship chain, and the relative elevation of each pixel is calculated using the maximum likelihood method. Finally, a new surface-based soft alignment method is used to establish the correlation between the elevation systems of training labels and predicted outputs robustly. The quantitative evaluation on the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam data set reveals a significant improvement of the bare FCN. The visual contrast evaluation indicates that the soft-aligned gradient-chaining network can retain more surface features, and its outputs are more reasonable and visually pleasing than the bare FCN.
An efficient Long Short-Term Memory model based on Laplacian Eigenmap in artificial neural networks

A new algorithm for data prediction based on the Laplacian Eigenmap (LE) is presented. We construct the Long Short-Term Memory model with the application of the LE in artificial neural networks. The new Long Short-Term Memory model based on Laplacian Eigenmap (LE-LSTM) reserves the characteristics of original data using the eigenvectors derived from the Laplacian matrix of the data matrix. LE-LSTM introduces the projection layer embedding data into a lower dimension space so that it improves the efficiency. With the implementation of LE, LE-LSTM provides higher accuracy and less running time on various simulated data sets with characteristics of multivariate, sequential, and time-series. In comparison with previously reported algorithms such as stochastic gradient descent and artificial neural network with three layers, LE-LSTM leads to many more successful runs and learns much faster. The algorithm provides a computationally efficient approach to most of the artificial neural network data sets.
Blind Color Image Watermarking Using Fan Beam Transform and QR Decomposition

Digital watermarking has been utilized effectively for copyright protection of multimedia contents. This paper suggests a blind symmetric watermarking algorithm using fan beam transform (FBT) and QR decomposition (QRD) for color images. At first, the original image is transferred from RGB to L*a*b* color model and FBT is applied to b* component. Then the b*component of the original image is split into m × m non-overlapping blocks and QRD is conducted to each block. Watermark data is placed into the selected coefficient of the upper triangular matrix using a new embedding function. Simulation results suggest that the presented algorithm is extremely robust against numerous attacks, and also yields watermarked images with high quality. Furthermore, it represents more excellent performance compared with the recent state-of-the-art algorithms for robustness and imperceptibility. The normalized correlation (NC) of the proposed algorithm varies from 0.8252 to 1, the peak signal-to-noise ratio (PSNR) varies from 54.1854 to 54.1892, and structural similarity (SSIM) varies from 0.9285 to 0.9696, respectively. In contrast, the NC of the recent state-of-the-art algorithms varies from 0.5193 to 1, PSNR varies from 38.5471 to 52.64, and SSIM varies from 0.9311 to 0.9663, respectively.
Habit Representation Based on Activity Recognition

With the increasing elderly population, attention has been drawn to the development of applications for habit assessment using activity data from smart environments that can be implemented in care facilities. In this paper, we introduce a novel habit assessment method based on information of human activities. First, a recognition system tracks the user’s activities of daily living by collecting data from multiple object sensors and ambient sensors that are distributed within the environment. Based on this information, the activities of daily living are expressed using Fourier series representation. The durations and sequence of the activities are represented by the phases and amplitudes of the harmonics. In this manner, each sequence is represented in a form that we refer to as a behavioral spectrum. After that, signals are clustered to find habits. We also calculate the variability, and by comparing the explained variance, the types of habits are found. For an evaluation, two datasets (young and elderly population) were used, and the results showed the potential habits of each group. The outcomes of this study can help improve and expand the applications of smart homes.