Reviewerly/exHarmony huggingface dataset:
Exploring incivility and moral foundations toward Asians in English-speaking tweets in hate crime-reporting cities during the COVID-19 pandemic

Purpose This study aims to explore the extent to which Twitter users engaged in uncivil and morally questionable expressions in their comments about specific Asian countries and citizens. The integrated threat theory (ITT) was used to formulate questions surrounding incivility and moral foundations within Twitter discourses related to the COVID-19 pandemic. Design/methodology/approach The authors collected tweets and retweets posted by English-speaking Twitter users in the United States (US) across the following three phases: (1) initial discovery of COVID-19 in China, (2) high US mortality rate from COVID-19 and (3) the announcement that a vaccine would soon be available in the US. Findings The authors found a significant difference in uncivil tweets posted in cities with higher levels of reported hate crimes against Asians than cities with low levels. Lastly, English-speaking Twitter users tended to employ moral virtue words and moral vice words when discussing China and Chinese culture/populations. Research limitations/implications The bags-of-words employed are limited in capturing nuanced and metaphorical terms. In addition, the analysis focused solely on Tweets composed in English and thus did not capture the thoughts and opinions of non-English speakers. Lastly, this study did not address all Asian countries. In this sense, the findings of this study might not be applicable to Tweets about other nations. Practical implications Given that many Twitter users tend to use terms of moral virtue in support of Asians and Asian communities, the authors suggest that non-governmental organization administrators provide morally supportive social media campaigns that encourage users to engage in civil discourse. Social implications These findings have theoretical implications as the frameworks of integrated threats and moral foundations were used to offer group-level explanations for online behavior. Additional research is needed to explore whether these frameworks can be used to explain negativity in other communication environments. Originality/value This study expands the findings of prior studies that identified the extent to which Twitter users express hate speech, focusing on general Twitter discourse across three specific periods of the pandemic: degrees of incivility and moral foundations, and comparison of incivility based on the prevalence of reported hate crimes.
Block sampling Kaczmarz–Motzkin methods for consistent linear systems

The sampling Kaczmarz–Motzkin (SKM) method is a generalization of the randomized Kaczmarz method and the Motzkin method. It first samples some rows of coefficient matrix randomly to build a set and then makes use of the maximum violation criterion within this set to determine a constraint. Finally, it makes progress by enforcing this single constraint. In this paper, based on the SKM method and the block strategies, we present two block sampling Kaczmarz–Motzkin methods for consistent linear systems. Specifically, we also first sample a subset of rows of coefficient matrix and then determine an index in this set using the maximum violation criterion. Unlike the SKM method, in the block methods, we devise different greedy strategies to build index sets. Then, the new methods make progress by enforcing the corresponding multiple constraints simultaneously. Numerical experiments show that, for the same accuracy, our methods outperform the SKM method and the famous deterministic method, i.e., the CGLS method, in terms of the number of iterations and computing time.
Evaluating Fairness in Argument Retrieval

Existing commercial search engines often struggle to represent different perspectives of a search query. Argument retrieval systems address this limitation of search engines and provide both positive (PRO) and negative (CON) perspectives about a user's information need on a controversial topic (e.g., climate change). The effectiveness of such argument retrieval systems is typically evaluated based on topical relevance and argument quality, without taking into account the often differing number of documents shown for the argument stances (PRO or CON). Therefore, systems may retrieve relevant passages, but with a biased exposure of arguments. In this work, we analyze a range of non-stochastic fairness-aware ranking and diversity metrics to evaluate the extent to which argument stances are fairly exposed in argument retrieval systems.
The Neglected Involvement of Organic Matter in Forming Large and Rich Hydrothermal Orogenic Gold Deposits

Orogenic gold deposits have provided most of gold to humanity. These deposits were formed by fluids carrying dissolved gold at temperatures of 200–500 °C and at crustal depths of 4–12 km. The model involves gold mobilization as HS− complexes in aqueous solution buffered by CO2, with gold precipitation following changes in pH, redox activity (fO2), or H2S activity. In this contribution, the involvement of carbonaceous organic matter is addressed by considering the formation of large and/or rich orogenic gold deposits in three stages: the source of gold, its solubilization, and its precipitation. First, gold accumulates in nodular pyrite within carbonaceous-rich sedimentary rocks formed by bacterial reduction of sulfates in seawater in black shales. Second, gold can be transported as hydrocarbon-metal complexes and colloidal gold nanoparticles for which the hydrocarbons can be generated from the thermal maturation of gold-bearing black shales or from abiotic origin. The capacity of hydrocarbons for solubilizing gold is greater than those of aqueous fluids. Third, gold can be precipitated efficiently with graphite derived from fluids containing hydrocarbons or by reducing organic-rich rocks. Black shales are thus a key component in the formation of large and rich orogenic gold deposits from the standpoints of source, transport, and precipitation. Unusual CO2-rich, H2O-poor fluids are documented for some of the largest and richest orogenic gold deposits, regardless of their age. These fluids are interpreted to result from chemical reactions involving hydrocarbon degradation, hence supporting the fundamental role of organic matter in forming exceptional orogenic gold deposits.
Open Source and Commercial Capture The Flag Cyber Security Learning Platforms - A Case Study

The use of gamified learning platforms as a method of introducing cyber security education, training and awareness has risen greatly. With this rise, the availability of platforms to create, host or otherwise provide the challenges that make up the foundation of this education has also increased. In order to identify the best of these platforms, we need a method to compare their feature sets. In this paper, we compare related work on identifying the best platforms for a gamified cyber security learning platform as well as contemporary literature that describes the most needed feature sets for an ideal platform. We then use this to develop a metric for comparing these platforms, before then applying this metric to popular current platforms.
CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation Based on CelebA

In efforts to build a hairstyle recommendation application, we are confronted with the lack of relevant hairstyle-related datasets. In this paper, we present a new large-scale dataset for hairstyle recommendation, CelebHair, based on the celebrity facial attributes dataset, CelebA. Our dataset inherited the majority of facial images along with some beauty-related facial attributes from CelebA. Additionally, we employed facial landmark detection techniques to extract extra features such as nose length and pupillary distance, and deep convolutional neural networks for face shape and hairstyle classification. Empirical comparison has demonstrated the superiority of our dataset to other existing hairstyle-related datasets regarding variety, veracity, and volume. Analysis and experiments have been conducted on the dataset in order to evaluate its robustness and usability.
Solar Resource Potentials and Annual Capacity Factor Based on the Korean Solar Irradiance Datasets Derived by the Satellite Imagery from 1996 to 2019

The Korea Institute of Energy Research builds Korean solar irradiance datasets, using gridded solar insolation estimates derived using the University of Arizona solar irradiance based on Satellite–Korea Institute of Energy Research (UASIBS–KIER) model, with the incorporation of geostationary satellites over the Korean Peninsula, from 1996 to 2019. During the investigation period, the monthly mean of daily total irradiance was in a good agreement with the in situ measurements at 18 ground stations; the mean absolute error is also normalized to 9.4%. It is observed that the irradiance estimates in the datasets have been gradually increasing at a rate of 0.019 kWh m−2 d−1 per year. The monthly variation in solar irradiance indicates that the meteorological conditions in the spring season dominate the annual solar insolation. In addition, the local distribution of solar irradiance is primarily affected by the geographical environment; higher solar insolation is observed in the southern part of Korea, but lower solar insolation is observed in the mountainous range in Korea. The annual capacity factor is the secondary output from the Korean solar irradiance datasets. The reliability of the estimate of this factor is proven by the high correlation coefficient of 0.912. Thus, in accordance with the results from the spatial distribution of solar irradiance, the southern part of Korea is an appropriate region for establishing solar power plants exhibiting a higher annual capacity factor than the other regions.
Pap smear based cervical cancer detection using residual neural networks deep learning architecture

Abstract Detecting and classifying the Pap smear cell images is important task for cervical cancer identification. In this article, dual tree complex wavelet transform (DTCWT) based modified deep learning algorithm is proposed for classifying the Pap smear cell images into four different cases normal, carcinoma in situ, dysplastic, and superficial. This proposed work consists of data augmentation module, DTCWT module and convolutional neural networks (CNN) module for the automatic classification of Pap smear cell images. The CNN module required huge number of cell images for obtaining high classification rate. Hence, the shearing and flipping functions are used in the data augmentation process to improve the dataset samples for further CNN classification module. Then, the spatial pixel behavior of data augmented images is transferred into multimodal pixels using DTCWT, which creates the sub band coefficients in matrix format. This matrix is trained and classified using ResNet 18 which classifies the source Pap smear cell image into four classes. This developed method is tested on the Pap smear cell images available in open access dataset. The average Pap smear detection index (PDI) of the automatic Pap smear cell image classification system is about 99%.
Blockchain Security, a Multilayer Paradigm

In this paper, we discuss the issue of blockchain security considering the matter in a perspective related to users’ education and habits, and to security as an attribute of a system as a whole. Throughout the document, we highlight how security can be put at stake by a nonchalant use and we suggest paying closer attention in approach to digital.
Maximum-Entropy Multi-Agent Dynamic Games: Forward and Inverse Solutions

In this article, we study the problem of multiple stochastic agents interacting in a dynamic game scenario with continuous state and action spaces.We define a new notion of stochastic Nash equilibrium for boundedly rational agents, which we call the entropic cost equilibrium (ECE).We show that ECE is a natural extension to multiple agents of maximum entropy optimality for a single agent.We solve both the "forward" and "inverse" problems for the multi-agent ECE game.For the forward problem, we provide a Riccati algorithm to compute closedform ECE feedback policies for the agents, which are exact in the linear-quadratic-gaussian case.We give an iterative variant to find locally ECE feedback policies for the nonlinear case.For the inverse problem, we present an algorithm to infer the cost functions of the multiple interacting agents given noisy, boundedly rational input and state trajectory examples from agents acting in an ECE.The effectiveness of our algorithms is demonstrated in a simulated multi-agent collision avoidance scenario, and with data from the INTERACTION traffic dataset.In both cases, we show that, by taking into account the agents' game theoretic interactions using our algorithm, a more accurate model of agents' costs can be learned, compared with standard inverse optimal control methods.
Cross-View Exocentric to Egocentric Video Synthesis

Cross-view video synthesis task seeks to generate video sequences of one view from another dramatically different view. In this paper, we investigate the exocentric (third-person) view to egocentric (first-person) view video generation task. This is challenging because egocentric view sometimes is remarkably different from the exocentric view. Thus, transforming the appearances across the two different views is a non-trivial task. Particularly, we propose a novel Bi-directional Spatial Temporal Attention Fusion Generative Adversarial Network (STA-GAN) to learn both spatial and temporal information to generate egocentric video sequences from the exocentric view. The proposed STA-GAN consists of three parts: temporal branch, spatial branch, and attention fusion. First, the temporal and spatial branches generate a sequence of fake frames and their corresponding features. The fake frames are generated in both downstream and upstream directions for both temporal and spatial branches. Next, the generated four different fake frames and their corresponding features (spatial and temporal branches in two directions) are fed into a novel multi-generation attention fusion module to produce the final video sequence. Meanwhile, we also propose a novel temporal and spatial dual-discriminator for more robust network optimization. Extensive experiments on the Side2Ego and Top2Ego datasets show that the proposed STA-GAN significantly outperforms the existing methods.
Real-time Local Path Planning for Intelligent Vehicle combining Tentacle Algorithm and B-spline Curve

The obstacle-avoidance problem of intelligent vehicles is one of the challenges that we face in path planning. In order to tackle it, this paper proposes a real-time path planning approach based on tentacle algorithm and B-spline curve. In this approach, firstly, some virtual tentacles are built to represent the precalculated paths of the ego vehicle at a current speed. Secondly, it selects the best tentacle path among them, which is required to provide a safe driving direction and sampling area for generating B-spline paths. When the vehicle drives along the best tentacle path, the B-spline path is generated according to the sampling area. Finally, the designed path is formed by segments of the best tentacle and a B-spline curve. Compared with other sampling-based path sets approaches, using the proposed approach needs shorter reaction time. Simulation and experiment results verify the real-time performance and effectiveness of the algorithm of local path planning.
Robust and effective image preprocessing conglomerate method for denoising of both grayscale and color images

Image quality is greatly affected by noise in various applications such as machine vision and computer vision. Noisy images lead to inaccurate results as well as segmentation and enhancement errors, making denoising vital to efficient image processing. Existing denoising methods do not work effectively on both grayscale and color images. Thus an effective denoising method that works well on both image types is proposed. The proposed two-stage method involves: (i) noisy pixel detection and (ii) noisy pixel restoration. In the first stage, the conglomerate method detects the noisy pixel position; in the second stage, the corrupted value of the noisy pixel is restored using a conglomerate method. The proposed method was used to process a variety of grayscale and color images. The result analysis of image denoising was done in two stages: in the first stage, results are carried out using large standard datasets; comparison of these results with those of five other denoising methods showed that the proposed conglomerate denoising method outperformed these existing ones. The second stage of result analysis considers the state-of-the-art methods in comparison with the proposed method. The proposed method outperforms the state-of-the-art methods for images affected with noise ranging from 10% to 90%.
GNSS观测的2021年青海玛多地震(Mw 7.4)同震形变及其滑动分布

北京时间2020年5月22日2时，中国青海省果洛州玛多县发生Mw 7.4地震。收集震中附近9个连续运行卫星定位基准站(continuously operating reference stations，CORS)观测数据，基于模糊度解算的精密单点定位(precise point positioning with ambiguity resolution，PPP-AR)技术处理了2 h时段的高频(1 Hz采样)全球导航卫星系统(global navigation satellite system，GNSS)数据，快速确定本次地震的近场同震位移，其中水平方向最大约为0.6 m。结合远场12个CORS震前、震后各3 d低频(30 s采样)数据的非差PPP解算同震形变场，基于弹性位错模型反演了玛多地震断层几何参数和滑动分布。结果显示：玛多地震属于典型走滑事件，发震断层走向278.49°，倾角为64.38°，滑动角为-10.90°，破裂长度约为138.72 km，宽度为4.82 km；滑动量超过3 m的滑移主要集中在东部小于18 km深度的区域，最大破裂可达4.2 m。反演的地震矩为1.85×1020 Nm，相当于矩震级7.45，比美国地质调查局利用地震波的反演结果略大。
A Systematic Review on the Chronological Development of Bangla Sign Language Recognition Systems

Despite sign language being the primary medium of conversation with deaf and dumb (DD) individuals, it is hard to grasp for any class of people, making communication immensely challenging. Bengali being one of the most spoken languages globally, significant research works on Bangla Sign Language (BdSL) have come to light to deal with this challenge. In recent years, researchers from diverse backgrounds have proposed solutions to automate the process of BdSL recognition. In this literature review, we have explored the trends of research on BdSL by comparing incorporated features and evaluation results of systems between 2002-2021 and techniques applied on different existing and new datasets. We have also collected and combined metadata from datasets of all BdSL Alphabets and numerics used so far. The literature findings of this paper indicate that most proposed models perform well on images containing static and single-handed signs but, performance falls significantly in complex backgrounds. Moreover, we focused on identifying insights and similarities of the existing system, finding research gaps and propose possible future directions. We expect this article will draw the attention of more researchers into this domain and this is the first identifiable academic literature review of BdSL recognition systems to the best of our knowledge.
The political implication of women and industrialisation in Africa

This study examines the effect of political implications of women on industrialisation in Africa. The results after controlling for cross-sectional dependency show that women political implication Granger causes industrialisation in Africa. Besides, the Fixed effect Driscoll/Kraay standard error estimator reveal that women political empowerment negatively affect industrialisation in Africa. These negative effects are nullified by high economic freedom and high female economic participation in the economy. The investment freedom thresholds require for the negative net effects to be nullified are 52.30469, 55.51639, 49.324895, and 55.594059 respectively for the women political empowerment index, women civil liberty, women political participation and women civil society participation interactions; while when women economic participation rates of 43.0777, 35.82, and 46.9 are attained for women political empowerment index, women civil liberty and women civil society participation respectively, complementary policies are needed for a positive effect on industrialisation. The study implores policy makers to improve on the economic freedom of the countries and to elaborate on policies that favour women economic inclusion, if policy towards political inclusion is foreseen in the industrialisation agenda.
Beyond Learning: Back to Geometric Essence of Visual Odometry via Fusion-Based Paradigm

Camera-based motion estimation, also called visual odometry (VO), is a technique applied in the area of autonomous driving and simultaneous localization and mapping (SLAM), which is gaining huge popularity due to its simplicity and use of limited resources in localization. In this article, we propose to consider the depth and optical flow estimation as a joint learning task and then obtain ego motion based on geometric constraints. To be specific, we construct a network to learn dense optical flow and depth map simultaneously, where the scale of the prediction is aligned by an additional velocity supervision signal. Then, we formulate the uncertainty of the two main geometric methods and leverage covariance intersection (CI) fusion, allowing the ego motion to be efficiently predicted in an automodeling and compensation way, without manual intervention, postprocessing, or pose network training utilized before. Compared with recent approaches to similar tasks, our method requires neither complicated feature descriptor calculations nor expensive supplemental sensors. Moreover, our model flexibly adapts to both unsupervised and weakly supervised learning, which improves the feasibility of the framework in diverse settings. We conduct extensive experiments on several datasets representing driving and walking scenarios, and the results demonstrate a promising performance of the proposed method to state-of-the-art monocular ones.
Three-Dimensional Transfer Functions of Interference Microscopes

Three-dimensional transfer functions (3D TFs) are generally assumed to fully describe the transfer behavior of optical topography measuring instruments such as coherence scanning interferometers in the spatial frequency domain. Therefore, 3D TFs are supposed to be independent of the surface under investigation resulting in a clear separation of surface properties and transfer characteristics. In this paper, we show that the 3D TF of an interference microscope differs depending on whether the object is specularly reflecting or consists of point scatterers. In addition to the 3D TF of a point scatterer, we will derive an analytical expression for the 3D TF corresponding to specular surfaces and demonstrate this as being most relevant in practical applications of coherence scanning interferometry (CSI). We additionally study the effects of temporal coherence and disclose that in conventional CSI temporal coherence effects dominate. However, narrowband light sources are advantageous if high spatial frequency components of weak phase objects are to be resolved, whereas, for low-frequency phase objects of higher amplitude, the temporal coherence is less affecting. Finally, we present an approach that explains the different transfer characteristics of coherence peak and phase detection in CSI signal analysis.