Reviewerly/exHarmony huggingface dataset:
StylishGAN: Toward Fashion Illustration Generation

In this article, we propose StylishGAN, a generative adversarial network that generates a fashion illustration sketch given an actual photo of a human model. The generated stylish sketches not only capture the image style from real photos to hand drawings with a cleaner background, but also adjust model’s body into a perfectly proportioned shape. StylishGAN learns proportional transformation and texture information through a proposed body-shaping attentional module. Furthermore, we introduce a contextual fashionable loss that augments the design details, especially the fabric texture, of the clothing. To implement our method, we prepare a new fashion dataset, namely, StylishU, that consists of 3578 paired photo–sketch images. In each pair, we have one real photo collected from the fashion show and one corresponding illustration sketch created by professional fashion illustrators. Extensive experiments show the performance of our method qualitatively and quantitatively.
Stegan detection method for latent images for intellectual property protection systems

Objective. Improving the quality of detection and stego-detection of latent images embedded in the protected object of intellectual property by various methods. Method. The method for stego-detection of latent images based on deep learning is proposed. The method is based on the use of the VGG16 convolutional neural network model, in which the architecture and training parameters are optimized. Result. Increasing the accuracy of detecting stegocontainer images by 3.8%, as well as the possibility of using the algorithm of the developed method for images with a higher resolution than the dimension of the input of an artificial neural network. Conclusion. The developed method is intended for stegan detection in two cases: to detect the fact of illegal use of intellectual property objects; for use in computer forensics when identifying images containing hidden and prohibited information.
Graph learning for latent-variable Gaussian graphical models under laplacian constraints

In recent years, graph learning for smooth signals under Laplacian constraints has attracted increasing attention due to the wide application of graph Laplacian matrix in spectral graph theory, machine learning, and graph signal processing tasks. Standard graph learning methods usually assume that graphs are sparse, but the correlation between real-world entities is only sometimes sparse because of some common and potential effects. In this paper, we model these common effects as latent variables and assume that the Gaussian graphical model (GGM) under Laplacian constraints is conditionally sparse given latent variables but marginally non-sparse. Based on this assumption, the graph learning problem is formulated in a regularized maximum marginal likelihood (MML) framework with a sparse plus low-rank decomposition form. The specialized algorithm is developed to solve the proposed graph learning problem by incorporating Laplacian constraints into a multi-block alternating direction method of multipliers (ADMM) with proximal regularization terms. The experiments conducted on synthetic and real-world data sets demonstrate that the proposed graph learning method outperforms the standard method in inferring the sparsity pattern of the conditional graphical model of observed variables with the presence of latent variables.
Confidence for exploratory machine learning

Machine learning problems performing classification into categories whose definition and validity come from a necessary dialogue with the machine learning system represent a special class of problems we call "exploratory". Here we argue that such problems need special treatment and illustrate this to be the case for confidence calibration in gene function prediction.
Dual-stream coupling network with wavelet transform for cross-resolution person re-identification

Person re-identification is a prevalent technology deployed on intelligent surveillance. There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution, yet such models are not applicable to the open world. In real world, the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent. When low-resolution (LR) images in the query set are matched with high-resolution (HR) images in the gallery set, it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images. To address the above issues, we present a dual-stream coupling network with wavelet transform (DSCWT) for the cross-resolution person re-identification task. Firstly, we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images, which is applied to restore the lost detail information of LR images. Then, we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions. Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.
Analysis of Reinforcement Learning for Determining Task Replication in Workflows

Executing workflows on volunteer computing resources where individual tasks may be forced to relinquish their resource for the resource’s primary use leads to unpredictability and often significantly increases execution time. Task replication is one approach that can ameliorate this challenge. This comes at the expense of a potentially significant increase in system load and energy consumption. We propose the use of Reinforcement Learning (RL) such that a system may ‘learn’ the ‘best’ number of replicas to run to increase the number of workflows which complete promptly whilst minimising the additional workload on the system when replicas are not beneficial. We show, through simulation, that we can save 34% of the energy consumption using RL compared to a fixed number of replicas with only a 4% decrease in workflows achieving a pre-defined overhead bound.
Design and Implementation of an Android GIS Data Collection Application for Students with Non-Programming Experience Using the MIT App Inventor: A Case Study on a Tick Data Collection APP

Learning programming can be a daunting task, especially for students without a programming background or non-computer science students.Most Geographic Information Science (GIS) experts and students come from a non-programming background and the development of applications has mostly been perceived to be a field for computer science students and has mostly been shunned by other non-programming students and courses.Compounded by the availability of Open-Source Data collection tools like the Open Data Kit (ODK), learning programming and android application development is often overlooked by Geo Spatial Scientists.Many studies have been done to identify a suitable application development kit to introduce application development to students in non-programming classes.MIT App inventor is a user-friendly drag-and-drop application development platform that is ideal for introducing application development to students with a non-programming background.In this paper, students doing an undergraduate honors degree in spatial sciences were introduced to the MIT App inventor for the development of a Geo-App capable of collecting spatial data that can be exported into a GIS system like Arc GIS, and QGIS after the data has been collected.The paper investigates the suitability of MIT App Inventor in the development of Geo-Spatial Applications.The application was developed for collaborative mapping of tick species for Mashonaland Central Province in Zimbabwe.The main purpose of the application developed is to act as a tool for veterinary extension workers under the Veterinary Services How to cite this paper:
A Tutorial to Use the MediaPipe Framework with ROS2

This chapter presents a tutorial on the use of the MediaPipe package for ROS2, developed by the authors. Through the MediaPipe framework it is possible to collect data from the positions of the hand, face and body of an operator, using a webcam, and publish them in ROS2 topics, which can be used for various applications in robotics. A complete example application using this technology is also covered in this chapter. MediaPipe is used to collect data from the image of an operator’s pose (hands, fingers, face and body positions), and this information is then used to control the navigation of a robot inside a simulated environment. The programming language used is Python and the only hardware needed for execution is a computer with Linux Ubuntu 20.04, ROS2 Galactic and a webcam. At the end of the chapter, the reader will be able to develop control strategies or other functionalities that use information about the movements of the operator, captured directly by the developed ROS2 package, through a webcam. All files used in this chapter can be accessed from the link https://github.com/dmartinelli1997/media_pipe_ros2 , as well as the project videos. All code used in this project can be found in the following repository: https://github.com/dmartinelli1997/Script_ROSBOOK2021.git . (This project was partially funded by National Counsil of Technological and Scientific Development of Brazil (CNPq), and by Coordination for the Improvement of Higher Level People (CAPES).) (This study was financed by the Human Resource Program of The Brazilian National Agency for Petroleum, Natural Gas, and Biofuels—PRH-ANP—FINEP Management, contractual instrument n $$^{\circ }$$ 0.1.19.0240.00, Ref 0431/19 - PRH21-UTFPR.)
The Brain Activity of the Bilingual Code-Switching Communication

This investigation is aimed at conducting a fundamental experimental research of impact of Anglo-Americanisms of a wide semantic range used in virtual network communication. The main subject of the research is the network communication of young native speakers of the Russian language, which is now replete with an increasing number of Anglo-Americanisms replacing Russian spoken language and hypothetically influencing: a) the final effect of communication; b) the emergence of non-chemical dependence (addiction) on the used Anglo-Americanisms among network communicants, native speakers of the Russian language; c) gradual rejection of neurophysiological and cognitive connections within the semantic models of the native language; d) cognitive and neurophysiological re-coding of the processes in the brain reinforced by the constant and long-term use of the same foreign language stimuli-patterns, which leads to a change in the behavioral reactions of Internet users in the process of virtual network communication, as well as real communication.
TERFDA: Tensor Embedding RF Domain Adaptation for varying noise interference

Radio frequency machine learning (RFML) can be loosely termed as a field that machine learning (ML) and deep learning (DL) techniques to applications related to wireless communications. However, traditional RFML basically assume that the data of training set and test set are independent and identically distributed and only a large number of labeled data can train a classification model which can effectively classify test set data. In other words, without enough training samples, it is impossible to learn an automatic modulation classifier that performs well in varying noise interference environment. Feature-based transfer learning minimizes the distribution difference between historical modulated signal data and new data by learning similarity-maximizing feature spaces. Therefore, in this paper, Dynamic Distribution Adaptation (DDA) is adopted to address the above challenges. We propose a Tensor Embedding RF Domain Adaptation (TERFDA) approach, which learns the latent subspace of the tensors formed by the time–frequency maps of the signals, so that use the multi-dimensional domain information of the signals to jointly learn the shared feature subspace of the source domain and the target domain, then perform DDA in the shared subspace. The experimental results show that under the modulated signal data, compared with the state-of-the-art DA algorithm, TERFDA has less requirements on the number of samples and categories, and has superior performance for confrontation the varying noise interference between source domain and target domain.
Stereo image quality assessment using deformed pixels and Sobel magnitude

The application of 3D technology is rapidly expanding, and stereoscopic imagery is typically used to display 3D data. However, compression, transmission, and other necessary processes may reduce the quality of these images. Stereo image quality assessment (SIQA) has gained more attention to guarantee that customers have a positive watching experience. In order to provide the highest level of experience, it is necessary to develop a quality evaluation mechanism for stereoscopic content that is both dependable and precise. A full-reference method for SIQA is presented in this paper. Compared to previous measures, this method gives users more freedom to use distorted pixel metrics and edge similarity. The binocular summation map is calculated by adding the left and right images for a stereo pair. Improved gradient similarity based distorted pixel measure (SGSDM) is used to calculate the quality of binocular summation. The scored 3D LIVE IQA database is used to evaluate the correlation of the proposed metric with the DMOS subjective score given by the database. The proposed method’s efficacy is demonstrated by experimental comparisons.
Phishing Website Detection using Machine Learning

Phishing is a common attack on credulous people by making them to disclose their unique information using counterfeit websites. The objective of phishing website URLs is to purloin the personal information like user name, passwords and online banking transactions. Phishers use the websites which are visually and semantically similar to those real websites. As technology continues to grow, phishing techniques started to progress rapidly and this needs to be prevented by using anti-phishing mechanisms to detect phishing. Machine learning is a powerful tool used to strive against phishing attacks. In this article, we proposed 5 different algorithms in machine learning to analyse the URLs. The accuracy of the Existing method is approximately 94%, and we have implemented it as 95.235% in the Proposed method. Here we used 5 classifiers which are Random Forest Classifier, AdaBoost Classifier, XGBoost Classifier, Support Vector Machine, Gradient Boosting Classifier. Among all these Classifiers, Random Forest Classifier gives the highest accuracy.
Data-driven dependency parsing of Vedic Sanskrit

Abstract This paper describes the first data-driven parser for Vedic Sanskrit, an ancient Indo-Aryan language in which a corpus of important religious and philosophical texts has been composed. We report and critically discuss experiments with the input feature representations, paying special attention to the performance of contextualized word embeddings and to the influence of morpho-syntactic representations on the parsing quality. In addition, we provide an in-depth discussion of the parsing errors that covers structural traits of the predicted trees as well as linguistic and extra-textual influence factors. In its optimal configuration, the proposed model achieves 87.61 unlabeled and 81.84 labeled attachment score on a held-out set of test sentences, demonstrating good performance for an under-resourced language.
Associative Operator Precedence Parsing: A Method To Increase Data Parsing Parallelism

Many data often come with a high volume in textual format (JSON, XML, CSV). Because parsing can easily dominate data analysis time, researchers have been working on parallelizing parsing. Operator Precedence Parsing (OPP), among candidate parsing methods, is amenable to parallelization, with a practical algorithm proposed. The “locally parsable” property allows the parser to deduce if a reduction is safe with limited context. However, when the grammar has productions that tend to produce a highly skewed parse tree, OPP raises reductions mostly in serial, and the parsing still suffers from a long critical path. In pactice, OPP has little or even no speedup when parsing data because data often contain high percentage of parallel elements (e.g., JSON array elements separated by commas) produced from such productions, a situation that frequently occurs when processing big data.
Modelling Illiquid Stocks Using Quantum Stochastic Calculus: Asymptotic Methods

This article investigates the Fokker-Planck equations that arise from the application of quantum stochastic calculus to the modelling of illiquid financial markets, using asymptotic methods. We present a power series solution for quantum stochastic processes with a non-zero conservation process. Whilst the series in question are in general divergent, we show they can be used to approximate solutions for longer time frames, and provide estimates for the relative error on the higher order terms.
Lithological and Alteration Mapping at the Um El-Rus Area, Central Eastern Desert, Egypt, Using Remote Sensing Techniques

Abstract The present study aims at discriminating the rock units and mapping the alteration zones from their host rocks, as promised targets for mineral exploration in Um El-Rus area using the widely considered ASTER data. Several remote sensing techniques were integrated for the lithological discrimination and alteration zone delineation. The approach methodology was able to map the various rock units and define the contacts between them precisely. Moreover, the spatial abundances of the alteration minerals (e.g., alunite, iron oxides, chlorite, kaolinite, illite, montmorillonite, and sericite) were mapped as an indication of the occurrence of alteration zones. More addition, the lineaments in the study area were extracted automatically using the PCI Geomatics software. Areas with considerable lineament density may indicate high potentiality for mineralization as these lineaments may represent faults and fractures that are the main channel ways for hydrothermal solutions to migrate through. This study yielded a modified geological map with enhanced lithological boundaries depending on the remote sensing data. Five localities were delineated and regarded as alteration zones with high potentiality for mineralization. A field visit was conducted to the study area through which the obtained results were validated. Field observations maintained the high accuracy of the obtained enhanced lithological map. During the field study, several alteration indicators were observed, including, quartz veins/veinlets, kaolinitization, chloritization, sericitization, and carbonatization types of alteration. The adopted alteration mapping methodology enabled the delineation of the already existing Um El-Rus gold mine; this validates the results as well.
Interpretable Image Recognition Models for Big Data With Prototypes and Uncertainty

Deep neural networks have been applied to big data scenarios and have achieved good results. However, trust in high-accuracy deep neural networks is not necessarily achieved when facing high-risk decision scenarios. Every wrong decision has incalculable consequences in medicine, autonomous driving, and other fields. For an image recognition model based on big data to gain trust, it is necessary to simultaneously solve the interpretability of decisions and risk predictability. This paper introduces uncertainty into a self-explanatory image recognition model to show that the model can interpret decisions and predict risk. This approch enables the model to trust its decisions and explanations and to provide early warning and detailed analysis of risky decisions. In addition, this paper introduces the process of using uncertainty and explanations to achieve model optimization, which will significantly improve the application value of the model in high-risk scenarios. The scheme proposed in this paper solves the trust crisis caused by using black box image recognition models.
An Effective Volleyball Trajectory Estimation and Analysis Method With Embedded Graph Convolution

Volleyball trajectory prediction and analysis based on deep learning has become a hot topic in sports video research. However, due to a large amount of calculation in video processing and the fast speed of volleyball movement with the target scale changing rapidly, these challenges lead to low performance. To this end, this paper proposes an effectively variant YOLOv4 framework to predict and analyze the volleyball trajectory based on video sequences. In the proposed framework, the authors adopt the pre-trained YOLOv4 to select some proposal regions with a high confidence score. Then, the authors embed graph convolution to effectively aggregate deep features. Moreover, to improve the detection and localization capacity of small targets, they introduce a new loss function by modeling the target area with Gaussian distribution. The experimental results show that the proposed framework can effectively prompt the performance of volleyball detection.
Mind the Gaps: Short-Term Crypto Price Prediction

Quant methods for short-term price prediction of tradable assets have been studied by academics and practitioners throughout finance. One such robust predictor of price movements is the micro-price. The micro-price “can be considered to be the ‘fair’ price of an asset, conditional on the information in the order book”, and has been shown to be a better short-term price predictor in equity markets than the mid-price and weighted mid-price (Stoikov 2017). In this study we seek to apply this idea to define a robust estimator of the micro-price for Bitcoin (BTC). Sourcing high-frequency, limit order book (LOB) data from Bitstamp, we construct three mid-price adjustment estimators of the micro-price. We show that the Volume-Adjusted Mid-Price (VAMP) outperforms trade and quote imbalance adjusted mid-prices in both prediction of short-term price direction and larger, one standard deviation price movements.
An Empirical Study on the Landscape of Mining and Mineral Processing (MMP) With Big Data

Over the last two decades, mining and mineral processing (MMP) has changed dramatically. Little is known about the bibliometric analysis of MMP. To this end, this study used the big data analysis to investigate the quantity and quality of scientific outputs in MMP over the past 21-year timespan. This study used IBM SPSS Statistics 25.0 and VOSviewer software to research on the 20 journals from Science Citation Index Expanded (SCI-Expanded) of Web of Science Core Collection (WOSCC). VOSviewer software was used to identify the visualization contributions of scientific outputs over 21-year timespan. Totally, the big data analysis shows people of China have the highest cumulative IFs, but their mean IFs are relatively low and are ranked in fourth place. Visually, people of Chin ranked the first in total link strength (2967), but not in links (86), which is the third place among Top15 countries. From the perspective of quality, it cannot rank the first. Thus, people of China should put more effort into improving the quality of scientific outputs.