Reviewerly/exHarmony huggingface dataset:
Bitcoin versus high-performance technology stocks in diversifying against global stock market indices

This paper investigates Bitcoin in finance but from the perspective of technology. Through comparing the relationship between the returns of Bitcoin (in terms of the US Dollar, Korean Won, and Euro) and high-performance technology stocks, with a range of global stock markets, we investigate whether there is any evidence of potential hedging and diversification properties and whether these are conditional on the states of the respective markets. We implement a quantile-on-quantile regression method to examine the relationship between both Bitcoin returns and technology stock returns, and stock market returns at varying quantiles. Our results show that although Bitcoin and high-performance technology stocks arguably share many similarities, it is clear that Bitcoin exhibits significant differences compared with high-performance technology stocks in terms of the diversification properties against global stock markets. With respect to the US stock markets, Bitcoin appears to be a consistent hedge when it is in a bullish state, however, there is some divergence in the relationships in relation to the European and Asian stock markets. In particular, Bitcoin (in terms of the Korean Won) may act as a safe haven against Asian stock markets when both are in a bearish state, although there does not appear to be strong and consistent evidence of this safe haven property across all Bitcoin returns and stock markets. From a financial perspective, this may suggest that individuals do not see and treat Bitcoin as a technology (or a technology company), but rather further supports the view of Bitcoin as a potential investment for financial gain (given its diversification properties).
genieclust: Fast and robust hierarchical clustering

genieclust is an open source Python and R package that implements the hierarchical clustering algorithm called Genie. This method frequently outperforms other state-of-the-art approaches in terms of clustering quality and speed, supports various distances over dense, sparse, and string data domains, and can be robustified even further with the built-in noise point detector. As domain-independent software, it can be used for solving problems arising in all data-driven research and development activities, including environmental, health, biological, physical, decision, and social sciences as well as technology and engineering. The Python version provides a scikit-learn-compliant API, whereas the R variant is compatible with the classic hclust(). Numerous tutorials, use cases, non-trivial examples, documentation, installation instructions, benchmark results and timings can be found at https://genieclust.gagolewski.com/.
Geochemical anomaly definition using multifractal modeling, validated by geological field observations: Siah Jangal area, SE Iran

Recognition of geochemical anomalies is a pivotal assignment in exploration projects. This study aims to delineate different AuCu geochemical anomalies using number-size (N-S) and concentration-area (C-A) multifractal models in the Siah Jangal area, SE Iran. In this research, lithogeochemical datasets were applied for the exploration of Au and Cu. A comparison between geochemical anomaly maps based on the N-S and C-A fractal models shows the N-S fractal modeling is a powerful tool for separation of weak elemental geochemical anomalies in all of sampling zones. Based on a comparison between the results of these two methods and field studies, the geochemical anomaly zones, defined by the N-S fractal model, are more accurate than those recognized by the C-A fractal model. The obtained results of the N-S and C-A fractal models have been interpreted with the extensive set of information including structural interpretation, geological and alteration data. Au and Cu mineralization in the Siah Jangal area are hosted mainly by Oligocene-Miocene sub-volcanic rocks, especially strongly altered porphyric quartz diorite, hornblende diorite and diorite. Moreover, the positive dependence between various alteration zones and high concentrations of Au and Cu proves that strongly anomalous areas are correlated with these alteration zones. High grade Au (> 1000 ppb) and Cu (>150 ppm) are associated with the altered sub-volcanic rocks in the northern, eastern, and SW parts of the study area. Therewith, the strong anomaly populations are mostly occurred within the fault and fracture systems in the study area. This is a promising signal because quartz-sulfide veins and veinlets are associated with such structures.
Exploit Visual Dependency Relations for Semantic Segmentation

Dependency relations among visual entities are ubiquity because both objects and scenes are highly structured. They provide prior knowledge about the real world that can help improve the generalization ability of deep learning approaches. Different from contextual reasoning which focuses on feature aggregation in the spatial domain, visual dependency reasoning explicitly models the dependency relations among visual entities. In this paper, we introduce a novel network architecture, termed the dependency network or DependencyNet, for semantic segmentation. It unifies dependency reasoning at three semantic levels. Intra-class reasoning decouples the representations of different object categories and updates them separately based on the internal object structures. Inter-class reasoning then performs spatial and semantic reasoning based on the dependency relations among different object categories. We will have an in-depth investigation on how to discover the dependency graph from the training annotations. Global dependency reasoning further refines the representations of each object category based on the global scene information. Extensive ablative studies with a controlled model size and the same network depth show that each individual dependency reasoning component benefits semantic segmentation and they together significantly improve the base network. Experimental results on two benchmark datasets show the DependencyNet achieves comparable performance to the recent states of the art.
Exploring the technological acceptance of a mobile learning tool used in the teaching of an indigenous language

Language is the primordial element for cultural transfer in indigenous communities; if it is not practiced, there is a risk of losing it and with it, a large part of the history of a community. Ecuador is a multicultural and multiethnic country with 18 indigenous peoples. Currently, in this country, some native languages are at risk of disappearing due to factors such as racial discrimination, underestimation of the language, and, above all, the lack of interest and motivation of the new generations to learn this language. Information technologies have made it possible to create mobile applications such as games, dictionaries, and translators that promote the learning of the Kichwa language. However, the acceptance of technology has not been evaluated, nor the intention to involve mobile devices in the process of teaching this language. Subsequently the objective of this work is to explore the acceptance of technology and the use of mobile devices to motivate the learning of the Kichwa language. For this purpose, the mobile application “Otavalo Rimay” was used with several students of a Kichwa language learning center. The methodology used to verify the hypothesis of this work was Design Sciences Research (DSR) together with the theory of acceptance and use of technology (UTAUT). The instrument used for this evaluation was a survey carried out after the use of the mobile application. The statistical analysis of the results obtained indicates characteristics such as the utility and perceived ease of use, positively influence students to motivate the use of mobile devices in learning a language. The results also show the great technological acceptance by students for learning and confirm that currently, mobile learning is accepted for use in education.
Multi-Class Sentiment Analysis of Social Media Data with Machine Learning Algorithms

The volume of social media data on the Internet is constantly growing.This has created a substantial research field for data analysts.The diversity of articles, posts, and comments on news websites and social networks astonishes imagination.Nevertheless, most researchers focus on posts on Twitter that have a specific format and length restriction.The majority of them are written in the English language.As relatively few works have paid attention to sentiment analysis in the Russian and Kazakh languages, this article thoroughly analyzes news posts in the Kazakhstan media space.The amassed datasets include texts labeled according to three sentiment classes: positive, negative, and neutral.The datasets are highly imbalanced, with a significant predominance of the positive class.Three resampling techniques (undersampling, oversampling, and synthetic minority oversampling (SMOTE)) are used to resample the datasets to deal with this issue.Subsequently, the texts are vectorized with the TF-IDF metric and classified with seven machine learning (ML) algorithms: naïve Bayes, support vector machine, logistic regression, k-nearest neighbors, decision tree, random forest, and XGBoost.Experimental results reveal that oversampling and SMOTE with logistic regression, decision tree, and random forest achieve the best classification scores.These models are effectively employed in the developed social analytics platform.
Tuning configuration of apache spark on public clouds by combining multi-objective optimization and performance prediction model

Choosing the right configuration for Spark deployed in the public cloud to ensure the efficient running of periodic jobs is hard, because there can be a huge configuration space to explore which is composed of numerous performance-related parameters in different dimensions (e.g., application-level and cloud-level). Choosing poorly will not only significantly degrade performance but may also lead to greater overhead. However, automatically searching for the optimal configuration of various applications to trade-off performance and cost is challenging. To address this issue, we propose a new optimal configuration search algorithm named AB-MOEA/D by combining multi-objective optimization algorithm and performance prediction model. AB-MOEA/D uses a decomposition-based multi-objective optimization algorithm to find the configuration with the objective of minimizing the execution time and cost, where the performance model constructed on the Adaboost algorithm is used to evaluate the fitness of each candidate configuration. Besides, we also present the configuration automatic tuning system with AB-MOEA/D as the optimization engine. The experimental results on six benchmarks with five data sets show that AB-MOEA/D significantly outperforms the previous work in terms of execution time and cost, with average improvements of approximately 35 and 40 percent.
A novel shape matching descriptor for real-time static hand gesture recognition

The current state-of-the-art hand gesture recognition methodologies heavily rely in the use of machine learning. However there are scenarios that machine learning cannot be applied successfully, for example in situations where data is scarce. This is the case when one-to-one matching is required between a query and a dataset of hand gestures where each gesture represents a unique class. In situations where learning algorithms cannot be trained, classic computer vision techniques such as feature extraction can be used to identify similarities between objects. Shape is one of the most important features that can be extracted from images, however the most accurate shape matching algorithms tend to be computationally inefficient for real-time applications. In this work we present a novel shape matching methodology for real-time hand gesture recognition. Extensive experiments were carried out comparing our method with other shape matching methods with respect to accuracy and computational complexity. Our method outperforms the other methods and provides a good combination of accuracy and computational efficiency for real-time applications. • Novel shape matching method for real-time hand gesture recognition. • Novel shape matching problem formulation. • Comparison of several shape matching methods. • Novel static hand gesture matching benchmark dataset.
Cryptocurrency Price Prediction Using Neural Networks and Deep Learning

This rise in cryptocurrencies' value has contributed to the decentralization of authority, lowering control amongst countries. The wide price range of digital currencies highlights the need for reliable preparation for predicting the currency's price. A new model is a situation in which this paper presents a new way of forecasting digital value for money by considering several variables, such as stock market capitalization, volume, distribution, and high-end delivery. To include training results, active LSTM networks, and an overview of long-term organizations are considered. The proposed technique is used for the benchmark data sets. The results indicate the efficiency of the forecasting of digital currency.
Application of digital image processing technology in online education under COVID-19 epidemic

In the current epidemic situation, appropriate learning resources are the prerequisite and basis for effective online education. Current online education, teacher teaching, and student learning lack a sense of immersion and participation. The reason is that the technology of online education is lagging. The thesis introduces augmented reality technology to enhance the immersion and interactivity of online education; introduces the technology based on Hadhoop to store image resources and improve the speed of reading image resources; the use of random forest algorithm for image recognition digital information Mining to recommend the most suitable online resources. Image processing technology can develop appropriate interactions and functions according to different scenarios to enhance the immersion and participation of online teaching, comprehensively improve the quality of teaching and learning, and provide technical support for the improvement of online learning.
Blockchain Based Counterfeit Medicine Authentication System

For a few decades, it is a very big challenge to monitor and keep track of genuine medicine in health care. Lacking a trust system and strong monitoring authority, syndicates can make counterfeit medicine easily. With the shifting of life-critical healthcare, it becomes an emergency to ensure substandard drugs. Because counterfeit medicine has a deadly effect on the human body and has disastrous results. To detect the falsified medicine, we proposed a drug tracing system using blockchain technology. Our system is able to detect substandard and anomaly drugs from manufacturer company to patient's hand. Also can verify the defective and expired drugs in the market using smartphones by scanning QR (Quick Response) code. Blockchain security could make the system more transparent and reliable. This paper aims to ensure drug quality, transaction security, and data safety using blockchain technology.
Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles

Machine learning obtains good accuracy in determining the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles. However, the models used so far are not understandable to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of explainable artificial intelligence (XAI) to help users understand why specific predictions are made. Where previous attempts at explainability for NOC estimation have relied upon using simpler, more understandable models that achieve lower accuracy, we use techniques that can be applied to any machine learning model. Our explanations incorporate SHAP values and counterfactual examples for each prediction into a single visualization. Existing methods for generating counterfactuals focus on uncorrelated features. This makes them inappropriate for the highly correlated features derived from STR data for NOC estimation, as these techniques simulate combinations of features that could not have resulted from an STR profile. For this reason, we have constructed a new counterfactual method, Realistic Counterfactuals (ReCo), which generates realistic counterfactual explanations for correlated data. We show that ReCo outperforms state-of-the-art methods on traditional metrics, as well as on a novel realism score. A user evaluation of the visualization shows positive opinions of end-users, which is ultimately the most appropriate metric in assessing explanations for real-world settings.
On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best

We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state evolutionary algorithms (EAs). For the standard bimodal benchmark function TwoMax , we rigorously prove that using uniform parent selection leads to exponential runtimes with high probability to locate both optima for the standard ( <?TeX $\mu$?> +1) EA and ( <?TeX $\mu$?> +1) RLS with any polynomial population sizes. However, we prove that selecting the worst individual as parent leads to efficient global optimisation with overwhelming probability for reasonable population sizes. Since always selecting the worst individual may have detrimental effects for escaping from local optima, we consider the performance of stochastic parent selection operators with low selective pressure for a function class called TruncatedTwoMax, where one slope is shorter than the other. An experimental analysis shows that the EAs equipped with inverse tournament selection, where the loser is selected for reproduction and small tournament sizes, globally optimise TwoMax efficiently and effectively escape from local optima of TruncatedTwoMax with high probability. Thus, they identify both optima efficiently while uniform (or stronger) selection fails in theory and in practice. We then show the power of inverse selection on function classes from the literature where populations are essential by providing rigorous proofs or experimental evidence that it outperforms uniform selection equipped with or without a restart strategy. We conclude the article by confirming our theoretical insights with an empirical analysis of the different selective pressures on standard benchmarks of the classical MaxSat and multidimensional knapsack problems.
Healthcare System Using Blockchain

Medical data privacy and data protection are crucial issues during medical services. Data security or secure storage of medical data is always a major concern for the majority of the population. An effective and efficient healthcare system requires a technology platform to communicate securely and seamlessly. Blockchain has been emerging technology for a few years now. The key features of blockchain which attract most developers are its immutability, decentralization, transparency, distributed ledger. So, the use of Blockchain in HealthCare systems will make a drastic impact as it can be used to securely store personal medical data. This paper has designed a HealthCare system that securely manages personal medical data and creates interaction between Doctors, Patients, Insurance Companies, and Pharmacy shops or Medical shops. The paper further highlights a comparison between a traditional system and the proposed system, describing the scope of the system, areas of future work.
3D-VNPU: A Flexible Accelerator for 2D/3D CNNs on FPGA

Three-dimensional convolutional neural networks (3D CNNs) have proven to be outstanding in applications such as video analysis, 3-dimension geometric data, and 3-dimension medical image diagnosis. Compared to 2D CNNs, 3D CNNs require high computational complexity to get spatio-temporal features while Winograd algorithm can significantly reduce the amount of computation. Prior works based on 3D Winograd accelerators are only applied to stride-1 convolution, however, most of the popular 3D CNNs contain stride-2 convolution layers. In this paper, we propose a novel flexible Winograd-based decomposition method (FWDM) to apply the 3D Winograd to different strides convolution. Evaluation results show that FWDM reduces computational complexity by a factor of 3.2 for C3D, 2.9 for 3D ConvNet, and 2.6 for 3D ResNet-18. Furthermore, we design a flexible computing engine to stretch the use range of the decomposition method. Coupling FWDM and computing engine, a Winograd-based, 2D/3D CNNs compatible, highly efficient, and flexible accelerator (3D-VNPU) is proposed. Finally, we demonstrate the effectiveness of 3D-VNPU on FPGA platform (Xilinx ZCU102) and achieve 1.35TOPS for C3D, 1.2TOPS for 3D ResNet-18, and 1.1TOPS for VGG-16. DSP efficiency outperforms other CNNs accelerators 2.57~15.3x compared with prior works in FPGA of C3D. Compared to GPU and CPU, our accelerator achieves improvement up to 37.9x in performance relative to CPU and 11.8x in energy efficiency relative to GPU.
Fast Modelling Algorithm for Realistic Three-Dimensional Human Face for Film and Television Animation

Aiming at the face photos of film and television animation, this paper proposes a new fast three-dimensional (3D) face modelling algorithm. First of all, based on the LBF algorithm, this paper proposes a multifeature selection idea to automatically detect multiple features of the face. Secondly, in order to solve the shortcomings of slow training speed while achieving large pose face alignment, the regression-based CNN is selected as the algorithm to achieve fast convergence. Then, due to the influence of various factors, the extracted feature points are not completely correct, and Gabor features are used to screen the matching of feature points. Finally, by analysing the principle of 3DMM 3D face reconstruction, a single-view 3D face reconstruction method based on CNN is proposed. The experimental results show that the algorithm in this paper has good reconstruction performance and real-time performance and can realize the rapid modelling of human face.
Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification

Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervision. We present Uncertainty-Wizard, a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading TF.KERAS deep learning API and it provides a near-transparent and easy to understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.