Reviewerly/exHarmony huggingface dataset:
An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer

This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery.
Digital Competences for Smart Learning During COVID-19 in Higher Education Students from Spain and Latin America

Networked society has become the context of a technology-supported environment for learning and teaching. The access to knowledge is essentially mediated through connected smart devices that nurture digital skills and define a new learning concept: Smart learning, moreover during COVID-19 pandemic. This study aims to present a descriptive and correlational analysis focusing on the development of smart learning in three different areas when using the Internet: use, learning and critical thinking from the perspective of digital competences during pandemic lockdown. The study was designed under a non-experimental quantitative methodology in which 926 students in higher education from four countries participated. After an in-depth literature review, a questionnaire was designed including not only these three areas mentioned but a deeper analysis of each one in five areas of digital skills development. Results show significant differences among countries that could help to improve not only formal education in Higher Education, but the digital skills needed for lifelong learning for citizenship in a next future conditioned by recent situation. Based on findings, several suggestions are proposed for achieving a smart education supported by digital skills in online environments.
Estimating kaolinite crystallinity using near-infrared spectroscopy: Implications for its geology on Earth and Mars

Abstract Kaolinite is an Al-rich phyllosilicate commonly observed on Earth as a product of the chemical weathering of aluminosilicates. It has also been detected on the martian surface by orbital remote sensing observations. While the determination of the geological processes of formation of terrestrial kaolinite (i.e., hydrothermal activity, continental surface weathering, diagenesis) involves the coupling of field observation and multiple laboratory measurements, only geomorphology and associated minerals are generally available to determine their geological origin on Mars. Kaolinite crystallinity depends on many physicochemical parameters reflecting its conditions of crystallization. To determine if the near-infrared (NIR) spectral signature of kaolinite enables estimation of its crystallinity and furthermore if this method can be used to identify the geological processes involved in kaolinite formation, we carried out an in-depth analysis of NIR spectra of reference terrestrial kaolinites that formed in various geological contexts. We calculated second and third derivatives for each spectrum to highlight subtle variations in the spectral properties of kaolinite. This allowed the identification of 27 spectral contributions for the 4500 and 7000 cm−1 Al-OH-related regions of absorption bands. The position shifts and shape variations of these spectral contributions were intimately linked to variations of crystallinity, which was qualitatively estimated using Hinckley and Liétard XRD (dis)order indices. The results obtained show that the NIR signature of kaolinite is influenced by the stacking disorder of layers that has some influence on the vibrations of the interfoliar and inner Al-OH groups. Our study also confirms that: (1) well-ordered kaolinites are not restricted to hydrothermal deposits; (2) kaolinites from a similar sedimentary or pedogenetic context often display contrasting degrees of crystalline order; and (3) poorly ordered kaolinites are more likely to have a sedimentary or pedogenetic origin. Finally, this work highlights that obtaining spectra with sufficient spectral resolution could help to estimate the crystallinity of kaolinite and, in the best cases, its geological origin, both on Earth and Mars, especially with in situ NIR measurements.
Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning

Cross-domain recommendation (CDR) has been attracting increasing attention of researchers for its ability to alleviate the data sparsity problem in recommender systems. However, the existing single-target or dual-target CDR methods often suffer from two drawbacks, the assumption of at least one rich domain and the heavy dependence on domain-invariant preference, which are impractical in real world where sparsity is ubiquitous and might degrade the user preference learning. To overcome these issues, we propose a Multi-Sparse-Domain Collaborative Recommendation (MSDCR) model for multi-target cross-domain recommendation. Unlike traditional CDR methods, MSDCR treats the multiple relevant domains as all sparse and can simultaneously improve the recommendation performance in each domain. We propose a Multi-Domain Separation Network (MDSN) and a Gated Aspect Preference Enhancement (GAPE) module for MSDCR to enhance a user's domain-specific aspect preferences in a domain by transferring the complementary aspect preferences in other domains, during which the uniqueness of the domain-specific preference can be preserved through the adversarial training offered by MDSN and the complementarity can be adaptively determined by GAPE. Meanwhile, we propose a Multi-Domain Adaptation Network (MDAN) for MSDCR to capture a user's domain-invariant aspect preference. With the integration of the enhanced domain-specific aspect preference and the domain-invariant aspect preference, MSDCR can reach a comprehensive understanding of a user's preference in each sparse domain. At last, the extensive experiments conducted on real datasets demonstrate the remarkable superiority of MSDCR over the state-of-the-art single-domain recommendation models and CDR models.
<scp>QForte</scp>: An Efficient State-Vector Emulator and Quantum Algorithms Library for Molecular Electronic Structure

We introduce a novel open-source software package QForte, a comprehensive development tool for new quantum simulation algorithms. QForte incorporates functionality for handling molecular Hamiltonians, Fermionic encoding, ansatz construction, time evolution, and state-vector emulation, requiring only a classical electronic structure package as a dependency. QForte also contains black-box implementations of a wide variety of quantum algorithms, including variational and projective quantum eigensolvers, adaptive eigensolvers, quantum imaginary time evolution, and quantum Krylov methods. We highlight two features of QForte: (i) how the Python class structure of QForte enables the facile implementation of new algorithms, and (ii) how existing algorithms can be executed in just a few lines of code.
Cryptocurrencies’ hashrate and electricity consumption: evidence from mining activities

Purpose Given the growing importance of cryptocurrencies and the technique called “SegWit” that allows to compile more transactions in a mined block, the electricity consumed per block might potentially decrease. The purpose of this study is to consider that the difficulty to mine a block might be a better indicator of the Bitcoin\Ether’s electricity consumption. Design/methodology/approach This study applies the vector error correction model to investigate data related to primary energy consumption and electricity production, supply and consumption for Bitcoin and Ether hashrates from 2016M1 to 2021M5. Findings The hashrate (difficulty of solving the cryptographic problem related to the validation of a transaction) is found to have a positive cointegration with energy and electricity consumption. Despite the launch of the Segregation Witness (SegWit) mechanism allowing blocks to handle a higher number of transactions per block, this Bitcoin and Ether growing need in electricity has significantly been increasing since October 2019. Originality/value The major contribution of this study is to investigate a more relevant indicator, namely, hashrate (computational difficulty to solve cryptographic enigma associated with cryptocurrencies-related transaction). The approach of this study can be justified by the fact that there exists a technical solution consisting in increasing the number of transactions per blocks so that less electricity might be required to validate a transaction.
An attention-based framework for multi-view clustering on Grassmann manifold

The key problem of multi-view clustering is to handle the inconsistency among multiple views. This article proposes an attention-based framework for multi-view clustering on Grassmann manifold (AMCGM). To be specific, the proposed AMCGM framework aims to learn a representative element on Grassmann manifold with the following four highlights: 1) AMCGM framework performs an attention-based weighted-learning scheme to capture the difference of views; 2) The clustering results can be directly generated by the structured graph learned via AMCGM, avoiding the randomness caused by traditional label-generation procedures, such as K-means clustering; 3) AMCGM has high extensibility since it can generate many multi-view clustering models on Grassmann manifold; 4) On Grassmann manifold, the relationship between the projection metric (PM)-based multi-view clustering model and squared projection metric (SPM)-based model is studied. Based on AMCGM framework, we propose some generated models and provide some useful conclusions. Moreover, to solve the optimization problems involved in the proposed AMCGM framework and generated models, we propose an efficiently iterative algorithm and provide rigorous convergence analysis. Extensive experimental results demonstrate the superb performance of our framework.
Infinite Families of 3-Designs and 2-Designs From Almost MDS Codes

Combinatorial designs are closely related to linear codes. Recently, some near MDS codes were employed to construct <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -designs by Ding and Tang, which settles the question as to whether there exists an infinite family of near MDS codes holding an infinite family of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> -designs for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t \geq 2$ </tex-math></inline-formula> . This paper is devoted to the construction of infinite families of 3-designs and 2-designs from special equations over finite fields. First, we present an infinite family of almost MDS codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ GF}}(p^{m})$ </tex-math></inline-formula> holding an infinite family of 3-designs. We then provide an infinite family of almost MDS codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ GF}}(p^{m})$ </tex-math></inline-formula> holding an infinite family of 2-designs for any field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ GF}}(q)$ </tex-math></inline-formula> . In particular, some of these almost MDS codes are near MDS. Second, we present an infinite family of near MDS codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ GF}}(2^{m})$ </tex-math></inline-formula> holding an infinite family of 3-designs by considering the number of roots of a special linearized polynomial. Compared to previous constructions of 3-designs or 2-designs from linear codes, the parameters of some of our designs are new and flexible.
Comments on “MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge”

The MoNuSAC 2020 challenge was hosted at the ISBI 2020 conference, where the winners were announced.Challenge organizers, in addition to the leaderboard, released the evaluation code and visualisations of the prediction masks of the "top 5" teams.This shows a very high level of transparency, and provides a unique opportunity to better understand the challenge results.Our analysis of the code and all released data, however, shows three different problems in the computation of the metric used for the official ranking: a coding mistake resulting in erroneous false positives; another resulting in missed false positives; and a problem with the metric's aggregation method.We demonstrate the errors, and confirm that the mistaken version of the code was indeed used to rank the algorithms in the challenge.Our results can be fully replicated with the code provided on GitHub.
Cross Site Scripting Attacks Classification using Convolutional Neural Network

The global nature of web applications puts them at a high risk of attacks from different locations and with various levels of severity and complexity. Cross-Site Scripting (XSS) is a code injection attack that happens at the client-side i.e., through the web browsers. The attacker&#x0027;s main strategy is to execute the malicious injected scripts in a legitimate web application through the victim&#x0027;s browser. XSS vulnerabilities can be a source of other security attacks such as the spread of malware, credential theft, credential phishing, social network worms, and website defacing. Plenty of research has been carried out on the detection and prevention of XSS using machine learning techniques that involved URL-based features, HTML features, and JavaScript features and achieved an accuracy of about 98&#x0025;. XSS attacks can have different forms and evolve regularly, new patterns emerge daily, feature extraction can be challenging, so there is a high probability of being not exhaustive. A Convolutional Neural network can be useful for XSS classification tasks, as because of its architecture it requires less feature extraction pre-processing task. In this paper, we used almost all the characters of XSS Scripts during feature generation and used the Convolutional Neural Network (CNN) technique to classify and detect the XSS scripts as malicious or benign and achieved the accuracy of 98.62 and precision of 98.6 and recall 98.86.
Pyramid frequency network with spatial attention residual refinement module for monocular depth estimation

Deep-learning-based approaches to depth estimation are rapidly advancing, offering superior performance over existing methods. To estimate the depth in real-world scenarios, depth estimation models require the robustness of various noise environments. In this work, a Pyramid Frequency Network(PFN) with Spatial Attention Residual Refinement Module(SARRM) is proposed to deal with the weak robustness of existing deep-learning methods. To reconstruct depth maps with accurate details, the SARRM constructs a residual fusion method with an attention mechanism to refine the blur depth. The frequency division strategy is designed, and the frequency pyramid network is developed to extract features from multiple frequency bands. With the frequency strategy, PFN achieves better visual accuracy than state-of-the-art methods in both indoor and outdoor scenes on Make3D, KITTI depth, and NYUv2 datasets. Additional experiments on the noisy NYUv2 dataset demonstrate that PFN is more reliable than existing deep-learning methods in high-noise scenes.
Sentiment Classification Method Based on Blending of Emoticons and Short Texts

With the development of Internet technology, short texts have gradually become the main medium for people to obtain information and communicate. Short text reduces the threshold of information production and reading by virtue of its short length, which is in line with the trend of fragmented reading in the context of the current fast-paced life. In addition, short texts contain emojis to make the communication immersive. However, short-text content means it contains relatively little information, which is not conducive to the analysis of sentiment characteristics. Therefore, this paper proposes a sentiment classification method based on the blending of emoticons and short-text content. Emoticons and short-text content are transformed into vectors, and the corresponding word vector and emoticon vector are connected into a sentencing matrix in turn. The sentence matrix is input into a convolution neural network classification model for classification. The results indicate that, compared with existing methods, the proposed method improves the accuracy of analysis.
Research on Face Recognition Algorithm Based on Image Processing

While network technology is convenient for our daily life, the problems that are exposed are also endless. The most important thing for everyone is information security. In order to improve the security level of network information and identify and detect faces, the method used in this paper has improved compared with the traditional AdaBoost method and skin color method. AdaBoost detection is performed on the image, which reduces the probability of false detection. The experiment compares the experimental results of the AdaBoost method, the skin color method and the skin color + AdaBoost method. All operations in the KPCA and KFDA algorithms are performed by the inner product kernel function defined in the original space, and no specific non-linear mapping function is involved.The full name of KPCA is kernel principal component analysis. The full name of KFDA is kernel Fisher discriminant analysis. Combining the zero-space method kernel discriminant analysis method improves the ability of discriminant analysis to extract non-linear features. Through the secondary extraction of PCA features, a better recognition result than the PCA method is obtained. This paper also proposes a zero-space based Fisher discriminant analysis method. Experiments show that the zero-space-based method makes full use of the useful discriminant information in the zero space of the intraclass dispersion matrix, which improves the accuracy of face recognition to some extent.If you choose the polynomial kernel function, when d = 0.8, KPCA has a higher recognition ability. When d = 2, the recognition rate of KFDA and zero space-based KFDA is the largest. For polynomial functions, in general, d = 2.
The Evolution of Mining Pools and Miners’ Behaviors in the Bitcoin Blockchain

We analyzed 23 mining pools and explore the mobility of miners throughout Bitcoin’s history. Mining pools have emerged as major players to ensure that the Bitcoin system stays secure, valid, and stable. Many questions remain open regarding how mining pools have evolved throughout Bitcoin’s history and when and why miners join or leave the pools. We investigated the reward payout flow of mining pools and characterized them based on payout irregularity and structural complexity. Based on our proposed algorithm, we identified miners and studied their mobility in the pools over time. Our analysis shows that Bitcoin mining is an industry that is sensitive to external events (e.g., market price and government policy). Over time, competition between pools involving reward schemes and pool fees motivated miners to migrate between pools (i.e., pool hopping and cross pooling). These factors converged toward optimal scheme and values, which made mining activities more stable.
Improving Word Translation via Two-Stage Contrastive Learning

Word translation or bilingual lexicon induction (BLI) is a key cross-lingual task, aiming to bridge the lexical gap between different languages.In this work, we propose a robust and effective two-stage contrastive learning framework for the BLI task.At Stage C1, we propose to refine standard cross-lingual linear maps between static word embeddings (WEs) via a contrastive learning objective; we also show how to integrate it into the self-learning procedure for even more refined cross-lingual maps.In Stage C2, we conduct BLI-oriented contrastive fine-tuning of mBERT, unlocking its word translation capability.We also show that static WEs induced from the 'C2-tuned' mBERT complement static WEs from Stage C1.Comprehensive experiments on standard BLI datasets for diverse languages and different experimental setups demonstrate substantial gains achieved by our framework.While the BLI method from Stage C1 already yields substantial gains over all state-of-the-art BLI methods in our comparison, even stronger improvements are met with the full two-stage framework: e.g., we report gains for 112/112 BLI setups, spanning 28 language pairs.
Optimal and two-step adaptive quantum detector tomography

Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we design optimal probe states for detector estimation based on the minimum upper bound of the mean squared error (UMSE) and the maximum robustness. We establish the minimum UMSE and the minimum condition number for quantum detectors and provide concrete examples that can achieve optimal detector tomography. In order to enhance the estimation precision, we also propose a two-step adaptive detector tomography algorithm to optimize the probe states adaptively based on a modified fidelity index. We present a sufficient condition on when the estimation error of our two-step strategy scales inversely proportional to the number of state copies. Moreover, the superposition of coherent states is used as probe states for quantum detector tomography and the estimation error is analyzed. Numerical results demonstrate the effectiveness of both the proposed optimal and adaptive quantum detector tomography methods.
Applying Decision Tree for Hiding Data in Binary Images for Secure and Secret Information Flow

In today's digital world, it is noticeable that images are transferred in binary form which carries significant and confidential information. To secure and keep such information unnoticeable is the prime concern during transmission. Information hiding as watermarking or secret messaging are most acceptable ways to attain this. In this direction, a new information flow system using data hiding approach is proposed in this chapter. A decision tree is applied for locating the most suitable blocks for data hiding in the presented technique. Decision trees are one of the best techniques used as a classifier. In this approach, the decision tree classifies a particular block to be selected for hiding information, based on the LURD pattern of 3X3 pixel block of image and the blocks of information to be hidden. For security, information is encrypted and then scattered in the image. The receiver can retrieve the data without the original image. The results prove that the proposed scheme is capable of minimizing distortion caused due to data hiding.