Reviewerly/exHarmony huggingface dataset:
Non‐Differentiable Leaning of Quantum Circuit Born Machine with Genetic Algorithm

The Quantum Circuit Born Machine (QCBM) is a generative quantum machine learning model that can be efficiently trained and run on the NISQ-era quantum processors. It is likely that QCBM will be one of the first quantum machine learning models to find productive applications in quantitative finance as a powerful market generator. In this paper we test QCBM performance on a dataset of spot FX log-returns (heavy-tailed distribution) as well as specially constructed mixture of Normal distributions (which models spiky light-tailed distribution). The QCBM has greater expressive power than comparable classical neural networks such as restricted Boltzmann machine (RBM) and, therefore, has potential to demonstrate quantum advantage by generating high-quality samples from the learned empirical distribution of the market risk factors while using less computational resources than its classical counterpart. However, efficient training of QCBM remains a challenging problem. Traditional differentiable learning approach may not work well when the loss function is highly non-smooth. In such cases it may be more efficient to use the non-differentiable learning methods. This paper proposes a non-differentiable learning approach to the training of QCBM based on genetic glgorithm (GA). The paper also presents results of the numerical experiments which compare performance of QCBM trained with GA against performance of the equivalent classical RBM and investigates the question of GA convergence as a function of QCBM architecture and the choice of algorithm's hyperparameters.
Blockchain Technology and Its Applications in the Healthcare Sector

Blockchain is a new technological revolution that has gained considerable attention after the successful implementation of Bitcoin. Researchers are working continuously in order to extend its applications in non-financial services so as to keep pace with research progress in this area. Interest of researchers in non-financial services ranging from cybersecurity, the internet of things (IoT), data management, and food science to healthcare industry is growing rapidly. There are a plethora of numerous use cases in healthcare industry where Blockchain can have significant impacts. By providing safe and secure management of data, Blockchain is reforming traditional healthcare practices. Blockchain in the future can resolve the problems like data isolation and communication delay caused due to a lack of interoperability. In this chapter, an attempt has been made to highlight the developments in healthcare industry due to Blockchain technology. In addition, this chapter would also discuss potential applications of Blockchain in healthcare, challenges faced, and future perspectives.
ALBERT over Match-LSTM Network for Intelligent Questions Classification in Chinese

This paper introduces a series of experiments with an ALBERT over match-LSTM network on the top of pre-trained word vectors, for accurate classification of intelligent question answering and thus the guarantee of precise information service. To improve the performance of data classification, a short text classification method based on an ALBERT and match-LSTM model was proposed to overcome the limitations of the classification process, such as few vocabularies, sparse features, large amount of data, lots of noise and poor normalization. In the model, Jieba word segmentation tools and agricultural dictionary were selected to text segmentation, GloVe algorithm was then adopted to expand the text characteristic and weighted word vector according to the text of key vector, bi-directional gated recurrent unit was applied to catch the context feature information and multi-convolutional neural networks were finally established to gain local multidimensional characteristics of text. Batch normalization, Dropout, Global Average Pooling and Global Max Pooling were utilized to solve overfitting problem. The results showed that the model could classify questions accurately, with a precision of 96.8%. Compared with other classification models, such as multi-SVM model and CNN model, ALBERT+match-LSTM had obvious advantages in classification performance in intelligent Agri-tech information service.
A Study of OWA Operators Learned in Convolutional Neural Networks

Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations.
Amata: An Annealing Mechanism for Adversarial Training Acceleration

Despite the empirical success in various domains, it has been revealed that deep neural networks are vulnerable to maliciously perturbed input data that much degrade their performance. This is known as adversarial attacks. To counter adversarial attacks, adversarial training formulated as a form of robust optimization has been demonstrated to be effective. However, conducting adversarial training brings much computational overhead compared with standard training. In order to reduce the computational cost, we propose an annealing mechanism, Amata, to reduce the overhead associated with adversarial training. The proposed Amata is provably convergent, well-motivated from the lens of optimal control theory and can be combined with existing acceleration methods to further enhance performance. It is demonstrated that on standard datasets, Amata can achieve similar or better robustness with around 1/3 to 1/2 the computational time compared with traditional methods. In addition, Amata can be incorporated into other adversarial training acceleration algorithms (e.g. YOPO, Free, Fast, and ATTA), which leads to further reduction in computational time on large-scale problems.
Fake Account Detection using Machine Learning and Data Science

Nowadays, Online Social Media is dominating the world in several ways. Day by day the number of users using social media is increasing drastically. The main advantage of online social media is that we can connect to people easily and communicate with them in a better way. This provided a new way of a potential attack, such as fake identity, false information, etc. A recent survey suggest that the number of accounts present in the social media is much greater than the users using it. This suggest that fake accounts have been increased in the recent years. Online social media providers face difficulty in identifying these fake accounts. The need for identifying these fake accounts is that social media is flooded with false information, advertisements, etc. Traditional methods cannot distinguish between real and fake accounts efficiently. Improvement in fake account creation made the previous works outdated. The new models created used different approaches such as automatic posts or comments, spreading false information or spam with advertisements to identify fake accounts. Due to the increase in the creation of the fake accounts different algorithms with different attributes are use. Previously use algorithms like naïve bayes, support vector machine, random forest has become inefficient in finding the fake accounts. In this research, we came up with an innovative method to identify fake accounts. We used gradient boosting algorithm with decision tree containing three attributes. Those attributes are spam commenting, artificial activity and engagement rate. We combined Machine learning and Data Science to accurately predict fake accounts.
The Emergence of a First Paradigm in Vocabulary Research: The Bibliometrics of System

This paper uses a bibliometric method to analyse the vocabulary research published in the journal System between 1976 and 2017. The bibliometric method used here is co-citation analysis, an approach which allows us to map the influences that have significantly impacted on vocabulary research. The analysis is intended to expand on an earlier analysis by Lei and Liu (2019), which studied the outputs in System but also analyses the features of all the papers published in the journal. This paper identifies five main clusters in the System data set. It also reports how these clusters grow and change over time. It argues that the data point to the emergence of a first paradigm in vocabulary research (Kuhn, 1971), and suggests some ways in which this paradigm might shift in response to demographic changes among the researchers who publish in System.
Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASR

With the advent of globalization, there is an increasing demand for multilingual automatic speech recognition (ASR), handling language and dialectal variation of spoken content. Recent studies show its efficacy over monolingual systems. In this study, we design a large multilingual end-to-end ASR using self-attention based conformer architecture. We trained the system using Arabic (Ar), English (En) and French (Fr) languages. We evaluate the system performance handling: (i) monolingual (Ar, En and Fr); (ii) multi-dialectal (Modern Standard Arabic, along with dialectal variation such as Egyptian and Moroccan); (iii) code-switching -- cross-lingual (Ar-En/Fr) and dialectal (MSA-Egyptian dialect) test cases, and compare with current state-of-the-art systems. Furthermore, we investigate the influence of different embedding/character representations including character vs word-piece; shared vs distinct input symbol per language. Our findings demonstrate the strength of such a model by outperforming state-of-the-art monolingual dialectal Arabic and code-switching Arabic ASR.
Multitask Training with Text Data for End-to-End Speech Recognition

We propose a multitask training method for attention-based end-to-end speech recognition models to better incorporate language level information. We regularize the decoder in a sequence-to-sequence architecture by multitask training it on both the speech recognition task and a next-token prediction language modeling task. Trained on either the 100 hour subset of LibriSpeech or the full 960 hour dataset, the proposed method leads to an 11% relative performance improvement over the baseline and is comparable to language model shallow fusion, without requiring an additional neural network during decoding. Analyses of sample output sentences and the word error rate on rare words demonstrate that the proposed method can incorporate language level information effectively.
Joint Topology Learning and Graph Signal Recovery Using Variational Bayes in Non-Gaussian Noise

This brief proposes a joint graph signal recovery and topology learning algorithm using a Variational Bayes (VB) framework in the case of non-Gaussian measurement noise. It is assumed that the graph signal is Gaussian Markov Random Field (GMRF) and the graph weights are considered statistical with the Gaussian prior. Moreover, the non-Gaussian noise is modeled using two distributions: Mixture of Gaussian (MoG), and Laplace. All the unknowns of the problem which are graph signal, Laplacian matrix, and the (Hyper)parameters are estimated by a VB framework. All the posteriors are calculated in closed forms and the iterative VB algorithm is devised to solve the problem. The efficiency of the proposed algorithm in comparison to some state-of-the-art algorithms in the literature is shown in the simulation results.
Domestic practices of increasing the international competitiveness of the Russian higher education institutions in the context of global challenges and threats

The Russian universities have to be demand among potential foreign applicants in the context of tightening competition in the global higher education market for resources, personnel and students. The possibility of attraction talented young people from other countries appears through the implementation of the education export in the context of the knowledge economy and global competition. In addition, interaction with foreign students contributes to the academic development of universities, knowledge diplomacy and international cooperation. The experience of the National Research University of Information Technologies, Mechanics and Optics and the Peoples’ Friendship University of Russia in the field of increasing and applying indicators for assessing international competitiveness has been analysed. The correlation and regression analysis of the dependence of the number of foreign students studying in Russia on the number of Russian universities in the world university rankings has been carried out. The assessment of changes in the work of universities to attract foreign students during the COVID-19 pandemic has been performed.
Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems

As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks. However, the majority of existing collaborative filtering systems are not well designed to handle missing data. Particularly, in order to inject the negative signals in the training phase, these solutions largely rely on negative sampling from unobserved user-item interactions and simply treating them as negative instances, which brings the recommendation performance degradation. To address the issues, we develop a Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is capable of exploring transferable knowledge from observed and unobserved user-item interactions. The network architecture of CRANet is formed of an integrative structure with a reflective receptor network and an information fusion autoencoder module, which endows our recommendation framework with the ability of encoding implicit user's pairwise preference on both interacted and non-interacted items. Additionally, a parametric regularization-based tied-weight scheme is designed to perform robust joint training of the two-stage CRANet model. We finally experimentally validate CRANet on four diverse benchmark datasets corresponding to two recommendation tasks, to show that debiasing the negative signals of user-item interactions improves the performance as compared to various state-of-the-art recommendation techniques. Our source code is available at https://github.com/akaxlh/CRANet.
Parents’ Perception on the Implementation of Home Learning during Covid-19

This study aimed to describe the parents’ perception on the benefits and challenges towards home learning during the pandemic. Descriptive qualitative study taking a case in two elementary schools in Kupang, East Nusa Tenggara. There were 20 parents from both schools taken purposively as the sample. 20 questions posted on Google Form as the instrument. The results showed that the implementation of home learning brought two perceptions on the benefits and challenges. The benefits were regarding to possibility of home learning to provide an individualized experience and flexibility system of education, to minimize the possibility of the kids to get infected by the Covid-19, to intimate relationship between the kids and parents, to enforce as well as boost the parents’ digital literacy as well as creativity in carrying out the learning material. Further perception mentioned that the implementation of home learning also brought certain challenges to those parents such as, requiring more expenses to buy the internet quota, causing the kids less motivated and overwhelming in learning, raising the work load of the parents, time inefficiency for working parents by spending more time to assist the kids during the learning, increasing the tense between the kids and parents during the learning.
Arabic Code-Switching Speech Recognition Using Monolingual Data

Code-switching in automatic speech recognition (ASR) is an important challenge due to globalization. Recent research in multilingual ASR shows potential improvement over monolingual systems. We study key issues related to multilingual modeling for ASR through a series of large-scale ASR experiments. Our innovative framework deploys a multi-graph approach in the weighted finite state transducers (WFST) framework. We compare our WFST decoding strategies with a transformer sequence to sequence system trained on the same data. Given a code-switching scenario between Arabic and English languages, our results show that the WFST decoding approaches were more suitable for the intersentential code-switching datasets. In addition, the transformer system performed better for intrasentential code-switching task. With this study, we release an artificially generated development and test sets, along with ecological code-switching test set, to benchmark the ASR performance.
An Attentive Fourier-Augmented Image-Captioning Transformer

Many vision–language models that output natural language, such as image-captioning models, usually use image features merely for grounding the captions and most of the good performance of the model can be attributed to the language model, which does all the heavy lifting, a phenomenon that has persisted even with the emergence of transformer-based architectures as the preferred base architecture of recent state-of-the-art vision–language models. In this paper, we make the images matter more by using fast Fourier transforms to further breakdown the input features and extract more of their intrinsic salient information, resulting in more detailed yet concise captions. This is achieved by performing a 1D Fourier transformation on the image features first in the hidden dimension and then in the sequence dimension. These extracted features alongside the region proposal image features result in a richer image representation that can then be queried to produce the associated captions, which showcase a deeper understanding of image–object–location relationships than similar models. Extensive experiments performed on the MSCOCO dataset demonstrate a CIDER-D, BLEU-1, and BLEU-4 score of 130, 80.5, and 39, respectively, on the MSCOCO benchmark dataset.
ACE-GCN: A Fast Data-driven FPGA Accelerator for GCN Embedding

ACE-GCN is a fast and resource/energy-efficient FPGA accelerator for graph convolutional embedding under data-driven and in-place processing conditions. Our accelerator exploits the inherent power law distribution and high sparsity commonly exhibited by real-world graphs datasets. Contrary to other hardware implementations of GCN, on which traditional optimization techniques are employed to bypass the problem of dataset sparsity, our architecture is designed to take advantage of this very same situation. We propose and implement an innovative acceleration approach supported by our “implicit-processing-by-association” concept, in conjunction with a dataset-customized convolutional operator. The computational relief and consequential acceleration effect arise from the possibility of replacing rather complex convolutional operations for a faster embedding result estimation. Based on a computationally inexpensive and super-expedited similarity calculation, our accelerator is able to decide from the automatic embedding estimation or the unavoidable direct convolution operation. Evaluations demonstrate that our approach presents excellent applicability and competitive acceleration value. Depending on the dataset and efficiency level at the target, between 23× and 4,930× PyG baseline, coming close to AWB-GCN by 46% to 81% on smaller datasets and noticeable surpassing AWB-GCN for larger datasets and with controllable accuracy loss levels. We further demonstrate the unique hardware optimization characteristics of our approach and discuss its multi-processing potentiality.
Geoenvironmental analysis of oil extraction activities in urban and rural zones of Santa Elena Province, Ecuador

From an environmental viewpoint, oil wells can be considered a potential source of pollution when improperly managed.The Santa Elena province (Ecuador), located on the Pacific coast, currently has a residual crude oil extraction compared to production generated in the Amazon region of the country.However, this activity in the coastal zone is very near to urban and rural populations.Therefore, a detailed influence analysis of these wells on the environment is necessary.This work aims to analyse, from a geoenvironmental perspective, the oil wells impact located in a pilot zone (urban and rural) of the oil field studied and, complementarily, to describe their incidence on the community and territory.The methodological process includes (i) oil wells compilation and inventory within the study zone and selection of a pilot zone; (ii) contaminating factors identification generated by the wells infrastructure (mechanical and territorial) and their current state of activity (production or abandoned); (iii) environmental impacts analysis generated through the development of a cause-effect matrix and, finally, the evaluation of a method and results found through a focus group technique.The results reflect a negative impact on the land cover and vegetative-animal environment in the vicinity of the wells, caused by the continuous release of gases, metallic oxidation and bituminous exhumation.The overall impacts interpretation compiled indicates that comprehensive action is needed at the wells to control and minimise them.The implementation of new environmental strategies through zoning can help to achieve adequate land-use planning, thus combining the safe and sustainable use of the resource with the development of other community activities (urbanisation, tourism, industry, agriculture and fishing) and environmental protection.
Conditional Loss and Deep Euler Scheme for Time Series Generation

We introduce three new generative models for time series that are based on Euler discretization of Stochastic Differential Equations (SDEs) and Wasserstein metrics. Two of these methods rely on the adaptation of generative adversarial networks (GANs) to time series. The third algorithm, called Conditional Euler Generator (CEGEN), minimizes a dedicated distance between the transition probability distributions over all time steps. In the context of Itô processes, we provide theoretical guarantees that minimizing this criterion implies accurate estimations of the drift and volatility parameters. Empirically, CEGEN outperforms state-of-the-art and GANs on both marginal and temporal dynamic metrics. Besides, correlation structures are accurately identified in high dimension. When few real data points are available, we verify the effectiveness of CEGEN when combined with transfer learning methods on model-based simulations. Finally, we illustrate the robustness of our methods on various real-world data sets.