Reviewerly/exHarmony huggingface dataset:
Economic Globalization, Entrepreneurship, and Inclusive Growth in Africa

An increasing number of studies are examining the relationship between entrepreneurship and growth. This relationship is controversial, especially for developing countries. Recent improvements in economic growth have led to a focus on growth inclusiveness, which spreads economic opportunities throughout a society. However, studies that focus on the role of entrepreneurship in inclusive growth remain scarce. To fill that gap, this study investigates the dynamic relationship between economic globalization, entrepreneurship, and inclusive growth in 21 African countries using panel econometrics to examine data covering 2006 to 2018. The results reveal that the impact of economic globalization and entrepreneurship on inclusive growth is positive and significant. We find that economic globalization enhances entrepreneurial development, and causality tests show that economic globalization drives inclusive growth. We also find a unidirectional causality from entrepreneurship to inclusive growth. Finally, we observe no direction of causality between economic globalization and entrepreneurship but observe a bidirectional causality between governance and entrepreneurship. We discuss the implications of these results.
Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.
A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN.
DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

Abstract Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.
Survey of BERT-Base Models for Scientific Text Classification: COVID-19 Case Study

On 30 January 2020, the World Health Organization announced a new coronavirus, which later turned out to be very dangerous. Since that date, COVID-19 has spread to become a pandemic that has now affected practically all regions in the world. Since then, many researchers in medicine have contributed to fighting COVID-19. In this context and given the great growth of scientific publications related to this global pandemic, manual text and data retrieval has become a challenging task. To remedy this challenge, we are proposing CovBERT, a pre-trained language model based on the BERT model to automate the literature review process. CovBERT relies on prior training on a large corpus of scientific publications in the biomedical domain and related to COVID-19 to increase its performance on the literature review task. We evaluate CovBERT on the classification of short text based on our scientific dataset of biomedical articles on COVID-19 entitled COV-Dat-20. We demonstrate statistically significant improvements by using BERT.
TableFormer: Robust Transformer Modeling for Table-Text Encoding

Understanding tables is an important aspect of natural language understanding.Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias.Such spurious biases make the model vulnerable to row and column order perturbations.Additionally, prior work has not thoroughly modeled the table structures or table-text alignments, hindering the table-text understanding ability.In this work, we propose a robust and structurally aware table-text encoding architecture TABLEFORMER, where tabular structural biases are incorporated completely through learnable attention biases.TABLEFORMER is (1) strictly invariant to row and column orders, and, (2) could understand tables better due to its tabular inductive biases.Our evaluations showed that TABLEFORMER outperforms strong baselines in all settings on SQA, WTQ and TABFACT table reasoning datasets, and achieves state-of-the-art performance on SQA, especially when facing answer-invariant row and column order perturbations (6% improvement over the best baseline), because previous SOTA models' performance drops by 4% -6% when facing such perturbations while TABLEFORMER is not affected.
E-HRM and employee flexibility in Islamic banks in Jordan

The study aimed at investigating the effect of e-HRM use on employee flexibility based on Davis’ technology acceptance model. Seven hypotheses were proposed. Two external factors (HR department role and organizational readiness) were linked to e-HRM perceived usefulness and e-HRM ease of use. These two factors linked to the behavioral intention to use e-HRM, which in turn connected to employee flexibility. All these propositions were accepted through analyzing data collected via a questionnaire from a sample consisting of managers and employees of human resource departments in Islamic banks in Jordan. The study contributes to the literature through clarifying and extending the technology acceptance model of e-HRM, as identifying two of the external factors that significantly affect e-HRM perceived usefulness and e-HRM ease of use, as well as, and spreading the model to include employee flexibility.
A novel squeeze YOLO-based real-time people counting approach

Real-time people counting based on videos is one of the most popular projects in the construction of smart cities.To develop an accurate people counting approach, deep learning can be used as it greatly improves the accuracy of machine learning-based approaches.To this end, we have previously proposed an accurate you only look once (YOLO)-based people counting approach, dubbed YOLO-PC.However, the model of YOLO-PC was very large with an excessive number of parameters, thus it requires large storage space on the device and makes transmission on internet a time consuming task.In this paper, a new real-time people counting method named as squeeze YOLO-based people counting (S-YOLO-PC) is proposed.S-YOLO-PC uses the fire layer of SqueezeNet to optimise the network structure, which reduces the number of parameters used in the model without decreasing its accuracy.Based on the obtained the experimental results, S-YOLO-PC reduces the number of model parameters by 11.5% and 9% compared to YOLO and YOLO-PC, respectively.S-YOLO-PC can also detect and count people with 41 frames per second (FPS) with the average precision (AP) of person of 72%.
Designing the Teaching Model of Multilevel Gradual Training of Students in Innovative Engineering

Introduction. Undoubtedly, today the innovative training of students is a must, but the problem is how to realize it methodologically, meeting contemporary needs of the state and society. The objective of the article is to design a teaching model that would serve the basis for the methodology of training students in innovative engineering, based on multilevel integration and gradual education, adapted to certain conditions of the educational environment. Materials and Methods. The article developed the methodology for intertwined integrated and interdisciplinary scientific and methodological approaches comprising the unified system. It aimed to develop models of multilevel gradual training of students in innovative engineering. Methods (morphological one, involved in compilation of models for integrating levels and stages of learning; hypothetical-deductive one helping formulate the working hypothesis; experimental one confirming the working hypothesis and aiding to study the efficiency, etc.) and principles (multilevelness and gradualness) formed the basis for the development of models for training in innovative engineering. Results. The morphological table of models of multilevel gradual training of technical university students in IE was developed. One model of interdisciplinary integration of general technical disciplines with various types of activity was implemented using three stages (basic, synthesis-modelling, design and production) within the framework of the business game “Design office”. Model efficiency and the hypothesis were confirmed during the experiment. Discussion and Conclusion. The method was proposed for selecting models of multilevel and gradual training of students in innovative engineering. The methodology for interdisciplinary integration of general technical disciplines with various activities was developed. The methodology of the pedagogical experiment was elaborated for multilevel monitoring of innovative competences’ formedness. All results form the basis for methodological and scientific support in shaping students’ innovative thinking.
Boosting Neural Machine Translation with Similar Translations

This paper explores data augmentation methods for training Neural Machine Translation to make use of similar translations, in a comparable way a human translator employs fuzzy matches.In particular, we show how we can simply feed the neural model with information on both source and target sides of the fuzzy matches, we also extend the similarity to include semantically related translations retrieved using distributed sentence representations.We show that translations based on fuzzy matching provide the model with "copy" information while translations based on embedding similarities tend to extend the translation "context".Results indicate that the effect from both similar sentences are adding up to further boost accuracy, are combining naturally with model fine-tuning and are providing dynamic adaptation for unseen translation pairs.Tests on multiple data sets and domains show consistent accuracy improvements.To foster research around these techniques, we also release an Open-Source toolkit with efficient and flexible fuzzy-match implementation.
Vision-Based Anti-UAV Detection and Tracking

Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. Several detection and tracking systems for UAVs have been introduced in recent years, but most of them are based on radio frequency, radar, and other media. We assume that the field of computer vision is mature enough to detect and track invading UAVs. Thus we propose a visible light mode dataset called Dalian University of Technology Anti-UAV dataset, DUT Anti-UAV for short. It contains a detection dataset with a total of 10,000 images and a tracking dataset with 20 videos that include short-term and long-term sequences. All frames and images are manually annotated precisely. We use this dataset to train several existing detection algorithms and evaluate the algorithms’ performance. Several tracking methods are also tested on our tracking dataset. Furthermore, we propose a clear and simple tracking algorithm combined with detection that inherits the detector’s high precision. Extensive experiments show that the tracking performance is improved considerably after fusing detection, thus providing a new attempt at UAV tracking using our dataset. The datasets and results are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wangdongdut/DUT-Anti-UAV</uri> .
A Novel DVL Calibration Method Based on Robust Invariant Extended Kalman Filter

The calibration accuracy of the Doppler Velocity Log (DVL) error directly affects the performance of the SINS/DVL integrated navigation system. Previous studies of DVL calibration have not dealt with DVL measurement outliers. Therefore, in this paper, a novel DVL calibration method is proposed to reduce the influence of outliers on calibration accuracy. Introducing the Lie group theory to the DVL calibration study for the first time, and taking advantage of the Special Orthogonal Group of order 3 [SO(3)] representation, a new DVL calibration model based SO(3) group with the ability to represent large misalignment angles is established. And an invariant extended Kalman filter (IEKF) design for the DVL calibration model is introduced. Then, a robust IEKF is proposed by combining the linear error propagation equation of the SO(3)-based DVL calibration model with the statistical similarity measure (SSM) theory. The proposed robust IEKF algorithm enriches the IEKF theory and has the ability to accurately estimate the state of the Lie group model in outlier environments. The simulation and lake trial are used to illustrate the effectiveness and superiority of the proposed DVL calibration method.
SiamBAN: Target-Aware Tracking With Siamese Box Adaptive Network

Variation of scales or aspect ratios has been one of the main challenges for tracking. To overcome this challenge, most existing methods adopt either multi-scale search or anchor-based schemes, which use a predefined search space in a handcrafted way and therefore limit their performance in complicated scenes. To address this problem, recent anchor-free based trackers have been proposed without using prior scale or anchor information. However, an inconsistency problem between classification and regression degrades the tracking performance. To address the above issues, we propose a simple yet effective tracker (named Siamese Box Adaptive Network, SiamBAN) to learn a target-aware scale handling schema in a data-driven manner. Our basic idea is to predict the target boxes in a per-pixel fashion through a fully convolutional network, which is anchor-free. Specifically, SiamBAN divides the tracking problem into classification and regression tasks, which directly predict objectiveness and regress bounding boxes, respectively. A no-prior box design is proposed to avoid tuning hyper-parameters related to candidate boxes, which makes SiamBAN more flexible. SiamBAN further uses a target-aware branch to address the inconsistency problem. Experiments on benchmarks including VOT2018, VOT2019, OTB100, UAV123, LaSOT and TrackingNet show that SiamBAN achieves promising performance and runs at 35 FPS.
Influence of Knowledge Area on the Use of Digital Tools during the COVID-19 Pandemic among Latin American Professors

In this paper, quantitative, descriptive, and correlational research is carried out on the impact that the process of digitalization of academic activities in higher education has had on the habits of use of information and communication technologies (ICT) among professors and the influence of the area of knowledge on this impact. For this purpose, responses from 716 professors from different Latin American universities to a survey designed by the authors were statistically analyzed. Following the UNESCO guide on the use of ICT in higher education, the survey distinguishes the following teaching activities to measure the use of ICT: lessons, tutorials, sharing materials, and evaluation. The results show that the use of ICT has increased in all teaching activities, but unevenly, being particularly intense in those in which the use of ICT was less frequent during the pandemic—specifically, tutorials and evaluation, whose frequency of use has increased by around 50%. As a result of this uneven increase, the use of ICTs in different teaching activities has become more homogeneous after the pandemic. This increase was significantly higher among engineering and social sciences professors than in other areas of knowledge, as well as in evaluation and tutorial tools than in other activities. In addition, the ICT tools most used during the pandemic were those with which the professors or students were most familiar before the pandemic. It is recommended that universities increase the specific training of professors in the pedagogical use of ICT and that they should address the specificities of each area of knowledge.
Deep language algorithms predict semantic comprehension from brain activity

Abstract Deep language algorithms, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of automatic translation, summarization and dialogue. However, whether these models encode information that relates to human comprehension still remains controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but they also predict the extent to which subjects understand the corresponding narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear mapping model to predict brain activity from GPT-2’s activations. Finally, we show that this mapping reliably correlates ( $$\mathcal {R}=0.50, p&lt;10^{-15}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:mn>0.50</mml:mn><mml:mo>,</mml:mo><mml:mi>p</mml:mi><mml:mo>&lt;</mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mrow><mml:mo>-</mml:mo><mml:mn>15</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> ) with subjects’ comprehension scores as assessed for each story. This effect peaks in the angular, medial temporal and supra-marginal gyri, and is best accounted for by the long-distance dependencies generated in the deep layers of GPT-2. Overall, this study shows how deep language models help clarify the brain computations underlying language comprehension.
Experimental quantum adversarial learning with programmable superconducting qubits

Quantum computing promises to enhance machine learning and artificial intelligence. However, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from adversarial perturbations as well. Here we report an experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built on variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 μs, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4%, respectively, with both real-life images (for example, medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would substantially enhance their robustness to such perturbations. The vulnerability of quantum machine learning models against adversarial noises, together with a defense strategy way out of this dilemma, is demonstrated experimentally with a programmable superconducting quantum processor.
The impact of natural resources, economic growth, savings, and current account balance on financial sector development: Theory and empirical evidence

The development of the financial sector is one of the main macroeconomic goals for developing countries as well as developed ones. The literature focusing on the determinants of financial sector development presents very diverse and inconsistent results. Therefore, the main aim of the study is to empirically investigate the long run and causal linkages between natural resources, economic growth, savings, current account balance and financial sector development for 33 developing countries. The study applies the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) and Dumitrescu-Hurlin panel causality tests, which are among the new panel data techniques. The results reveal that natural resources, economic growth, savings, current account balance and financial sector development are cointegrated. Natural resources, economic growth and current account balance negatively affect financial development while savings stimulate financial development. It is found that there exists a bi-directional causality between all the explanatory variables and financial development. Theoretical and policy implications are discussed within the perspective of macroeconomics and financial sustainability for developing countries.