Reviewerly/exHarmony huggingface dataset:
Ensemble Kalman inversion: mean-field limit and convergence analysis

Ensemble Kalman inversion (EKI) has been a very popular algorithm used in Bayesian inverse problems (Iglesias et al. in Inverse Probl 29: 045001, 2013). It samples particles from a prior distribution and introduces a motion to move the particles around in pseudo-time. As the pseudo-time goes to infinity, the method finds the minimizer of the objective function, and when the pseudo-time stops at 1, the ensemble distribution of the particles resembles, in some sense, the posterior distribution in the linear setting. The ideas trace back further to ensemble Kalman filter and the associated analysis (Evensen in J Geophys Res: Oceans 99: 10143–10162, 1994; Reich in BIT Numer Math 51: 235–249, 2011), but to today, when viewed as a sampling method, why EKI works, and in what sense with what rate the method converges is still largely unknown. In this paper, we analyze the continuous version of EKI, a coupled SDE system, and prove the mean-field limit of this SDE system. In particular, we will show that 1. as the number of particles goes to infinity, the empirical measure of particles following SDE converges to the solution to a Fokker–Planck equation in Wasserstein 2-distance with an optimal rate, for both linear and weakly nonlinear case; 2. the solution to the Fokker–Planck equation reconstructs the target distribution in finite time in the linear case, as suggested in Iglesias et al. (Inverse Probl 29: 045001, 2013).
Hybrid genetic algorithm based smooth global-path planning for a mobile robot

The goal of the global path planning task is to produce an optimal safe path for robot respect to the given map. There are many global path planning methods that have been studied. In this paper, we propose a novel Hybrid Genetic Algorithm (HGA) that is used to generating smooth paths for differential wheeled robots. The main idea of HGA is to provide the dynamic mutation rate and switchable global-local search method to the mutation operator of ordinary genetic algorithm. By deploying these modifications, the premature convergence of the generic genetic algorithm and the high time-consuming fitness calculation of the memetic algorithm are reduced. HGA also takes care of chromosome length (defined by the size of a set of points that construct a path) by applying the population replacement method. Our algorithm satisfies the important criteria in the path planning task: safe and minimum-length. Based on continuous-curvature Piecewise Cubic Bezier Curve, HGA directly provides the smoothed paths for differential wheeled robots. Therefore, our proposed algorithm does not need any third-party algorithm for smoothing planned path. Several experiments regarding the proposed algorithm on our robot and its results are analyzed and presented.
Comprehensive study on complex-valued ZNN models activated by novel nonlinear functions for dynamic complex linear equations

This paper proposes two complex-valued zeroing neural network (Cv-ZNN) models for solving dynamic complex time-variant linear equations. The models involve two complex-valued nonlinear processing methods and adapt two real-valued activation functions. The convergence and robustness of the two Cv-ZNN models are analyzed comprehensively. Firstly, the convergence discussion shows that the corresponding stable error can rapidly converge to zero in finite time, with the upper limit of constriction time calculated. The upper limit of stable error is successfully obtained when the model implementation error is injected into the Cv-ZNN models, which displays their superior robustness. Besides, the results of numerical simulations reveal that the Cv-ZNN models effectively address the time-variant linear system of equations over complex field, even in noisy environments. The simulation results also demonstrate that two novel nonlinear activation functions perform much better than the Linear, Power, Bipolar Sigmoid Power-Sigmoid and Sign-bi-power activation functions.
Globalization and female economic participation in MINT and BRICS countries

Purpose This study examines the effect of globalization on female economic participation (FEP) in MINT (Mexico, Indonesia, Nigeria and Turkey) and BRICS (Brazil, Russia, India, China and South Africa) countries between 2004 and 2018. Design/methodology/approach Four measures of globalization are employed and sourced from KOF globalization index, 2018, while the female labour force participation rate is a proxy for FEP. The empirical evidence is based on the Pooled Mean Group (PMG) estimator. Findings The findings of the PMG estimator from the Panel ARDL method reveal that political and overall globalization in MINT and BRICS countries have a positive impact on FEP, whereas social globalization exerts a negative impact on FEP in the long-run. It is observed that economic globalization has no long-run effect on FEP. Contrarily, all the measures of globalization reflect no short-run effect on FEP. This supports the argument that globalization has no immediate effect on FEP. Thus, it is recommended that both MINT and BRICS countries should find a way of improving the process of globalization generally to empower women to be involved in economic activities. Originality/value This study complements the extant literature by focusing on how globalization dynamics influence FEP in the MINT and BRICS countries.
Stagnation Detection with Randomized Local Search

Recently a mechanism called stagnation detection was proposed that automatically adjusts the mutation rate of evolutionary algorithms when they encounter local optima. The so-called $SD-(1+1)EA$ introduced by Rajabi and Witt (GECCO 2020) adds stagnation detection to the classical $(1+1)EA$ with standard bit mutation, which flips each bit independently with some mutation rate, and raises the mutation rate when the algorithm is likely to have encountered local optima. In this paper, we investigate stagnation detection in the context of the $k$-bit flip operator of randomized local search that flips $k$ bits chosen uniformly at random and let stagnation detection adjust the parameter $k$. We obtain improved runtime results compared to the $SD-(1+1)EA$ amounting to a speed-up of up to $e=2.71\dots$ Moreover, we propose additional schemes that prevent infinite optimization times even if the algorithm misses a working choice of $k$ due to unlucky events. Finally, we present an example where standard bit mutation still outperforms the local $k$-bit flip with stagnation detection.
Virtual Reality (VR) and Augmented Reality (AR) Technologies for Accessibility and Marketing in the Tourism Industry

The study investigated the roles of virtual reality (VR) and augmented reality (AR) for accessibility and marketing in tourism. Literature is reviewed in this exploratory study and examples of VR and AR for accessibility and marketing in the tourism industry are presented. The review was conducted with two perspectives: accessibility role and marketing role of VR and AR technologies in the tourism industry. As a result of reviewing, these technologies have both strengths and weaknesses for accessibility in tourism. VR has both strengths such as a secure environment, alternative access, and weaknesses such as user neglect, high cost. Similarly, AR has both strengths such as knowledge enrichment, enhancement of experience, and weaknesses such as high cost, lack of security. The high cost of both technologies restricts accessibility and marketing. Also, VR and AR have benefits as reducing strategy costs, attracting tourists with gamification strategies, ease of brand promotion, building brand loyalty, collecting data, and customized product development in tourism marketing.
Awareness, acceptance, and adoption of the national digital contact tracing tool post COVID-19 lockdown among visitors to a public hospital in Singapore

Coronavirus disease 2019 (COVID-19) was first imported into Singapore on 22nd January 2020 by a tourist from Wuhan, China. Since 4th February 2020, local clusters of COVID-19 emerged, rapidly progressing to multiple unlinked clusters over the subsequent months [1]. A partial lockdown—including restrictions on social activities and activities involving close physical proximity (e.g. employment)—was implemented from 7th April to 1st June 2020 to curb local transmissions. These restrictions were gradually lifted as cases of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general community decreased [2].
Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy

Deep Convolutional Neural Networks (DCNNs) have the potential to revolutionize the field of Medical Imaging Diagnostics due to their capabilities of learning by using only raw data. However, DCNNs can only learn when trained using thousands of data points, which is not always available when dealing with medical data. Moreover, due to patient privacy concerns and the small prevalence of certain diseases in the population, medical data often presents unbalanced classes and fewer data points than other data types. Researchers often rely on Generative Adversarial Networks (GANs) to synthesize more data from a given distribution to solve this problem. Nevertheless, GANs are computationally intensive models requiring the use of powerful hardware to run. In the present work, an algorithm for pruning GANs based on Evolution Strategy (ES) and Multi-Criteria Decision Making (MCDM) is proposed in which a model with the best trade-off between computational complexity and synthesis performance can be found without the use of any trade-off parameter. In the proposed algorithm, the model with the best trade-off is defined geometrically as the candidate solution with the minimum Manhattan distance (MMD) in a two-dimensional objective space established by the number of Floating-Point Operations (FLOPs) and the Wasserstein distance of all candidate solutions, also known as the knee solution. The results show that the pruned GAN model achieves similar performance compared with the original model with up to 70% fewer Floating-Point Operations.
Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation

Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs. The SR methods learned on synthetic data do not perform well in real-world, due to the domain gap between the artificially synthesized and real LR images. Some efforts are thus taken to capture real-world image pairs. However, the captured LR-HR image pairs usually suffer from unavoidable misalignment, which hampers the performance of end- to-end learning. Here, focusing on the real-world SR, we ask a different question: since misalignment is unavoidable, can we propose a method that does not need LR-HR image pairing and alignment at all and utilizes real images as they are? Hence we propose a framework to learn SR from an arbitrary set of unpaired LR and HR images and see how far a step can go in such a realistic and “unsupervised” setting. To do so, we firstly train a degradation generation network to generate realistic LR images and, more importantly, to capture their distribution (i.e., learning to zoom out). Instead of assuming the domain gap has been eliminated, we minimize the discrepancy between the generated data and real data while learning a degradation adaptive SR network (i.e., learning to zoom in). The proposed unpaired method achieves state-of- the-art SR results on real-world images, even in the datasets that favour the paired-learning methods more.
Tracking sentiment towards news entities from Arabic news on social media

The tracking sentiment of the news entities over time provides important information to governments and enterprises during the decision-making process. Recently, it has attracted the attention of the research community as well due to its popularity in many applications including; tracking news about elections, e-commerce, and e-governance. However, most of the work is focused on English whereas limited contributions have been done for Arabic. Moreover, there are no annotated corpora in the Arabic news domain that can be used to perform the sentiment tracking task. In this research, we present an Arabic news corpus and its associated sentiment tracking system to monitor the sentiments towards news entities in the Arab world. Sentiment classification and Named Entity Recognition techniques are used to prepare the corpus for the tracking task. A sample dataset containing 7200 tweets was manually annotated to be used in building multiple classifiers and annotate more than 2.3M tweets using the semi-supervised technique. The results of sentiment classification by using different machine learning classifiers and internal testing set show that semi-automatically annotated dataset outperforms the manually annotated dataset by 23% and 16% on two-way and three-way classification respectively using F1-score. The tracking results illustrate that over time the sentiment tracking performs well at discovering the most popular entities, from social media and, tracking their shifts in different Arab regions. It can be used to detect the possible reasons for sentiment change over time and, to predict the future sentiment of the news entities.
Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding

Objective Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU. Methods A machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates. Results The optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all. Conclusions The potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.
Roman Urdu reviews dataset for aspect based opinion mining

Social media, today, demonstrates the rapid growth of modern society as it becomes the main platform for Internet users to communicate and express themselves. People around the world, use a number of devices and resources to access the Internet, set up social networks, conduct online business, e-commerce, e-surveys, etc. Currently, social media is not only a technology that provides information to consumers, it also encourages users to connect and share their views and perspectives. It leads to an increase in inspiration towards Opinion Mining (OM), which is important for both customers and companies in making decisions. Individuals like to see the opinions provided by other customers about a particular product or a service. Companies need to analyze their customer's feedback to strengthen their business decisions. A lot of research has been performed in various languages in the field of Aspect Based OM (ABOM). However, there are still certain languages that need to be explored, such as Roman Urdu (RU). This paper presents a proposed reviews data-set (a RU data-set) of mobile reviews that has been manually annotated with multi-aspect sentiment labels at the sentence-level. It presents base-line results using different Machine Learning (ML) algorithms. The results demonstrate 71% F1-score for aspect detection and 64% for aspect-based polarity.
A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm

This paper presents a local trajectory planning and control method based on the Rapidly-exploring Random Tree algorithm for autonomous racing vehicles. The paper aims to provide an algorithm allowing to compute the planned trajectory in an unknown environment, structured with non-crossable obstacles, such as traffic cones. The investigated method exploits a perception pipeline to sense the surrounding environment by means of a LIDAR-based sensor and a high-performance Graphic Processing Unit. The considered vehicle is a four-wheel drive electric racing prototype, which is modeled as a 3 Degree-of-Freedom bicycle model. A Stanley controller for both lateral and longitudinal vehicle dynamics is designed to perform the path tracking task. The performance of the proposed method is evaluated in simulation using real data recorded by on-board perception sensors. The algorithm can successfully compute a feasible trajectory in different driving scenarios.
Strengthening urban sustainability: Identification and analysis of proactive measures to combat blight

The process of identifying and analyzing proactive measures for the prevention of urban blight is complicated by its complexity and death of data. Blight affects people's lives in many ways, including economically, socially, psychologically, and physically. The phenomenon comprises many factors, including making appropriate identification and eradication decisions that are challenging to analyze and structure. This study took a constructivist stance and sought to develop a new approach to urban blight to complement the existing literature. The methodology combined cognitive mapping techniques applied in decision conferences, and the decision making trial and evaluation laboratory technique based on the multiple-criteria decision analysis approach. This integrated approach facilitated the development of a multi-criteria model that identifies proactive anti-blight measures in a more complete, transparent way. This dual methodology enables incorporating objective and subjective elements, and field specialists' knowledge and experiences, thereby reducing important decision criteria omissions. Additionally, this approach generates a transparent visualization of relationships among criteria. Identified blight determinants can be used to prevent blight, and improve the quality of life in the affected areas. These strategies were carefully analyzed by two independent experts from the Direção Geral do Território (General Directorate of the Territory of Portugal). The advantages and limitations of the proposed methodology are also deliberated.
3D measurement system based on divergent multi-line structured light projection, its accuracy analysis

This paper proposes an effective three-dimensional(3D) measurement system based on divergent multi-line structured light projection, and analyzes the measurement accuracy of the system. In the system, the previous methods have been improved. The accuracy of system calibration and 3D measurement can be improved effectively by changing the calibration board, changing the relative distance between the camera and the laser. Firstly, the camera calibration and the light plane calibration are realized by projecting a circular calibration board and a blank board. Secondly, the Steger centerline extraction algorithm is used to complete the extraction of the centerline of the stripe. Finally, based on the system calibration parameters and the centerline extraction results, the surface 3D measurement of the object is realized, and the camera calibration, light plane calibration and 3D measurement accuracy are analyzed. Experiments show that the calibration method proposed in this paper can obtain higher accuracy than the checkerboard calibration method, which can reduce the camera calibration error by more than 3 times and the light plane calibration error by more than 1 times. In addition, compared with the previous calibration method, the blank board avoids the influence of the calibration pattern on the light stripe. Finally, the relative error of light plane calibration is controlled within 1% by changing the relative distance between the camera and the laser.
Cultural dimensions and skills in the 21<sup>st</sup> century: the Israeli education system as a case study

This study aims to examine how 21st-century skills are implemented in the Israeli education system. For a better, in-depth understanding of the reasons underlying the manner in which said skills are implemented among educators in Israel, Hofstede's cultural dimension theory was applied as an analytical tool. This is a pioneer research and contributes to the scientific literature in the realms of education and multiculturalism. Study findings indicate the importance of understanding the relationships between cultural dimensions and cooperative education, creative teaching, motivation for teaching, teaching empathy, and implementing the multicultural approach in teaching and learning processes. These findings have implications on the development of skills, knowledge, and values required to shape the education system's graduates. These young people will integrate into the global labor market in the future and steer Israeli society towards social change centered on parity and egalitarianism.
A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records

In the last years, the need to de-identify privacy-sensitive information within Electronic Health Records (EHRs) has become increasingly felt and extremely relevant to encourage the sharing and publication of their content in accordance with the restrictions imposed by both national and supranational privacy authorities.In the field of Natural Language Processing (NLP), several deep learning techniques for Named Entity Recognition (NER) have been applied to face this issue, significantly improving the effectiveness in identifying sensitive information in EHRs written in English.However, the lack of data sets in other languages has strongly limited their applicability and performance evaluation.To this aim, a new de-identification data set in Italian has been developed in this work, starting from the 115 COVID-19 EHRs provided by the Italian Society of Radiology (SIRM): 65 were used for training and development, the remaining 50 were used for testing.The data set was labelled following the guidelines of the i2b2 2014 de-identification track.As additional contribution, combined with the best performing Bi-LSTM + CRF sequence labeling architecture, a stacked word representation form, not yet experimented for the Italian clinical de-identification scenario, has been tested, based both on a contextualized linguistic model to manage word polysemy and its morpho-syntactic variations and on sub-word embeddings to better capture latent syntactic and semantic similarities.Finally, other cutting-edge approaches were compared with the proposed model, which achieved the best performance highlighting the goodness of the promoted approach.