Reviewerly/exHarmony huggingface dataset:
MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record

Electronic Health Records (EHRs) contain rich clinical data collected at the point of the care, and their increasing adoption offers exciting opportunities for clinical informatics, disease risk prediction, and personalized treatment recommendation. However, effective use of EHR data for research and clinical decision support is often hampered by a lack of reliable disease labels. To compile gold-standard labels, researchers often rely on clinical experts to develop rule-based phenotyping algorithms from billing codes and other surrogate features. This process is tedious and error-prone due to recall and observer biases in how codes and measures are selected, and some phenotypes are incompletely captured by a handful of surrogate features. To address this challenge, we present a novel automatic phenotyping model called MixEHR-Guided (MixEHR-G), a multimodal hierarchical Bayesian topic model that efficiently models the EHR generative process by identifying latent phenotype structure in the data. Unlike existing topic modeling algorithms wherein the inferred topics are not identifiable, MixEHR-G uses prior information from informative surrogate features to align topics with known phenotypes. We applied MixEHR-G to an openly-available EHR dataset of 38,597 intensive care patients (MIMIC-III) in Boston, USA and to administrative claims data for a population-based cohort (PopHR) of 1.3 million people in Quebec, Canada. Qualitatively, we demonstrate that MixEHR-G learns interpretable phenotypes and yields meaningful insights about phenotype similarities, comorbidities, and epidemiological associations. Quantitatively, MixEHR-G outperforms existing unsupervised phenotyping methods on a phenotype label annotation task, and it can accurately estimate relative phenotype prevalence functions without gold-standard phenotype information. Altogether, MixEHR-G is an important step towards building an interpretable and automated phenotyping system using EHR data.
Digitalization of the educational process for the training of the pre-service teachers

According to the Development Concept of the Digital Economy and Society in Ukraine, the priority of this area is to develop a substantial national policy on digitalization of education, as this is the key part of the education reform in Ukraine. For this reason, universities should firstly take into account the particularities of teaching the current generation of students and the needs of the digital society as a whole. This paper considers the process of transition from informatization to digitalization in society, implementation of digital support for the educational process in the university, development of the digital educational environment for the training university teachers, and proposes the digital tools for such an environment. The authors propose several ways to improve the development level of digitalization of the educational environment in the university. This is to take into account the needs of the digital society and the modern generation of students, provide a high level of the digital literacy formation of university graduates and support the development of a new digital security system of the modern university. Aiming to design the digital educational environment for increasing the of educators’ digital literacy level, the authors propose to develop and implement the following computer, multimedia and computer-based learning tools and equipment, which includes blended and distance learning classes, cloud technologies, tools of virtual and augmented reality, tools for gamification of the educational process, educational robotics, tools for learning 3D technologies, MOOCs.
Deep transfer learning with metric structure for fault diagnosis

Maximum Mean Discrepancy (MMD) is a classical method of measuring distribution distance and is widely used in Deep Transfer Learning (DTL) for fault diagnosis. However, in practice, MMD neglects the label information and the spatial structure relationship of the data and performs unsatisfied. To address this issue, a novel DTL fault diagnosis method with metric structure is proposed in this paper. Besides MMD, a novel domain adaptation module is designed through learning a common metric matrix from the Two Transferred Domains (TTD). Thereby, it can constrain TTD in the same geometric structure and make the distribution of TTD similar by both the mean discrepancy and the metric structure. Furthermore, a novel soft pseudo-label trick is proposed for dividing similar and dissimilar sample pairs of the unlabeled samples in the target domain. Experimental results have demonstrated that this method has well diagnostic performance in multiple transfer tasks.
Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People

Social networks are an essential component of the Internet of People (IoP) and play an important role in stimulating interactive communication among people. Graph convolutional networks provide methods for social network analysis with its impressive performance in semi-supervised node classification. However, the existing methods are based on the assumption of balanced data distribution and ignore the imbalanced problem of social networks. In order to extract the valuable information from imbalanced data for decision making, a novel method named minority-weighted graph neural network (mGNN) is presented in this article. It extends imbalanced classification ideas in the traditional machine learning field to graph-structured data to improve the classification performance of graph neural networks. In a node feature aggregation stage, the node membership values among nodes are calculated for minority nodes’ feature aggregation enhancement. In an oversampling stage, the cost-sensitive learning is used to improve edge prediction results of synthetic minority nodes, and further raise their importance. In addition, a Gumbel distribution is adopted as an activation function. The proposed mGNN is evaluated on six social network data sets. Experimental results show that it yields promising results for imbalanced node classification.
The next generation of soil and water bodies heavy metals prediction and detection: New expert system based Edge Cloud Server and Federated Learning technology

Heavy metals (HMs) in soil and water bodies greatly threaten human health. The wide separation of HMs urges the necessity to develop an expert system for HMs prediction and detection. In the current perspective, several propositions are discussed to design an innovative intelligence system for HMs prediction and detection in soil and water bodies. The intelligence system incorporates the Edge Cloud Server (ECS) data center, an innovative deep learning predictive model and the Federated Learning (FL) technology. The ECS data center is based on satellite sensing sources under human expertise ruling and HMs in-situ measurement. The FL system comprises a machine learning (ML) technique that trains an algorithm across multiple decentralized edge servers holding local data samples without exchanging them or breaching data privacy. The expected outcomes of the intelligence system are to quantify the soil and water bodies' HMs, develop new modified HMs pollution contamination indices and provide decision-makers and environmental experts with an appropriate vision of soil, surface water, and crop health.
High-quality visually secure image cryptosystem using improved Chebyshev map and 2D compressive sensing model

In this paper, we propose an improved Chebyshev map and a visually meaningful image encryption (VMIE) algorithm based on two-dimensional compressive sensing (2DCS). First, the adoption of 2DCS compresses the plain image, wherein the measurement matrix is processed by the simulated annealing algorithm to improve the quality of the reconstructed image. Second, a diffusion operation is performed on the sample data. Furthermore, a matrix encoding matrix is employed to insert the cryptographic image into the host image to obtain a visually meaningful steganographic image. The experimental results and comprehensive analyses show that the proposed scheme has feasible performance and stands up to various attacks. Eventually, the comparison with existing related schemes demonstrates that the proposed image encryption system has the advantages of visual security and reconstruction quality.
Object Tracking in Satellite Videos: Correlation Particle Filter Tracking Method With Motion Estimation by Kalman Filter

Object tracking in satellite videos faces various challenges such as target occlusion, target rotation, and background clutter. This study proposes a correlation particle filter algorithm with motion estimation for object tracking in satellite videos. The tracker, called CPKF, combines the strengths of the correlation, particle, and Kalman filters. Compared with existing tracking methods based on correlation filters, the proposed tracker has three major advantages: (1) Particle sampling, and motion estimation build robustness against partial and complete occlusion. (2) Color histogram model makes it robust to target rotation. (3) Fusion of multiple feature response maps effectively handle background clutter and low contrast. The experimental results demonstrate that the proposed tracking algorithm performs better than state-of-the-art methods.
Employees’ perception of robots and robot-induced unemployment in hospitality industry under COVID-19 pandemic

The impact of the pandemic is driving the recent upsurge in service automation and the adoption of service robots in the hospitality industry. As service paradigm and customer expectations shift from conventional customized and personalized services towards a digitalized service environment, such customer orientation may favor using service robots at scales that could render service employees redundant. This study aims to answer the above question by investigating service employees’ perceptions of service robots. Data solicited from 405 service employees in the United States of America via Amazon’s MTurk were analyzed using structural equation modeling. The result revealed that employees’ awareness of adopting and using service robots significantly impacts their perception of robot-induced unemployment. Further, results indicated that the perception of robots’ social skills significantly influences service employees’ perception of robot-induced unemployment. Employee status was found to moderate the relationships mentioned above. Specifically, entry-level employees perceive the unemployment risk more than managers.
Hybrid quantum-classical reservoir computing of thermal convection flow

We simulate the nonlinear chaotic dynamics of Lorenz-type models for a classical two-dimensional thermal convection flow with three and eight degrees of freedom by a hybrid quantum-classical reservoir computing model. The high-dimensional quantum reservoir dynamics are established by universal quantum gates that rotate and entangle the individual qubits of the tensor product quantum state. A comparison of the quantum reservoir computing model with its classical counterpart shows that the same prediction and reconstruction capabilities of classical reservoirs with thousands of perceptrons can be obtained by a few strongly entangled qubits. We demonstrate that the mean squared error between model output and ground truth in the test phase of the quantum reservoir computing algorithm increases when the reservoir is decomposed into separable subsets of qubits. Furthermore, the quantum reservoir computing model is implemented on a real noisy IBM quantum computer for up to seven qubits. Our work thus opens the door to model the dynamics of classical complex systems in a high-dimensional phase space effectively with an algorithm that requires a small number of qubits.
TaLWaR: Blockchain-Based Trust Management Scheme for Smart Enterprises With Augmented Intelligence

In recent years, the Internet of Things (IoT) and enterprise management systems (EMS) have been rapidly growing and applied in advanced Industries. It provides better big data analytics and the most promising computing platforms. Moreover, IoT is transforming into the augmented intelligence of things (AIoT), developing a human-oriented paradigm for enterprises with AI. Still, smart enterprises and industries have additional requirements, such as device and data trust, robust decision-making, communication latency, and secure data storage. However, previous emerging paradigms and approaches did not fully address all of the aforementioned requirements. Therefore, this article proposes a blockchain-based trust management scheme for smart enterprises with augmented intelligence. The blockchain-based device trust authentication mechanism is used at the device connection layer for device authentication in clusters of IoT devices (smart enterprises branch-SEB). Furthermore, the blockchain-based augmented intelligence enabled approach is leveraged for data authentication at the authentication layer. Finally, smart enterprise data are stored in the distributed hash table (DHTs) and decentralized cloud layer with distributed hash table. We evaluated the proposed scheme using qualitative and quantitative analysis and compared it to the existing studies, showing better performance as 40.887-ms computational cost and 1872-bits transactional cost.
Sentiment Analysis on Online Learning During the Covid-19 Pandemic Based on Opinions on Twitter using KNN Method

Coronavirus Disease of 2019 began in Wuhan in December 2019 and it was declared as a global pandemic by WHO. Until January 2021, it affected all of human activities on earth i.e., experiencing many obstacles from restrictions on activities, closure of tourist attractions to restrictions on face-to-face learning activities in schools or universities. Due to the policy of providing a broad influence on the community with various comments through social media, many twitter users make tweets containing positive and negative comments leading to statements about online learning or daring. The problem is that they contain so many different words, abbreviations, informal language, and symbols, creating difficulties to choose which words or groups of words that can produce positive or negative statements. K-Nearest Neighbors algorithm is used to classify positive and negative tweet data, the results were AUC for class 0: 0.754, 1: 0.635, 2: 0.721 and with a precision classification score of 0.86, recall is 0.85 so that the results of the classification of negative and positive sentences on the online learning tweet data were ROC-AUC of 0. 853 and the accuracy value of 0.885.
An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems

To solve complex high-dimensional optimization problems, an opposition-based butterfly optimization algorithm with adaptive elite mutation (OBOAEM) is proposed. In the initial stage, the opposition-based learning mechanism is introduced to increase the diversity of the initial population and improve the probability of finding the optimal value. In order to balance the process of global search and local search, the segmental adjustment factor is used to improve the optimization accuracy of the algorithm. In the final stage of the algorithm, the elite mutation strategy is adopted to prevent precocity of the algorithm. In this paper, 23 benchmark functions are selected to test OBOAEM and original algorithm BOA in low dimension, and 16 benchmark functions are introduced to test OBOAEM and eight intelligent optimization algorithms in high dimensions 100, 500 and 1000. In addition, the simulation experiments of 30 CEC2014 complex deformation functions reveal the OBOAEM has a good effect in solving complex optimization problems, which are compared with six state-of-art algorithms. Friedman test is used forstatistical analysis, showing that OBOAEM has better optimization performance for complex high-dimensional problems. Finally, OBOAEM is applied to engineering design problems, and it is proved that OBOAEM is competitive in solving real-world problems.
Arabic Automatic Speech Recognition: A Systematic Literature Review

Automatic Speech Recognition (ASR), also known as Speech-To-Text (STT) or computer speech recognition, has been an active field of research recently. This study aims to chart this field by performing a Systematic Literature Review (SLR) to give insight into the ASR studies proposed, especially for the Arabic language. The purpose is to highlight the trends of research about Arabic ASR and guide researchers with the most significant studies published over ten years from 2011 to 2021. This SLR attempts to tackle seven specific research questions related to the toolkits used for developing and evaluating Arabic ASR, the supported type of the Arabic language, the used feature extraction/classification techniques, the type of speech recognition, the performance of Arabic ASR, the existing gaps facing researchers, along with some future research. Across five databases, 38 studies met our defined inclusion criteria. Our results showed different open-source toolkits to support Arabic speech recognition. The most prominent ones were KALDI, HTK, then CMU Sphinx toolkits. A total of 89.47% of the retained studies cover modern standard Arabic, whereas 26.32% of them were dedicated to different dialects of Arabic. MFCC and HMM were presented as the most used feature extraction and classification techniques, respectively: 63% of the papers were based on MFCC and 21% were based on HMM. The review also shows that the performance of Arabic ASR systems depends mainly on different criteria related to the availability of resources, the techniques used for acoustic modeling, and the used datasets.
Work-in-progress—The Effect of Students’ Perceptions on Intention to use Metaverse Learning Environment in Higher Education

The purpose of this work-in-progress study is to identify the predictiveness of perceived ease to use(PEU), perceived usefulness (PU), perceived enjoyment (PE), and frequency of experience (FE) on the intention to use (IU) a metaverse-based learning environment. A total of 226 undergraduate students participated in the metaverse-based learning using V-story (VirBELA) for a one-semester. Multiple regression was used for data analysis. As a result, PU, PE, and PEU predicted IU in the metaverse-based learning environment. This study empirically analyzed the acceptance factors of learners in a metaverse-based learning environment and is meant as basic data for metaverse research in a learning environment.
Modeling Threats to AI-ML Systems Using STRIDE

The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. However, ML models exhibit specific vulnerabilities that conventional IT systems are not subject to. As systems incorporating ML components become increasingly pervasive, the need to provide security practitioners with threat modeling tailored to the specific AI-ML pipeline is of paramount importance. Currently, there exist no well-established approach accounting for the entire ML life-cycle in the identification and analysis of threats targeting ML techniques. In this paper, we propose an asset-centered methodology-STRIDE-AI-for assessing the security of AI-ML-based systems. We discuss how to apply the FMEA process to identify how assets generated and used at different stages of the ML life-cycle may fail. By adapting Microsoft's STRIDE approach to the AI-ML domain, we map potential ML failure modes to threats and security properties these threats may endanger. The proposed methodology can assist ML practitioners in choosing the most effective security controls to protect ML assets. We illustrate STRIDE-AI with the help of a real-world use case selected from the TOREADOR H2020 project.
Construction information authentication and integrity using blockchain-oriented watermarking techniques

Information authentication and integrity (IAI) plays a critical role in construction digital transformation. Blockchain technology can prevent information from fabrication and falsification but cannot guarantee IAI before it enters the on-chain world. To cover the void, this paper describes a blockchain-oriented deployment framework to secure IAI for construction by formulating watermarks from the 5W1H (Who?, What?, Where?, Where?, Why?, How?) and hiding them in digital content. It does so by conducting a literature review and industrial stakeholder engagements, based on which the design requirements are derived and a framework with seven modules is further developed. By deploying it in a modular construction project, the framework demonstrates its capability to guide the formation and embedding of watermarks, accurately detect and localize the integrity damage, and recover the full authentication data even after some degree of tampering. Future studies are encouraged to deploy the framework with various blockchain systems in construction applications. • A blockchain-oriented framework to secure authentication and integrity for construction. • Watermark formulation from the 5W1H (Who?, What?, Where?, Where?, Why?, How?). • Seven devised modules within the framework to hide the watermark in digital content. • Accurate detection and localization of the integrity damage. • Robust recovering of the full authentication data.
An Improved Grey Wolf Optimization with Multi-Strategy Ensemble for Robot Path Planning

Grey wolf optimization (GWO) is a meta-heuristic algorithm inspired by the hierarchy and hunting behavior of grey wolves. GWO has the superiorities of simpler concept and fewer adjustment parameters, and has been widely used in different fields. However, there are some disadvantages in avoiding prematurity and falling into local optimum. This paper presents an improved grey wolf optimization (IGWO) to ameliorate these drawbacks. Firstly, a modified position update mechanism for pursuing high quality solutions is developed. By designing an ameliorative position update formula, a proper balance between the exploration and exploitation is achieved. Moreover, the leadership hierarchy is strengthened by proposing adaptive weights of α, β and δ. Then, a dynamic local optimum escape strategy is proposed to reinforce the ability of the algorithm to escape from the local stagnations. Finally, some individuals are repositioned with the aid of the positions of the leaders. These individuals are pulled to new positions near the leaders, helping to accelerate the convergence of the algorithm. To verify the effectiveness of IGWO, a series of contrast experiments are conducted. On the one hand, IGWO is compared with some state-of-the-art GWO variants and several promising meta-heuristic algorithms on 20 benchmark functions. Experimental results indicate that IGWO performs better than other competitors. On the other hand, the applicability of IGWO is verified by a robot global path planning problem, and simulation results demonstrate that IGWO can plan shorter and safer paths. Therefore, IGWO is successfully applied to the path planning as a new method.