Reviewerly/exHarmony huggingface dataset:
Measuring the effectiveness of an automated text messaging active surveillance system for COVID-19 in the south of Ireland, March to April 2020

We report the effectiveness of automated text messaging for active surveillance of asymptomatic close contacts of coronavirus disease (COVID-19) cases in the Cork/Kerry region of Ireland. In the first 7 weeks of the COVID-19 outbreak, 1,336 close contacts received 12,421 automated texts. Overall, 120 contacts (9.0%) reported symptoms which required referral for testing and 35 (2.6%) tested positive for COVID-19. Non-response was high (n = 2,121; 17.1%) and this required substantial clinical and administrative resources for follow-up.
Enhancement of Short Text Clustering by Iterative Classification

Short text clustering is a challenging task due to the lack of signal contained in short texts. In this work, we propose iterative classification as a method to boost the clustering quality of short texts. The idea is to repeatedly reassign (classify) outliers to clusters until the cluster assignment stabilizes. The classifier used in each iteration is trained using the current set of cluster labels of the non-outliers; the input of the first iteration is the output of an arbitrary clustering algorithm. Thus, our method does not require any human-annotated labels for training. Our experimental results show that the proposed clustering enhancement method not only improves the clustering quality of different baseline clustering methods (e.g., k-means, k-means--, and hierarchical clustering) but also outperforms the state-of-the-art short text clustering methods on several short text datasets by a statistically significant margin.
Blockchain – A Most Disruptive Technology On The Spotlight Of World Engineering Education Paradigm

Throughout its history, preferably over the last four centuries, the world witnessing game changing and disruptive technologies on certain regular intervals. Wheels, Compass, calendar and pozzolana are some of the remarkable inventions in before Christ (B.C) period. Clock, printing press, steam engines, vaccines, electricity, mechanical computer, telegraph, iron and steel, aero plane, transistors, ARPANET, personal computer and Internet are few outstanding innovations fuelled our world for last four centuries. If we look at the time line of inventions, it is dramatically reduced over the advancement of time. Inventions took centuries in B.C, but needs only a few years or a decade in A.D. We would like to add the blockchain technology in this line up, which could fuel new era of inventions in new dimensions. This technology relies on decentralized concepts, entirely against the present centralized system, the world relies on. Why we still need a third party book keepers or why we still need an third party intermediary for trust and transactions? This article will answer these arguments. This article begins with the conceptual understanding, then reveals its disruptiveness through several case studies across several industries such as Banking, Administration, Supply chain management, Logistics, Asset management, Intellectual property management, Transport and Energy. Blockchain technology has the potential to redefine every one another technology of human beings. Concept of ledger and its evolution, from its voyage from Indian origin single entry ledger to Satoshi Nakamoto’s triple entry ledger has been highlighted, as these are vital to understand the technology behind bit coin. Without mentioning the crypto currencies, the blockchain use cases will not ended up, hence few excerpts of crypto currencies also added. From cryptography in technology, Anti money laundering, Know your customer, Transaction monitoring in Banking to day to day administration as in the case of land records storage, identity management etc., are few contributions of this article. We also covered the case studies from corporate giants such as Amazon, IBM, MAERSK to the startups like EzyRemit, Signzy etc., and from an individual state of administration Andrapradesh to the entire country, Dubai. It is important for the global engineering education needs to identify and nurture the disruptive technologies, which could contribute to our society in meaningful way. We believe that, blockchain technology belongs to the category of future technologies, which could shape up and fuel the development of global economy for next few decades as internet did for past few decades. Through this article, the young generation engineering pupils, matured practitioners and influential decision makers will understand the potential of blockchain technology and provide impetus to global engineering education.
Discord platform as an online learning environment for emergencies

The study focuses on revealing the features of using the Discord platform as a means of creating a quality remote communication environment during emergency training. The authors present the results of the analysis of scientific developments of scientists in the issues of creation and use of distance learning tools, special attention is given to the problems of distance learning and the structure of its functioning. The authors point out that modern distance learning systems are not well developed for use in emergencies. The main problem is the insufficient level of development in the field of establishing communication channels of quality communication in the conditions of distance education, especially the attention is focused on schools and other institutions of secondary education. To solve this problem, scientists conduct a comparative analysis of distance communication tools, their features and capabilities in terms of the introduction of remote learning communication servers on them and reveals the features of the Bogdan Khmelnytsky Discord platform of Melitopol State Pedagogical University using Discord platform communication environment during distance learning quarantined by the worldwide pandemic of faith in COVID-19.
LMS vs XMSS: Comparison of Stateful Hash-Based Signature Schemes on ARM Cortex-M4

Stateful hash-based signature schemes are among the most efficient approaches for post-quantum signature schemes. Although not suitable for general use, they may be suitable for some use cases on constrained devices. LMS and XMSS are hash-based signature schemes that are conjectured to be quantum secure. In this work, we compared multiple instantiations of both schemes on an ARM Cortex-M4. More precisely, we compared performance, stack consumption, and other figures for key generation, signing and verifying. To achieve this, we evaluated LMS and XMSS using optimised implementations of SHA-256, SHAKE256, Gimli-Hash, and different variants of Keccak. Furthermore, we present slightly optimised implementations of XMSS achieving speedups of up to $$3.11{\times }$$ for key generation, $$3.11{\times }$$ for signing, and $$4.32{\times }$$ for verifying.
Automated CPE Labeling of CVE Summaries with Machine Learning

Open Source Security and Dependency Vulnerability Management (DVM) has become a more vital part of the software security stack in recent years as modern software tend to be more dependent on open source libraries. The largest open source of vulnerabilities is the National Vulnerability Database (NVD), which supplies developers with machine-readable vulnerabilities. However, sometimes Common Vulnerabilities and Exposures (CVE) have not been labeled with a Common Platform Enumeration (CPE) -version, -product and -vendor. This makes it very hard to automatically discover these vulnerabilities from import statements in dependency files. We, therefore, propose an automatic process of matching CVE summaries with CPEs through the machine learning task called Named Entity Recognition (NER). Our proposed model achieves an F-measure of 0.86 with a precision of 0.857 and a recall of 0.865, outperforming previous research for automated CPE-labeling of CVEs.
Perception of Educators towards the Adoption of Education 5.0: A Case of a State University in Zimbabwe

This study was an exploration of educators’ perceptions towards the adoption of Education 5.0 which was conducted at a state University in Zimbabwe. Education 5.0 is a new curriculum reform that would need a buy-in of educators for possible smooth implementation. The study revealed that educators perceived Education 5.0 as a helpful and more beneficial to the education system than the preceding Education 3.0 although they bemoaned lack of infrastructure and financial resources for proper implementation. The study recommended that the University administrators should source funding from business partners and banks to build physical infrastructures such as industrial parks and innovation hubs to support Education 5.0 adoption. The study also recommended that the Ministry of Higher and Tertiary Education, Innovation, Science and Technology Development conduct seminars with educators to address sticky issues regarding Education 5.0, since educators are directly involved in its implementation.
An Outlier Detection Approach on Credit Card Fraud Detection Using Machine Learning: A Comparative Analysis on Supervised and Unsupervised Learning

Credit card fraud is a socially relevant problem that majorly faces a lot of ethical issues and poses a great threat to businesses all around the world. In order to detect fraudulent transactions made by the wrongdoer, machine learning algorithms are applied. The purpose of this paper is to identify the best-suited algorithm which accurately finds out fraud or outliers using supervised and unsupervised machine learning algorithms. The challenge lies in identifying and understanding them accurately. In this paper, an outlier detection approach is put forward to resolve this issue using supervised and unsupervised machine learning algorithms. The effectiveness of four different algorithms, namely local outlier factor, isolation forest, support vector machine, and logistic regression, is measured by obtaining scores of evaluation metrics such as accuracy, precision, recall score, F1-score, support, and confusion matrix along with three different averages such as micro, macro, and weighted averages. The implementation of local outlier factor provides an accuracy of 99.7 and isolation forest provides an accuracy of 99.6 under supervised learning. Similary in unsupervised learning, implementation of support vector machine provides an accuracy of 97.2 and logistic regression provides an accuracy of 99.8. Based on the experimental analysis, both the algorithms used in unsupervised machine learning acquire a high accuracy. An overall good, as well as a balanced performance, is achieved in the evaluation metrics scores of unsupervised learning. Hence, it is concluded that the implementation of unsupervised machine learning algorithms is relatively more suitable for practical applications of fraud and spam identification.
UNIVERSITY PROFESSORS AND THE DIGITALIZATION OF EDUCATION: ON THE THRESHOLD OF FORCE MAJEURE TRANSITION TO STUDYING REMOTELY

In March 2020, Russian higher education was pretty fast to start working remotely due to the threat of the Covid-19 spread. At the same time, discussions around the development of online education inRussiahave been ongoing for several years. Based on the data from semi-structured interviews with teaching staff in leading Russian universities, the paper studies issues related to using digital technologies and professors’ attitude to educational process digitalization. University teachers are shown to consider students and public policy as key drivers of the educational process digitalization. At the same time, the alarmist view on the active spread of digital technologies is still widespread. Professors who participated in the study did not consider different types of remote and online teaching as an equal substitute for traditional offline formats and technologies. This leads to conservative forecasts regarding the future of online and remote teaching. The results seem mostly important, since they present the situation in Russian universities on the eve of the forced transition to remote teaching due to the spread of the Covid-19 virus. These results make it possible to predict the difficulties that universities may face during the transition to emergency remote teaching.
A novel facial image recognition method based on perceptual hash using quintet triple binary pattern

Abstract Image classification (categorization) can be considered as one of the most breathtaking domains of contemporary research. Indeed, people cannot hide their faces and related lineaments since it is highly needed for daily communications. Therefore, face recognition is extensively used in biometric applications for security and personnel attendance control. In this study, a novel face recognition method based on perceptual hash is presented. The proposed perceptual hash is utilized for preprocessing and feature extraction phases. Discrete Wavelet Transform (DWT) and a novel graph based binary pattern, called quintet triple binary pattern (QTBP), are used. Meanwhile, the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms are employed for classification task. The proposed face recognition method is tested on five well-known face datasets: AT&amp;T, Face94, CIE, AR and LFW. Our proposed method achieved 100.0% classification accuracy for the AT&amp;T, Face94 and CIE datasets, 99.4% for AR dataset and 97.1% classification accuracy for the LFW dataset. The time cost of the proposed method is O ( nlogn ). The obtained results and comparisons distinctly indicate that our proposed has a very good classification capability with short execution time.
A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Abstract Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people with speech impairment to communication with normal people. This framework which is called ASLNN proposes a new hand posture recognition technique for the American sign language alphabet based on the neural network which works on the geometrical feature extraction of hands. A user’s hand is captured by a three-dimensional depth-based sensor camera; consequently, the hand is segmented according to the depth analysis features. The proposed system is called depth-based geometrical sign language recognition as named DGSLR. The DGSLR adopted in easier hand segmentation approach, which is further used in segmentation applications. The proposed geometrical feature extraction framework improves the accuracy of recognition due to unchangeable features against hand orientation compared to discrete cosine transform and moment invariant. The findings of the iterations demonstrate the combination of the extracted features resulted to improved accuracy rates. Then, an artificial neural network is used to drive desired outcomes. ASLNN is proficient to hand posture recognition and provides accuracy up to 96.78% which will be discussed on the additional paper of this authors in this journal.
Syntactic Edge-Enhanced Graph Convolutional Networks for Aspect-Level Sentiment Classification With Interactive Attention

Aspect-level sentiment classification is a hot research topic in natural language processing (NLP). One of the key challenges is that how to develop effective algorithms to model the relationships between aspects and opinion words appeared in a sentence. Among the various methods proposed in the literature, the graph convolutional networks (GCNs) achieve the promising results due to their good ability to capture the long distance between the aspects and the opinion words. However, the existing methods cannot effectively leverage the edge information of dependency parsing tree, resulting in the sub-optimal results. In this article, we propose a syntactic edge-enhanced graph convolutional network (ASEGCN) for aspect-level sentiment classification with interactive attention. Our proposed method can effectively learn better representations of aspects and the opinion words by considering the different types of neighborhoods with the edge constraint. To evaluate the effectiveness of our proposed method, we conduct the experiments on five standard sentiment classification results. Our results demonstrate that our proposed method obtains the better performance than the state-of-the-art models on four datasets, and achieves a comparative performance on Rest16.
Visualizing molecular structures and shapes: a comparison of virtual reality, computer simulation, and traditional modeling

Abstract The purpose of this study was to compare the effectiveness of three methods used to assist in teaching molecular geometry to college chemistry students. A pre- and post-test quasi-experiment was used to collect data about students’ performance in a given chemistry exercise. One research question was intended to evaluate and compare the effectiveness of the three methods in assisting students to understand the topic and carry out the exercise correctly, and a second research question addressed students’ attitudes towards the use of Virtual Reality (VR) in chemistry education. Results show a positive attitude towards the use of VR as an assisting tool to aid in understanding chemistry concepts. While the difference among the three methods was not significant, the results show that the VR brought more enthusiasm and positive attitudes toward the topic of molecular geometry among the students. Educational implications and recommendations for future research are presented as well.
Anomaly detection in facial skin temperature using variational autoencoder

Abstract Facial skin temperature is a physiological index that varies with skin blood flow controlled by autonomic nervous system activity. The facial skin temperature can be remotely measured using infrared thermography, and it has recently attracted attention as a remote biomarker. For example, studies have been reported to estimate human emotions, drowsiness, and mental stress on facial skin temperature. However, it is impossible to make a machine that can discriminate all infinite physiological and psychological states. Considering the practicality of skin temperature, a machine that can determine the normal state of facial skin temperature may be sufficient. In this study, we propose a completely new approach to incorporate the concept of anomaly detection into the analysis of physiological and psychological states by facial skin temperature. In this paper, the method for separating normal and anomaly facial thermal images using an anomaly detection model was investigated to evaluate the applicability of variational autoencoder (VAE) to facial thermal images.
Exploiting complex medical data with interpretable deep learning for adverse drug event prediction

A variety of deep learning architectures have been developed for the goal of predictive modelling and knowledge extraction from medical records. Several models have placed strong emphasis on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability. In this study we utilise such models with a large Electronic Patient Record (EPR) data set consisting of diagnoses, medication, and clinical text data for the purpose of adverse drug event (ADE) prediction. The first contribution of this work is an empirical evaluation of two state-of-the-art medical-code based models in terms of objective performance metrics for ADE prediction on diagnosis and medication data. Secondly, as an extension of previous work, we augment an interpretable deep learning architecture to permit numerical risk and clinical text features and demonstrate how this approach yields improved predictive performance compared to the other baselines. Finally, we assess the importance of attention mechanisms in regards to their usefulness for medical code-level and text-level interpretability, which may facilitate novel insights pertaining to the nature of ADE occurrence within the health care domain.
Adaptive Debanding Filter

Banding artifacts, which manifest as staircase-like color bands on pictures or video frames, is a common distortion caused by compression of low-textured smooth regions. These false contours can be very noticeable even on high-quality videos, especially when displayed on high-definition screens. Yet, relatively little attention has been applied to this problem. Here we consider banding artifact removal as a visual enhancement problem, and accordingly, we solve it by applying a form of content-adaptive smoothing filtering followed by dithered quantization, as a post-processing module. The proposed debanding filter is able to adaptively smooth banded regions while preserving image edges and details, yielding perceptually enhanced gradient rendering with limited bit-depths. Experimental results show that our proposed debanding filter outperforms state-of-the-art false contour removing algorithms both visually and quantitatively.
Coupled conditional backward sampling particle filter

The conditional particle filter (CPF) is a promising algorithm for general hidden Markov model smoothing.Empirical evidence suggests that the variant of CPF with backward sampling (CBPF) performs well even with long time series.Previous theoretical results have not been able to demonstrate the improvement brought by backward sampling, whereas we provide rates showing that CBPF can remain effective with a fixed number of particles independent of the time horizon.Our result is based on analysis of a new coupling of two CBPFs, the coupled conditional backward sampling particle filter (CCBPF).We show that CCBPF has good stability properties in the sense that with fixed number of particles, the coupling time in terms of iterations increases only linearly with respect to the time horizon under a general (strong mixing) condition.The CCBPF is useful not only as a theoretical tool, but also as a practical method that allows for unbiased estimation of smoothing expectations, following the recent developments by Jacob et al. (to appear).Unbiased estimation has many advantages, such as enabling the construction of asymptotically exact confidence intervals and straightforward parallelisation.
Evaluation of cloud computing resource scheduling based on improved optimization algorithm

Abstract Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced.