Reviewerly/exHarmony huggingface dataset:
Logics for Software Engineering

Logic is the basis for almost all formal methods in computer science. In this chapter, we introduce some of the most commonly used logics by examples. It serves as a reference for subsequent chapters. We start with propositional logic, introduce its syntax, semantics, and calculus. Then we extend our view of propositional logic as a so-called institution, discuss model transformations and modular specifications. Subsequently, we turn to first- and second-order logic and show how these can be obtained as natural extensions of propositional logic. Finally, we discuss non-classical logics: multimodal and deontic logics to deal with alternative and subjective viewpoints, respectively, and dynamic and temporal logics for reasoning about time. The chapter concludes with an elaborate example of how to transform an informal natural language description into a formal specification in linear temporal logic.
Threatening Practical Security of Discretely Modulated Continuous-Variable Quantum Key Distribution with Saturation Attack Over Atmospheric Channel

Exploiting imperfections of practical devices in continuous variable quantum key distribution (CVQKD), an eavesdropper can steal the information between legal participants by performing the potential saturation-induced attacks in homodyne detection. In this paper, we demonstrate the feasibility of the potential saturation attack in discretely modulated (DM) CVQKD over atmospheric turbulence. Subsequently,Â a countermeasure is suggested by using an adjustable optical filter (AOF) to attenuate the estimated parameter with the measured value in finite linearity domain. Moreover, we compensate the attenuated data. Numerical simulations illustrate the effects of the saturation attack on the estimated excess noise and the secret key rate of the CVQKD system. We find that legal participants can evaluate the information that has been eavesdropped because the secret key rate is negative due to the induced excess noise evaluated more than one.
Geographic Information Systems for Transportation

Modern transportation is highly dependent on geospatial information, and the functionality based on the information is so intimately interwoven with our daily lives that its significance is often overlooked until it fails in some way. The use of geospatial information for transportation proceeds from traditional GIS for transport planning and administration of transport networks to the provision of location-based services (LBS) and new applications in intelligent transport systems (ITS), including systems for advanced driver assistance (ADAS) and autonomous driving. This chapter describes the concepts for the use of geospatial information in transportation, starting with the legal framework established by the European Commission to ensure the provision of geospatial information related to transport. The framework is based on European legislations that set out clear obligations for data owners providing information to the public. Furthermore, the chapter describes the theory of navigable digital transport network models, based on the graph theory, and gives examples of databases and services with geospatial information from transport authorities, open data sources and commercial map providers. Location referencing methods used for transportation and standards for information exchange are also described. The chapter concludes that the legislations and the datasets and standards available lay a foundation for the further use of geospatial information for transportation and that this use is expected to grow.
WeCheat: Algorithm for e-Learning Smart Cheating Detection Using Mean-Shift Clustering

During the COVID-19 pandemic, most people around the world started using internet and online technologies for multiple purposes. A very common digital transformation is online learning or e-learning. Despite its importance, online learning has several issues compared to standard learning. One of the most issues of concern is cheating between students during online exams. This paper aims to address cheating problem by proposing a novel method called WeCheat. It automates the cheating detection process on e-learning platforms, such as Google Forms. The target for the detection of cheating-groups are online exams. To the best of our knowledge, this is the first time someone proposes such a system that detects cheating, particularly during the COVID-19 pandemic. To comply with the set goal, a clustering-based solution is proposed. This Method employs Mean-Shift, a non-parametric and density-based clustering algorithm for this task. Validation of this approach is evidenced by the application which performs excellently on an arbitrary number of features in the cheating detection problem in terms of accuracy. This application helps educational institutions to address cheating cases and at the same time offers the opportunity to focus more on the use of anti-cheating logistics.
Within – Country Diversity and Economic Performance: The Role of Financial Technology

The ongoing debate regarding the significance of religion and language on economic performance (GDP) is inconclusive and non-exhaustive. We present a non-parametric partial least square – structural equation modeling (PLS-SEM) analysis of within-country diversity and GDP. With a sample of 46 countries for the period 2004-2019, we provide evidence on (1) within-country diversity and GDP, (2) the direct and indirect effect of financial technology on GDP, and (3) the existence of a non-linear relationship between financial development (FINDX) and GDP. Our evidence shows that religious (linguistic) diversity increases (decreases) GDP. We also show that ATM per 100,000 adults (ATM_ADUL) intensifies the positive (negative) effect of religious (linguistic) diversity on GDP. Moreover, our evidence suggests that ATM_ADUL has a non-linear effect on FINDX. Additional evidence shows that FINDX also has a non-linear effect on GDP. Taken together, our findings show that ATM_ADUL moderates the effect of within-country diversity and has both direct and indirect effects (through FINDX) on GDP.
Generative Adversarial Networks for Noise Removal in Plain Carbon Steel Microstructure Images

Thermo-mechanical treatments are employed to bring about variety in the quality of metals. These treatments only work when carried out in accordance with appropriate schedules, so as to enable the evolution of metal microstructures in a suitable way. Recently, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computer-based simulations</i> of these treatments have become hugely desired in the metallurgy industry, due to their time and resource efficiency, and also because they are free from manual experimentation errors. However, such simulations are realizable only with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">digital microstructure images</i> , accessible in proper digitized forms. Taking that into consideration, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generative adversarial network</i> architecture for denoising steel microstructure images. Experimental results demonstrate the efficacy of the proposed model in comparison to the contemporary state-of-the-art techniques.
Compactness and Connectedness

This chapter is devoted to address the concepts of compactness and connectedness in topological settings, which first arose through the study of subsets of the Euclidean line $$ \mathbf {R}$$ in calculus and mathematical analysis.
Graph-Filtering and High-Order Bipartite Graph Based Multiview Graph Clustering

Multiview clustering, which partitions data into different groups, has attracted wide attention. With increasing data, bipartite graph-based multiview clustering has become an important topic since it can achieve efficient clustering by establishing relationship between data points and anchor points instead of all samples. Current most methods learn graph structure using one-order bipartite graph from original multi-view feature. However, original data inevitably contain noise feature and its structure is complex. To address this issue, we propose a novel graph filtering and high-order bipartite graph-based multiview graph clustering method, which consider the influence of noise feature and complex graph structure relationship. Concretely, we first employ graph filtering to original data feature space, then adopt a two-step random walk approach to establish the bipartite graph structural relationships of each view. At last,Â based on constructed high-order bipartite graph, we propose a self-weight bipartite graph-based multiview graph fusion framework, which reduces annoying weight parameter selection and obtains a joint bipartite graph. Experimental results on several benchmark datasets demonstrate that our method achieves better clustering performance than state-of-the-art multiview clustering methods.
Application and Challenge of Blockchain Technology in Medical Field

Due to its unique security, blockchain technology is widely used in the financial field.Under the background of the rapid development of information technology and the rapid improvement of medical level, it is also a general trend to integrate blockchain technology into the medical field.According to the characteristics of blockchain and the research contents of many scholars on the application of blockchain in the medical field, this paper analyzes and summarizes the problems existing in the current development of blockchain, puts forward corresponding solutions, and looks forward to the further application of blockchain technology in the medical field.
Electrical network equations

The analysis of electrical circuits is based on the application of two essential laws of physics originally proposed by the German physicist Gustav Kirchhoff in 1845. These laws, which can be applied in both the time and frequency domains, may be considered as particular cases of Maxwell equations (which would be proposed 20 years later). Both laws are described in the following considering only their formulation in the time domain (nevertheless, they can be applied in the same form to phasor analysis at any given frequency). Beforehand, some basic definitions related with circuit topology are introduced.
Activities Classification Based on IMU Signals

This article presents method for recognizing activity from data acquired from the accelerator, magnetometer, gyroscope and motion sensors. The experiments providing data were conducted in July 2021 in Katowice, Poland, as a part of the System for Monitoring Activity and Training Rationalization (SMART) project, financed by the polish National Centre for Research and Development. A variety of classifiers were tested in two approaches – using all available variables and using features selected with the Joint Mutual Information method. Separate models were built for each activity as well as models for selecting one activity out of 8 possible. The best obtained results exceeded 98% for the multi-activity classifier and 99% for each individual activity classifier, which legitimizes their application for commercialization.
Target-specific sentiment analysis method combining word-masking data enhancement and adversarial learning

Abstract Target-specific sentiment analysis is an emerging topic in the field of text mining but current approaches to deriving the polarity of a sentence suffer from two main drawbacks: on one hand, we lack of a large and well-curated corpus, and on the other hand, current solutions based on deep learning are particularly vulnerable to the attack of adversarial samples. A novel target-specific sentiment classification method is proposed. Firstly, the method of masking target entities is applied to replace synonyms and insert words randomly; secondly, the target-specific sentiment classification model of adversarial learning is constructed with six baseline models; finally, we combine data enhancement and adversarial learning to construct target-specific sentiment classification model. Experimental results show that Macro-F1 values are improved by 0.30–2.91, 0.88–2.42 and 0.13–1.94% compared to the six baseline models by using Laptop14, Restaurant14 and Twitter original datasets, respectively, using Adversarial learning. Using word-masking data enhancement samples and Adversarial learning from Laptop14, Restaurant14 and Twitter shows that Macro-F1 values are improved by 0.9–2.64, 1.59–3.09 and 0.18–1.71% compared to the six baseline (SC), respectively. Our method can effectively improve the quality of samples, it improves the classification performance and the capability of adversarial samples defense.
Experimental Study On Virtual-Reality Based Retail Mall Called “V-Mart”

V-Mart is a virtual reality-based smartphone application that will allow users to experience the immersive-ness and lifelike quality of a typical brick-and-mortar shop with a few added advantages of an e-commerce website, employing low-cost devices with limited interaction capabilities In this article, we provide some of our findings for developing and constructing a VR-based retail mall, as well as outline the approach utilized to create V-Mart. With the available technology and our V-Mart prototype implementation experience, we believe that 3D retail apps are possible.
Innovative Interaction Software for Remote Assistance Application

Technology is growing expeditiously and the demand for new innovative software and devices is encouraging to design and develop new software to cater to the needs of next-generation interactions. Augmented Reality(AR) is considered to be the most immersive way of interaction in the enterprise and education sectors. Traditional meeting platforms do not support immersive AR meetings. To stimulate immersive technology of interactive applications, a software application is designed and developed for conducting a remote meeting through AR glasses and laptop or desktop computers. The aim of this paper is to introduce utility-based immersive software “Remote Assistance” (RA) and the results of conducting various performance tests on the immersive software to validate the RA application. The RA application is an inclusive online meeting platform that connects the meeting participants remotely and facilitates the hands-free streaming of critical operations, screenshot sharing and annotations, loading, and interaction of 3D models by all meeting participants. RA facilitates technology-enhanced training and learning activities. The immersive software is checked for the stability and the non-functional requirements for conducting the online meetings through AR glasses and different laptop/desktop computers. The results of the nonfunctional test have confirmed the AR online meeting, a reliable platform where multiple users can join meeting rooms and successfully access the RA application.
The Effects of Tablet Use on Student Learning Achievements, Participation, and Motivation at Different Levels

The incorporation of tablets into education has become widespread in recent years. Their use enables learners to gain access to a plethora of learning resources. Nevertheless, reasonably little has been done to systematically review the effects of tablet-assisted learning on student learning achievements, participation, and motivation. In this study, both the positive and negative effects of tablet-assisted learning were explored. In addition, the motivational elements of tablet-assisted learning were discussed, coupled with an analysis of its effects on motivation and gender differences in tablet use. The roles of tablet use in learner participation and ways to enhance learner participation were also studied. Additionally, we discussed the impacts of tablet-assisted learning at different grade levels and provided constructive suggestions for technologists and educators. Further research should carry out empirical investigations with large samples or by combining tablets with gamification to identify more factors affecting tablet use and address the existing problems.
Digital Competence of Higher Education Learners in the Context of COVID-19 Triggered Online Learning

The spread of COVID-19 caused wide scale disruptions in the educational sector across the globe. Digital education involving use of digital tools, virtual platforms and online learning is viewed as one of the viable alternatives to continue the academic activities in such a backdrop. Higher education institutions have largely switched to this new mode of learning and are still continuing with the digital mode in many parts of the world, owing to the enduring pandemic threat. However, competence of the learners to engage effectively in the online classes and the influence of their socio-economic background on this competence were not given adequate consideration. The present study was an effort to glean into these aspects as they are crucial to the success of digital education. The study was conducted 833 students comprising of graduate, post graduate and Ph.D. students of an Agricultural University to assess their digital competencies and factors affecting an effective participation in online classes. The Digital Competence Framework 2.0 of EU Science Hub (DIGCOMP) was adapted and used for this study. Our findings suggest that the learners have a satisfactory level of competence in most of the aspects of digital competence. The results of a Tukey difference in the mean test reveals that digital competence among the learners vary significantly based on their gender, economic profile and academic level. This finding can be attributed to the difference in their socio- economic background, which confirms digital divide among learners. Our findings have implications for the design of policies for digital higher education and institutional management for ensuring effective learner participation particularly for the higher education institutions in developing countries.
Simulate Human Thinking: Cognitive Knowledge Graph Reasoning for Complex Question Answering

Question answering over knowledge graph has attracted increasing attention. Though the previous algorithms have achieved competitive performance, they fail to solve problems like humans resulting in the bottleneck of reasoning. However, it is difficult for machines to simulate the question answering process of humans. In order to address this challenge, we propose a novel Cognitive Knowledge Graph Reasoning (CKGR) model based on the cognitive architecture for complex question answering. The CKGR processes information hierarchically with a three-level framework. To fully analyze the question, the first level is proposed to transform the question into features according to different aspects. Then, the relative knowledge graph (KG) regions are activated to simulate the human unconscious thinking process by a memory mapping module. Finally, the CKGR goes deeper to infer the correct answer over KG considering the both semantic and logical parsing of the questions. The CKGR successfully narrows the gap between humans and machines. Extensive experiments on three real-world datasets demonstrate that the proposed method achieves better performance compared with the state-of-the-art methods and provides the reasoning score to find the reasonable path for the answer.
Differentially Evolved RBFNN for FNAB-Based Detection of Breast Cancer

In this work, computational intelligence-based detection of malignant and benign breast cancer classification has been obtained through the evolved radial basis function neural network. The cancer category has defined over observed parameters values of breast lesion extracted through fine needle aspiration. Generally, the final performance of a neural network is decided by the quality of the learning algorithm and transfer function characteristics selected over the active nodes. Gradient-based learning is very popular and useful also but suffered from trapping in the local minima and accuracy deficiency in the outcomes. Hence, the performance quality of the radial basis function neural network has been improved by evolving the basis function parameters and connection weights through a new mutation strategy in the differential evolution. The proposed approach maintains a better balance between exploration and exploitation by providing the probabilistic approach of mutation strategy selection among the defining the differential vector through an available best member or randomly selected member. The mutation strategy guided by the random member-based approach helps in exploration at a large level while the best member-based differential vector helps in faster convergence. The proposed work also provided the provision to have the neural network parameters in the integer value domain so that there will saving in the memory and easiness over hardware realization. The proposed algorithm's performances have been compared against the different commonly used strategies of available mutation strategies in the differential evolution and gradient-based strategy and have shown significant benefits in efficiency and precision. The proposed solution model can be used to assist the cytologist in making the final decision robust and accurate.
Comparing Conventional Statistical Models and Machine Learning in a Small Cohort of South African Cardiac Patients

Background: Machine learning is used to process big data volumes with complex non-linear relationships between predictive variables and predictions. Research into the usefulness of machine learning in small data volumes remains limited.Aim: To compare conventional statistical methods and machine learning to predict angiogram outcomes in a small cohort of South African cardiac patients.Methods: This is a retrospective study on patients with cardiac risk factors at Inkosi Albert Luthuli Central Hospital, Durban, South Africa, from 2002 to 2008. Models were designed using predictive risk factors to forecast a binary angiogram outcome (normal or abnormal) by applying conventional statistical models (binary logistic and log binomial) and stacking ensemble machine learning.Results: The outcome prevalence of abnormal angiograms was 99/173 (57%). Predictive data was used to model this outcome. The binary logistic regression model, which estimates odds ratio, was unsuitable. The log binomial model, which estimates relative risk, did not converge after various stepwise modelling attempts. Thereafter, machine learning models were used. These included logistic regression, k-nearest neighbour, decision tree, support vector machine, and naïve Bayes. The ensemble model amalgamated all algorithms and showed accuracy >70% and excellent performance at different thresholds with an area under the curve (AUC) >80%.Discussion: The logistic regression model was unsuitable because an odds ratio would have been unreliable and overestimated the true effect since the outcome prevalence was >10%. A log binomial model with relative risk estimates did not converge, possibly owing to the multiple predictive variables. Overall, conventional statistical models were unsuccessful in this instance. Machine learning models had limitations from a small dataset. However, the combined modelling with the stacking ensemble method produced good results in the small, homogenous database by exploiting the strengths of each contributing algorithm.Conclusions: Researchers may apply machine learning when conventional statistical models are inconclusive in homogenous small databases with multiple variables and a complex relationship to the outcome. Machine learning is a viable option even with relatively small cohorts if the number of predictive variables is also small.