Reviewerly/exHarmony huggingface dataset:
Gender differences in predictors of technology threat avoidance

Purpose The purpose of this paper is to examine gender differences in predictors of technology threat avoidance motivation and behavior among working US adults. Implications were considered in regard to cybersecurity awareness training motivation and perceptions of need for protective cybersecurity behavior in the workplace. Design/methodology/approach A single-shot regression-based study used ordinal regression supported by K-means clustering to evaluate the moderating effects of gender on predictors of technology threat avoidance motivation and behavior on a sample of n = 206 US adult workers. Findings The regression model explained 47.5% of variance in avoidance motivation and 39% of avoidance behavior variance. Gender moderated predictive associations between several independent variables and avoidance motivation: perceived susceptibility, perceived effectiveness, perceived cost and self-efficacy. Gender also moderated the association between avoidance motivation and avoidance behavior. Research limitations/implications The predictive impact of gender extends beyond the main effects in technology threat avoidance. Data frequency distributions and inter-variable relationships should be routinely considered in threat avoidance studies, especially if sample variables exhibit non-normal frequency distributions and nonlinear associations. Practical implications Gender was significantly associated with threat avoidance motivation and avoidance behavior and exhibited notable associations with antecedents of avoidance motivation. Related insights can inform the design and delivery of training content relating to technology threat avoidance as organizations strive to more effectively leverage information technology end-users as protective assets for the enterprise. Originality/value The uniqueness of this study derives from its focus and findings regarding the moderating effects of gender on technology threat avoidance factors and techniques used to measure and evaluate the associations between them.
Machine Learning Approach towards Mammographic Breast Density Measurement for Breast Cancer Risk Prediction: An Overview

“Mammographic breast density” is significant biomarker which has strong association with the occurrence of breast cancer. Generally, in dense breast the tissues of breast cancer get hidden and hence the sensitivity of mammogram reduces significantly. It also increases the risk of false positive diagnosis in computer detection system. Development in the cutting-edge technology has the ability to improve the breast density measurement standards, and lead to early prediction of breast cancer by overcoming the restriction of subjective analysis of breast density measurement. In spite of development of different techniques for automatic breast density measurement, very few tools are practically available for breast cancer risk prediction. Basic objective behind this article is to provide an organized overview of selective research articles having specific application of machine learning approach for mammographic breast density measurement. We have selected research articles which follows the pipe-line of image processing with machine learning. Accuracy and computational complexity of selected methods are highlighted in order to decide the best possible future guidelines of research in this area. Hence the modification in preprocessing algorithms to remove pectoral muscle, refining of segmentation as well as feature extraction algorithms in terms of computational complexity and accuracy, selection of more accurate machine learning algorithm can make a positive impact on accuracy enhancement of breast density measurement.
Hypers@DravidianLangTech-EACL2021: Offensive language identification in Dravidian code-mixed YouTube Comments and Posts

Code-Mixed Offensive contents are used pervasively in social media posts in the last few years. Consequently, gained the significant attraction of the research community for identifying the different forms of such content (e.g., hate speech, and sentiments) and contributed to the creation of datasets. Most of the recent studies deal with high-resource languages (e.g., English) due to many publicly available datasets, and by the lack of dataset in low-resource anguages, those studies are slightly involved in these languages. Therefore, this study has the focus on offensive language identification on code-mixed low-resourced Dravidian languages such as Tamil, Kannada, and Malayalam using the bidirectional approach and fine-tuning strategies. According to the leaderboard, the proposed model got a 0.96 F1-score for Malayalam, 0.73 F1-score for Tamil, and 0.70 F1-score for Kannada in the bench-mark. Moreover, in the view of multilingual models, this modal ranked 3rd and achieved favorable results and confirmed the model as the best among all systems submitted to these shared tasks in these three languages.
Polymorphic Iterable Sequential Effect Systems

Effect systems are lightweight extensions to type systems that can verify a wide range of important properties with modest developer burden. But our general understanding of effect systems is limited primarily to systems where the order of effects is irrelevant. Understanding such systems in terms of a semilattice of effects grounds understanding of the essential issues and provides guidance when designing new effect systems. By contrast, sequential effect systems—where the order of effects is important—lack an established algebraic structure on effects. We present an abstract polymorphic effect system parameterized by an effect quantale—an algebraic structure with well-defined properties that can model the effects of a range of existing sequential effect systems. We define effect quantales, derive useful properties, and show how they cleanly model a variety of known sequential effect systems. We show that for most effect quantales, there is an induced notion of iterating a sequential effect; that for systems we consider the derived iteration agrees with the manually designed iteration operators in prior work; and that this induced notion of iteration is as precise as possible when defined. We also position effect quantales with respect to work on categorical semantics for sequential effect systems, clarifying the distinctions between these systems and our own in the course of giving a thorough survey of these frameworks. Our derived iteration construct should generalize to these semantic structures, addressing limitations of that work. Finally, we consider the relationship between sequential effects and Kleene Algebras, where the latter may be used as instances of the former.
A real-time dynamic path planning method combining artificial potential field method and biased target RRT algorithm

Abstract In order to improve the efficiency and effectiveness of path planning for mobile robots in dynamic environment, based on the comprehensive analysis of multiple path planning algorithms, an improved algorithm based on fast-spreading random tree algorithm is proposed. The proposed algorithm effectively combines the artificial potential field(APF) method with the biased target RRT algorithm, and judges the local minimum point in the process of solving the APF method to complete the switching with the biased target RRT algorithm. The motion environment simulation model is established in the Python environment, and the path planning simulation case of the mobile robot in the dynamic environment is completed. The simulation results show that the combined algorithm can avoid the phenomenon that the APF method falls into the local minimum point while taking the global and real-time performance into account, reduce the planning time of the biased target RRT algorithm, and improve the quality of path generation.
dMVX

Multi-variant execution (MVX) systems amplify the effectiveness of software diversity techniques. The key idea is to run multiple diversified program variants in lockstep while providing them with the same input and monitoring their run-time behavior for divergences. Thus, adversaries have to compromise all program variants simultaneously to mount an attack successfully.
Access Control in IoT Networks: Analysis and Open Challenges

Internet of Things (IoT) has witnessed significant attention as well as growth in recent times because of the rapid surge of smart devices. Characteristically, these IoT devices are restricted in terms of computation power and storage capability, and consequently, they are more susceptible to attacks than other contemporary devices such as smartphones, tablets, or computers. The rapid growth of the IoT and the explosive development of sensitive data created by user equipment have headed to resilient demand for access control. This work encompasses a comprehensive analysis of all prevalent access control models for authentication/authorization in the Internet of Things (IoT). The Benefits and drawbacks of both traditional and contemporary (Blockchain-based) solutions for access control from the viewpoints of IoT are emphasized. Eventually, probable challenges and forthcoming research directions are defined.
Subarchitecture Ensemble Pruning in Neural Architecture Search

Neural architecture search (NAS) is gaining more and more attention in recent years because of its flexibility and remarkable capability to reduce the burden of neural network design. To achieve better performance, however, the searching process usually costs massive computations that might not be affordable for researchers and practitioners. Although recent attempts have employed ensemble learning methods to mitigate the enormous computational cost, however, they neglect a key property of ensemble methods, namely diversity, which leads to collecting more similar subarchitectures with potential redundancy in the final design. To tackle this problem, we propose a pruning method for NAS ensembles called " subarchitecture ensemble pruning in neural architecture search (SAEP)." It targets to leverage diversity and to achieve subensemble architectures at a smaller size with comparable performance to ensemble architectures that are not pruned. Three possible solutions are proposed to decide which subarchitectures to prune during the searching process. Experimental results exhibit the effectiveness of the proposed method by largely reducing the number of subarchitectures without degrading the performance.
Experimental Demonstration of a Multifunctional All-Optical Quantum State Transfer Machine

Quantum information protocol with quantum resources shows a great advantage in substantially improving security, fidelity, and capacity of information processing. Various quantum information protocols with diverse functionalities have been proposed and implemented. However, in general, the present quantum information system can only carry out a single information protocol or deal with a single communication task, which limits its practical application in the future. Therefore, it is essential to develop a multifunctional platform compatible with multiple different quantum information protocols. In this Letter, by utilizing an all-optical platform consisting of a gain-tunable parametric amplifier, a beam splitter, and an entanglement source, we experimentally realize the partially disembodied quantum state transfer protocol, which links the all-optical quantum teleportation protocol and the optimal $1\ensuremath{\rightarrow}N$ coherent state cloning protocol. As a result, these three protocols, which have different physical essences and functionalities, are implemented in a single all-optical machine. In particular, we demonstrate that the partially disembodied quantum state transfer protocol can enhance the state transfer fidelity compared with all-optical quantum teleportation under the same strength of entanglement. Our all-optical quantum state transfer machine paves a way to implement the multifunctional quantum information system.
Foreign Direct Investment, Governance and Inclusive Growth in Sub-Saharan Africa

Motivated by the projected rebound of foreign direct investment (FDI) inflow to sub-Saharan Africa (SSA) following the implementation of the AfCFTA and the finalization of the Africa Investment Protocol, we examine how FDI modulates the effects of various governance dynamics on inclusive growth in SSA. We do this by testing two hypotheses first, whether unconditionally FDI and various governance indicators (rule of law, control of corruption, regulatory quality, governance effectiveness, political stability, and voice and accountability) foster inclusive growth in SSA; and second, whether these governance dynamics engender positive synergy with FDI on inclusive growth in SSA. Using data from the World Bank’s World Governance Indicators and the World Development Indicators for the period 1990–2020, we employ several fixed effects, random effects, and the system GMM estimators for the analysis. First, we find that FDI and all our governance dynamics are significant inclusive growth enhancers in SSA. Second, though FDI amplifies the effects of all our governance dynamics on inclusive growth in SSA, governance effectiveness, voice and accountability, and political stability are keys. Policy recommendations are provided.
A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions

Stakeholders make various types of decisions with respect to requirements, design, management, and so on during the software development life cycle. Nevertheless, these decisions are typically not well documented and classified due to limited human resources, time, and budget. To this end, automatic approaches provide a promising way. In this paper, we aimed at automatically classifying decisions into five types to help stakeholders better document and understand decisions. First, we collected a dataset from the Hibernate developer mailing list. We then experimented and evaluated 270 configurations regarding feature selection, feature extraction techniques, and machine learning classifiers to seek the best configuration for classifying decisions. Especially, we applied an ensemble learning method and constructed ensemble classifiers to compare the performance between ensemble classifiers and base classifiers. Our experiment results show that (1) feature selection can decently improve the classification results; (2) ensemble classifiers can outperform base classifiers provided that ensemble classifiers are well constructed; (3) BoW + 50% features selected by feature selection with an ensemble classifier that combines Naïve Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) achieves the best classification result (with a weighted precision of 0.750, a weighted recall of 0.739, and a weighted F1-score of 0.727) among all the configurations. Our work can benefit various types of stakeholders in software development through providing an automatic approach for effectively classifying decisions into specific types that are relevant to their interests.
AFFINE LOGIC FOR CONSTRUCTIVE MATHEMATICS

Abstract We show that numerous distinctive concepts of constructive mathematics arise automatically from an “antithesis” translation of affine logic into intuitionistic logic via a Chu/Dialectica construction. This includes apartness relations, complemented subsets, anti-subgroups and anti-ideals, strict and non-strict order pairs, cut-valued metrics, and apartness spaces. We also explain the constructive bifurcation of some classical concepts using the choice between multiplicative and additive affine connectives. Affine logic and the antithesis construction thus systematically “constructivize” classical definitions, handling the resulting bookkeeping automatically.
Work-in-Progress-Enhancing Training in Virtual Reality with Hand Tracking and a Real Tool

The main goal of Virtual Training Environments (VTEs) is to maximize training effectiveness, which can be achieved by increasing the degree of immersion. While prior work mainly focused on the visual, auditory, and navigational aspects of immersion, proprioceptive aspects may be particularly important. In this work-in-progress paper, we explore this potential by implementing an industrial VTE, which can be interacted with, using VR gloves and a tracked real tool. Further, we evaluate the VTE in a pilot study with industry apprentices, providing initial evidence that participants experienced high presence and low task load, while being generally satisfied with the training.
Implementing Quantum Finite Automata Algorithms on Noisy Devices

Quantum finite automata (QFAs) literature offers an alternative mathematical model for studying quantum systems with finite memory. As a superiority of quantum computing, QFAs have been shown exponentially more succinct on certain problems such as recognizing the language $$\mathtt {MOD}_\mathrm{p}= \{{a^{j}} \mid {j \equiv 0 \mod p}\} $$ with bounded error, where p is a prime number. In this paper we present improved circuit based implementations for QFA algorithms recognizing the $$\mathtt {MOD}_\mathrm{p}$$ problem using the Qiskit framework. We focus on the case $$p=11$$ and provide a 3 qubit implementation for the $$\mathtt {MOD}_\mathrm{11}$$ problem reducing the total number of required gates using alternative approaches. We run the circuits on real IBM quantum devices but due to the limitation of the real quantum devices in the NISQ era, the results are heavily affected by the noise. This limitation reveals once again the need for algorithms using less amount of resources. Consequently, we consider an alternative 3 qubit implementation which works better in practice and obtain promising results even for the problem $$\mathtt {MOD}_\mathrm{31}$$ .
Decoding binary decisions under differential target probabilities from pupil dilation: A random forest approach

Although our pupils slightly dilate when we look at an intended target, they do not when we look at irrelevant distractors. This finding suggests that it may be possible to decode the intention of an observer, understood as the outcome of implicit covert binary decisions, from the pupillary dynamics over time. However, few previous works have investigated the feasibility of this approach and the few that did, did not control for possible confounds such as motor-execution, changes in brightness, or target and distractor probability. We report on our efforts to decode intentions from pupil dilation obtained under strict experimental control on a single trial basis using a machine learning approach. The basis for our analyses are data of 69 participants who looked at letters that needed to be selected with stimulus probabilities that varied systematically in a blockwise manner (n = 19,417 trials). We confirm earlier findings that pupil dilation is indicative of intentions and show that these can be decoded with a classification performance of up to 76% area under the curve for receiver operating characteristic curves if targets are rarer than distractors. To better understand which characteristics of the pupillary signal are most informative, we finally compare relative feature importances. The first derivative of pupil size changes was found to be most relevant, allowing us to decode intention within only about 800 ms of trial onset. Taken together, our results provide credible insights into the potential of decoding intentions from pupil dilation and may soon form the basis for new applications in visual search, gaze-based interaction, or human-robot interaction.
Dependent type systems as macros

We present Turnstile+, a high-level, macros-based metaDSL for building dependently typed languages. With it, programmers may rapidly prototype and iterate on the design of new dependently typed features and extensions. Or they may create entirely new DSLs whose dependent type "power" is tailored to a specific domain. Our framework's support of language-oriented programming also makes it suitable for experimenting with systems of interacting components, e.g., a proof assistant and its companion DSLs. This paper explains the implementation details of Turnstile+, as well as how it may be used to create a wide-variety of dependently typed languages, from a lightweight one with indexed types, to a full spectrum proof assistant, complete with a tactic system and extensions for features like sized types and SMT interaction.
A stereo photogrammetry system for model position and attitude measurement in hypersonic wind tunnel testing

In this paper, a measurement system based on stereo photogrammetry is proposed for position and attitude measurements of test models in hypersonic wind tunnel testing. Several adaptations were made to an existing stereo photogrammetry system to promote its routine use as a primary wind tunnel measurement system. First, cameras were installed in the wind tunnel test section to decrease the measurement distance and increase the base length, which helped improve the measurement accuracy. To deal with camera vibration caused by the freestream, a camera orientation method is proposed to estimate the exterior parameters of each frame relative to the tunnel coordinate system. Second, a scheme to determine the model coordinate system is proposed, which does not require placement of the targets at known locations of the model. Third, to solve the recognition problem of motion-blurred target images, a network was adopted for stereo deblurring and a stereo blur dataset simulating the wind tunnel test environment was generated for network training. To develop the novel stereo photogrammetry system, experiments were conducted in both the laboratory and a hypersonic wind tunnel at the China Aerodynamics Research and Development Center. The experimental results suggest that the proposed measurement system is practicable for position and attitude measurements of test models in hypersonic wind tunnel testing.
An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique.