Reviewerly/exHarmony huggingface dataset:
Austenitic parent grain reconstruction in martensitic steel using deep learning

In this work we develop a deep convolutional architecture to estimate the prior austenite structure from observed martensite electron backscatter diffraction micrographs. A novel data augmentation strategy randomizes the global reference coordinate system which makes it possible to train our model from only four micrographs. The model is much faster than algorithmic approaches and generalizes well when applied to micrographs of a different material. Empirical evidence suggests the efficacy of the model depends on the scale of the microstructure and receptive field of the vision model. This work demonstrates that modern computer vision approaches are well suited for capturing complex spatial-orientation patterns present in orientation imaging micrographs.
Stance Analysis of Distance Education in the Kingdom of Saudi Arabia during the COVID-19 Pandemic Using Arabic Twitter Data

The coronavirus has caused significant disruption to people's everyday lives, altering how people live, work, and study. The Kingdom of Saudi Arabia (KSA) reacted very quickly to suppress the spread of the virus even before the first case of COVID-19 was confirmed in the country. In the education sector, all face-to-face activities at public and private schools and universities were suspended, as they switched from traditional to distance learning for the entire 2020 academic year. This study collected 1,846,285 tweets to analyze the public's dynamic opinions towards distance education in the KSA during the 2020 academic year. Several classical machine-learning models and deep-learning models, including ensemble random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), multinomial naïve Bayes (MNB), convolutional neural network (CNN), and long short-term memory (LSTM), were tested on this data, and the best-performing models were selected to analyze the public stance towards distance education. Additionally, I correlated my analysis with the major events that were announced by the Ministry of Education (MOE). I observed that people in the KSA took some time to react and express their stances at the start of the academic year. Regarding the news, I observed that any exam-related topic attracted high engagement. In-favor stances increased when news headlines covered the topic of exams compared to other topics. The results show that the primary Saudi public stance favored distance education during the 2020 academic year.
Toward more careful corpus statistics: uncertainty estimates for frequencies, dispersions, association measures, and more

This article demonstrates that, counter to current practice, (i) corpus-linguistic studies should provide uncertainty/interval estimates for all corpus-linguistic statistics, even for basic/fundamental ones such as frequencies, dispersions, or association measures, and (ii) these statistics should be based on text-/file-based bootstrapping and confidence/data ellipses covering two or more dimensions of information. Four small case studies – three more programmatic and one more applied – are offered to exemplify the logic and method. The first case study shows how parametric confidence intervals or confidence intervals from word-based bootstrapping can be inappropriate; the second case study exemplifies the computation of frequency-cum-dispersion intervals; the third does the same for collocational/collostructional data (the ditransitive); and the last case study exemplifies the use of these methods in a diachronic statutory-interpretation context.
Learning-Based Path Planning and Predictive Control for Autonomous Vehicles With Low-Cost Positioning

Navigation algorithms for autonomous vehicles have become the subject of increasing interest, but most of them heavily rely on expensive high-precision positioning equipment, which hinders autonomous vehicles’ development. In this article, we propose a path planning and control framework for autonomous vehicles with low-cost positioning. For the path planning layer, a potential field is constructed by the potential functions of road boundaries, obstacles, and reference waypoints. According to the potential field, a reinforcement learning agent is developed to generate a collision-free path for path tracking. For the control layer, a model predictive control based on vehicle dynamics is designed and a linear terminal constraint is considered. Simulation results show that the proposed algorithm can effectively avoid static and dynamic obstacles. Moreover, compared with traditional methods, the proposed algorithm performs better when the positioning devices are imprecise. Furthermore, the real-time performance can meet the requirements of navigation.
Standards‐based modeling and deployment of serverless function orchestrations using BPMN and TOSCA

Abstract Function‐as‐a‐Service (FaaS) is a cloud service model enabling to implement serverless applications for a variety of use cases. These range from scheduled calls of single functions to complex function orchestrations executed using orchestration services such as AWS step functions. However, since the available function orchestration technologies vary in functionalities, supported modeling languages, and APIs, modeling such function orchestrations and their deployment require significant technology‐specific expertise. Moreover, the resulting models are typically not portable due to provider‐ and technology‐specific details, and major efforts are required when exchanging an orchestrator or provider due to such lock‐ins. To tackle this issue, we introduce a vendor‐ and technology‐agnostic method for the modeling and deployment of serverless function orchestrations, which relies on the business process model and notation (BPMN) and topology and orchestration specification for cloud applications (TOSCA) standards for modeling function orchestrations and their deployment, respectively. We also present a toolchain for modeling serverless function orchestrations in BPMN, generating proprietary models supported by different function orchestration technologies from BPMN models, specifying their actual deployment in TOSCA, and then enacting such deployment. Finally, we illustrate a case study applying our method and toolchain in practice.
Multivariate Time-series Anomaly Detection using SeqVAE-CNN Hybrid Model

Anomaly detection has been recognized as an important research area in many industries such as Information Technology, manufacturing, finance, etc. Recently, diverse research for anomaly detection has been conducted utilizing current deep learning methods including machine learning algorithms. However, multivariate time-series anomaly detection can be challenging problems because of the imbalance of anomaly data and the complexity of multivariate. In this paper, we propose a SeqVAE-CNN model based on deep learning using an unsupervised approach. Our model combines Variational Autoencoder (VAE) with Convolutional Neural Networks (CNN) as utilizing Seq2Seq structure to capture temporal correlations and spatial features in multivariate time-series. To demonstrate the performance of our approaches, we evaluate our model on 8 datasets from various domains. The experimental results demonstrate that our model has better performance of anomaly detection than other models by recording the highest AUROC and F1 scores on six of the eight datasets.
COVID-19 SOP’s Violations Detection in Terms of Face Mask Using Deep Learning

Facemask detection is a need of time as we are suffering in a pandemic situation of COVID-19, and facemask is considered the best preventive measure to stop the rapid spread. The vast majority of the world population is still unvaccinated, especially young and kids. Moreover, despite the vaccination, people are still getting Covid positive, and the majority are due to the Delta variant. So, we still need to have strict SOP implementation. The best way is to have some autonomous system to monitor SOP compliance and alert the authority to take countermeasures. Many people wear the mask, but the mask is usually on the chin and does not serve the purpose because the facemask must cover the mouth and nose to stop the spread. This study has proposed the improved version of the YOLOv4 model for the robust detection of face masks and checks whether the mask is worn in the recommended way. 2D convolutions of Yolov4 are replaced with the spatially separable convolutional in YOLOv4 to reduce the parameters so that the model can work in real-time. We have achieved an accuracy of 86.61&#x0025; in terms of proper mask-wearing. Unlike other proposed approaches, our model is not only detecting the mask but also determines that whether the mask is worn in the recommended manner.Data Availability: https://drive.google.com/drive/folders/1M0Bw1vxJhqx0KtiX5hKAAojZHkx0PbCS?usp=sharing
Supporting informal science learning with metacognitive scaffolding and augmented reality: effects on science knowledge, intrinsic motivation, and cognitive load

Background Museums have been paying an increasing attention to the design of experiences conducive to informal learning. While spontaneous inquiries in museums often pique visitors’ intrinsic motivation to learn, certain structures or scaffolds are needed to facilitate sense making in museum learning. Two promising approaches are identified: metacognitive scaffolding and augmented reality (AR) that offer on-demand content and interactions.Purpose This study aimed to answer the following research question: Compared with metacognitive scaffolding alone, how does the access to additional AR content affect informal learning experiences in a science museum in terms of science content knowledge, intrinsic motivation, and cognitive load?.Sample The sample of the study consisted of 63 sixth-grade students divided into two groups. The treatment group (31) received both metacognitive scaffolding and AR support in their museum visit, whereas the control group (32) received only metacognitive scaffolding.Design and methods A quasi-experimental research design was conducted. The independent variable was the treatment conditions, and the dependent variables were students’ science knowledge test performance, learning motivation, and cognitive load. The research instrument for this study included a science knowledge test, a learning motivation survey, and a perceived cognitive load questionnaire.Results The results revealed that the combination of metacognitive scaffolding and AR led the treatment group to significantly outcome the control group in the science knowledge post-test, but the effect did not last in the 2-week delayed retention test. The control group perceived more importance in the museum learning activity. No difference was found in cognitive load.Conclusions Metacognitive scaffolding may have an enduring impact on science learning in museums. The long-term impact of AR needs further investigation. Balance should be maintained between structure and open exploration to sustain intrinsic motivation in informal learning settings.
Explainable Artificial Intelligence in Cybersecurity: A Brief Review

Artificial intelligence has an enormous prospective to secure the cyber world. A lot of research is happening in the area of artificial intelligence in cyber security, however, the adoption in real-world practice is extremely limited. Lack of interpretability and accuracy of the artificial intelligence models pose restriction in implementation, as the user need to be confident that the Artificial Intelligence system used in decision making is trustworthy. Explainable Artificial Intelligence overcomes this concern and is the right step towards dependable and predictable Artificial Intelligence. This paper aims to provide a broad understanding of various terminology, classification, and scope of Explainable Artificial Intelligence in cyber security, challenges faced and metrics used for evaluation through the existing literature.
Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement Learning

The ability to perform aggressive movements,which are called aggressive flights, is important for quadrotors during navigation. However, aggressive quadrotor flights are still a great challenge to practical applications. The existing solutions to aggressive flights heavily rely on a predefined trajectory, which is a time-consuming preprocessing step. To avoid such path planning, we propose a curiosity-driven reinforcement learning method for aggressive flight missions and a similarity-based curiosity module is introduced to speed up the training procedure. A branch structure exploration strategy is also applied to guarantee the robustness of the policy and to ensure the policy trained in simulations can be performed in real-world experiments directly. The experimental results in simulations demonstrate that our reinforcement learning algorithm performs well in aggressive flight tasks, speeds up the convergence process and improves the robustness of the policy. Besides, our algorithm shows a satisfactory simulated to real transferability and performs well in real-world experiments.
Extreme-user conditions to enhance design creativity and empathy- application using visual impairment

Extreme-users who experience physical, sensory or cognitive challenges can help identify latent needs across a majority of the general population users. Identifying these latent needs may open doors to novel products, services, and systems. Empathic design techniques of simulation tools and scenarios allow designers to experience a range of extreme-user perspectives. However, research still lacks a thorough understanding of the potential impact of such techniques, especially their potential to uncover and address latent needs. This paper strengthens the understanding of simulation tools and scenarios by analyzing two user-centered workshops that applied simulated scenarios to empathize with users with visual impairments (VIs), and uses an empathic similarity metric to evaluate the empathic outcomes in conjunction with self-evaluated empathy. In addition to empathy, creativity is measured for the concepts shared by 36 (x2) workshop participants and 13 participants with VIs. Empirical analysis of the results, across two sequences of controlled studies, supports the potential of simulated scenarios in evoking participant creativity and empathy.
A statistically based sentence scoring method using mathematical combination for extractive Hindi text summarization

AbstractAbstractElectronic documents contain tremendous amounts of information. A text summary system is needed to save time and rapidly know about the document, because the manual text summary requires time, effort, and costs, which automatically creates a summary from the source document. Hindi is India’s most spoken language. A Hindi document summarization system does not exist. The method of summary generation is either extractive or abstractive. The extractive text summary method extracts the most suitable phrases from input documents to be incorporated into the summary whereas the abstractive is the human-like summary method. To select a summary sentence, it must be the highest score. In this paper, we apply different mathematical combinations using nine textual features to score a sentence. We analyzed the results on 2000 documents taken from the Forum of Information Retrieval Evaluation (FIRE)1 conference using ROUGE evaluation matrices at the retention rate of 40%.Subject Classification: 60K3585A35Keywords: Mathematical combinationMathematical modelingRetention ratioFrequency and Latent semantic analysis
Gradient-based elephant herding optimization for cluster analysis

Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient-based elephant herding optimization for cluster analysis (GBEHO) is proposed. A well-defined set of heuristics is introduced to select the initial centroids instead of selecting random initial points. Specifically, the elephant optimization algorithm (EHO) is combined with the gradient-based algorithm GBO for assigning initial cluster centers across the search space. Second, to overcome the imbalance between the original EHO exploration and exploitation, the initialized population is improved by introducing Gaussian chaos mapping. In addition, two operators, i.e., random wandering and variation operators, are set to adjust the location update strategy of the agents. Nine datasets from synthetic and real-world datasets are adopted to evaluate the effectiveness of the proposed algorithm and the other metaheuristic algorithms. The results show that the proposed algorithm ranks first among the 10 algorithms. It is also extensively compared with state-of-the-art techniques, and four evaluation criteria of accuracy rate, specificity, detection rate, and F-measure are used. The obtained results clearly indicate the excellent performance of GBEHO, while the stability is also more prominent.
Comparing Sensitivities of Geodetic Processing Methods for Rapid Earthquake Magnitude Estimation

Abstract Rapid earthquake magnitude estimation from real-time space-based geodetic observation streams provides an opportunity to mitigate the impact of large and potentially damaging earthquakes by issuing low-latency warnings prior to any significant and destructive shaking. Geodetic contributions to earthquake characterization and rapid magnitude estimation have evolved in the last 20 yr, from post-processed seismic waveforms to, more recently, improved capacity of regional geodetic networks enabled real-time Global Navigation Satellite System seismology using precise point positioning (PPP) displacement estimates. In addition, empirical scaling laws relating earthquake magnitude to peak ground displacement (PGD) at a given hypocentral distance have proven effective in rapid earthquake magnitude estimation, with an emphasis on performance in earthquakes larger than ∼Mw 6.5 in which near-field seismometers generally saturate. Although the primary geodetic contributions to date in earthquake early warning have focused on the use of 3D position estimates and displacements, concurrent efforts in time-differenced carrier phase (TDCP)-derived velocity estimates also have demonstrated that this methodology has utility, including similarly derived empirical scaling relationships. This study builds upon previous efforts in quantifying the ambient noise of three-component ground-displacement and ground-velocity estimates. We relate these noise thresholds to expected signals based on published scaling laws. Finally, we compare the performance of PPP-derived PGD to TDCP-derived peak ground velocity (PGV), given several rich event datasets. Our results indicate that TDCP-PGV is more likely than PPP-PGD to detect intermediate magnitude (∼Mw 5.0–6.0) earthquakes, albeit with greater magnitude estimate uncertainty and across smaller epicentral distances. We conclude that the computationally lightweight TDCP-derived PGV magnitude estimation is complementary to PPP-derived PGD magnitude estimates, which could be produced at the network edge at high rates and with increased sensitivity to ground motion than current PPP estimates.
A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers

Abstract In machine learning, hyperparameter tuning is strongly useful to improve model performance. In our research, we concentrate our attention on classifying imbalanced data by cost-sensitive support vector machines. We propose a multi-objective approach that optimizes model’s hyper-parameters. The approach is devised for imbalanced data. Three SVM model’s performance measures are optimized. We present the algorithm in a basic version based on genetic algorithms, and as an improved version based on genetic algorithms combined with decision trees. We tested the basic and the improved approach on benchmark datasets either as serial and parallel version. The improved version strongly reduces the computational time needed for finding optimized hyper-parameters. The results empirically show that suitable evaluation measures should be used in assessing the classification performance of classification models with imbalanced data.
HGEN: Learning Hierarchical Heterogeneous Graph Encoding for Math Word Problem Solving

Designing algorithms to solve math word problems (MWPs) is an important research topic in natural language processing and smart education domains. The task of solving MWPs involves transforming math problem texts into math equations. Although recent Graph2Tree-based models, which adopt homogeneous graph encoders to learn quantity representations, have obtained very promising results in generating math equations, they do not consider the heterogeneous issue and the long-distance dependencies of heterogeneous nodes. In this paper, we propose a novel hierarchical heterogeneous graph encoding called HGEN for MWPs. Specifically, HGEN first introduces a heterogeneous graph consisting of a node-level attention layer and a type-aware attention layer to learn the heterogeneous node embedding. HGEN then captures the long-distance dependent information by propagating the multi-hop nodes in a hierarchical manner. We conduct extensive experiments on two popular MWP datasets. Our empirical results show that HGEN significantly outperforms the state-of-the-art Graph2Tree-based models in the literature.
Deep Modular Bilinear Attention Network for Visual Question Answering

VQA (Visual Question Answering) is a multi-model task. Given a picture and a question related to the image, it will determine the correct answer. The attention mechanism has become a de facto component of almost all VQA models. Most recent VQA approaches use dot-product to calculate the intra-modality and inter-modality attention between visual and language features. In this paper, the BAN (Bilinear Attention Network) method was used to calculate attention. We propose a deep multimodality bilinear attention network (DMBA-NET) framework with two basic attention units (BAN-GA and BAN-SA) to construct inter-modality and intra-modality relations. The two basic attention units are the core of the whole network framework and can be cascaded in depth. In addition, we encode the question based on the dynamic word vector of BERT(Bidirectional Encoder Representations from Transformers), then use self-attention to process the question features further. Then we sum them with the features obtained by BAN-GA and BAN-SA before the final classification. Without using the Visual Genome datasets for augmentation, the accuracy of our model reaches 70.85% on the test-std dataset of VQA 2.0.
Understanding Machine Learning

Machine Learning is an emerging field of the Artificial Intelligence and data science. It is yet to be conceptualised and operationalised to be fully understood in its complexity and entirety. This chapter will consider it in a more detailed way through providing definitions to its constituting elements and analysing the learning process itself, and will review the accompanying factors around it.
Scalable Access Control Scheme of Internet of Things Based on Blockchain

With the development of Internet of things (IoT) technology, a large-scale, heterogeneous and dynamic distributed IoT environment has gradually formed between different IoTs. In order to solve the scalability problem of restricted device access management in the Internet of things, a distributed access control system model of the Internet of things based on blockchain technology is proposed. The system model adopts a single smart contract, which simplifies the whole process in the blockchain network and reduces the communication overhead between nodes. According to the simulation results and evaluation, it is proved that the solution has good scalability.