Reviewerly/exHarmony huggingface dataset:
Pandemics and technology engagement: New evidence from <scp>m‐Health</scp> intervention during COVID‐19 in India

Information provision for social welfare via cheap technological media is now a widely available tool used by policymakers. Often, however, an ample supply of information does not translate into high consumption of information due to various frictions in demand, possibly stemming from the pecuniary and non-pecuniary cost of engagement, along with institutional factors. We test this hypothesis in the Indian context using a unique data set comprising 2 million call records of enrolled users of ARMMAN, a Mumbai-based nongovernmental organization that sends timely informational calls to mobile phones of less-privileged pregnant women. The strict lockdown induced by COVID-19 in India was an unexpected shock on engagement with m-Health technology, in terms of both reductions in market wages and increased time availability at home. Using a difference-in-differences design on unique calls tracked at the user-time level with fine-grained time-stamps on calls, we find that during the lockdown period, the call durations increased by 1.53 percentage points. However, technology engagement behavior exhibited demographic heterogeneity increasing relatively after the lockdown for women who had to borrow the phones vis-à-vis phone owners, for those enrolled in direct outreach programs vis-à-vis self-registered women, and for those who belonged to the low-income group vis-à-vis high-income group. These findings are robust with coarsened exact matching and with a placebo test for a 2017-2018 sample. Our results have policy implications around demand-side frictions for technology engagement in developing economies and maternal health.
Enhancing EFL Learners’ Self-Efficacy Beliefs of Learning English with Emoji Feedbacks in CALL: Why and How

Encouraging feedback positively affects learners' self-efficacy; in language learning, self-efficacy predicts language learner performance and behavior. Our research involved three studies to expand knowledge about why and how we can enhance English as a Foreign Language (EFL) learners' self-efficacy beliefs in online settings. In Study 1, based on an online survey with 310 participants, we ascertained the extent to which EFL learners with greater self-efficacy tend to challenge themselves by learning content that requires a proficiency level that is higher than their current proficiency. In Study 2, we recruited 120 EFL learners; the results indicate that positive feedback via emojis embedded in online courses could significantly boost EFL learners' self-efficacy beliefs about learning English. Study 3 involved 35 participants and extended the understanding provided by the first two studies, showing that EFL learners not only like to use emojis for computer-mediated communication (CMC), but also prefer to receive them as feedback. This research adds to knowledge on "why" and "how" we can enhance EFL learners' self-efficacy beliefs in online contexts. We systematically provide empirical evidence regarding the aforementioned issues and demonstrate that positive feedback through emojis has great potential to enhance EFL learners' self-efficacy, even when such feedback is subliminal.
The build-up construction over a commutative non-unital ring

There is a local ring I of order 4, without identity for the multiplication, defined by generators and relations as $$\begin{aligned} I=\langle a,b \mid 2a=2b=0,\, a^2=b,\, \,ab=0 \rangle . \end{aligned}$$ We study a recursive construction of self-orthogonal codes over I. We classify self orthogonal codes of length n and size $$2^n$$ (called here quasi self-dual codes or QSD) up to the length $$n=6.$$ In particular, we classify Type IV codes (QSD codes with even weights) and quasi Type IV codes (QSD codes with even torsion code) up to $$n=6.$$
Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis

Microscopy with ultraviolet surface excitation (MUSE) is increasingly studied for intraoperative assessment of tumor margins during breast-conserving surgery to reduce the re-excision rate. Here we report a two-step classification approach using texture analysis of MUSE images to automate the margin detection. A study dataset consisting of MUSE images from 66 human breast tissues was constructed for model training and validation. Features extracted using six texture analysis methods were investigated for tissue characterization, and a support vector machine was trained for binary classification of image patches within a full image based on selected feature subsets. A weighted majority voting strategy classified a sample as tumor or normal. Using the eight most predictive features ranked by the maximum relevance minimum redundancy and Laplacian scores methods has achieved a sample classification accuracy of 92.4% and 93.0%, respectively. Local binary pattern alone has achieved an accuracy of 90.3%.
Research on Software Vulnerability Detection Method Based on Improved CNN Model

A software construction detection algorithm based on improved CNN model is proposed. Firstly, extract the vulnerability characteristics of the software, extract the characteristics from the static code by using the program slicing technology, establish the vulnerability library, standardize the vulnerability language, and vectorize it as the input data. Gru model is used to optimize CNN neural network. The organic combination of the two can quickly process the feature data and retain the calling relationship between the codes. Compared with single CNN and RNN model, it has stronger vulnerability detection ability and higher detection accuracy. In contrast, the software algorithm of the improved CNN model has strong vulnerability detection ability and higher detection accuracy. In terms of training loss rate, the DNN + Gru model is 17.2% lower than the single RNN model, 10.5% lower than the single CNN model, and 7% lower than the VulDeePecker model.
Conjunctive use of synchrotron X‐ray diffraction and Rietveld refinement in Fe‐oxide clays for forensic applications

Abstract Soils have distinctive chemical, physical, mineralogical and biological properties, which make it possible to differentiate them in different environments and also to test for an association of a particular questioned item with a crime scene. Mineral compositions and structures in the soil clay fraction can reflect a distinct characteristic when analyzed by powerful techniques. In this way, the aim of this study was to compare the discriminative power of Fe‐oxides concentrated clay samples from the same soil class with and without differences in parent material when analyzed using the Rietveld method and two sources of X‐ray diffraction: conventional and high‐resolution synchrotron radiations. Clay samples were Fe concentrated (kaolinite and gibbsite removal) to clarify mineralogical composition of 12 samples from three simulated crime scenes, developed under claystone and granite. By Rietveld refinement, detailed crystallographic data were obtained to discriminate samples according their provenance. By synchrotron radiation, mineral data demonstrated the power of quantitative (crystallite size and minerals contents) and qualitative (identification of majoritarian and accessory minerals) analysis by Rietveld refinement, which provides well‐resolved data able to discriminate samples from different and same geology. The techniques can be applied in other criminal investigations given their potential of discrimination.
A hybrid approach for noise reduction-based optimal classifier using genetic algorithm: A case study in plant disease prediction

Plant diseases can cause significant losses to agricultural productivity; therefore, their early prediction is much needed. So far, many machine learning-based plant disease prediction models have been recommended, but these models face a problem of noisy class label dataset that degrades the performance. Noisy class label dataset results from the improper assignment of positive class labels into negative class data samples or vice versa. Hence, a precise and noise-free plant disease model is required for a better prediction. The current study proposes noise reduction-based hybridized classifiers for plant disease prediction. One tomato and four soybean disease datasets have been selected to conduct the proposed research. The Adaptive Sampling-based Class Label Noise Reduction (AS-CLNR) method has been used along with the Support Vector Machine (SVM) approach for noise reduction. The noise-minimized datasets have been fed into the Extreme Learning Machine (ELM), Decision Tree (DT), and Random Forest (RF) classifiers whose parameters are optimized using Genetic Algorithm (GA) for developing plant disease prediction models. The performances of all these models viz. Hybrid SVM-GA-ELM, Hybrid SVM-GA-DT, and Hybrid SVM-GA-RF have been evaluated using Accuracy, Area under ROC Curve, and F1-Score metrics. Further, these classifiers have been ranked using the statistical Friedman Test in which the Hybrid SVM-GA-RF classifier performed the best. Lastly, the Nemenyi test has also been performed to find out if significant differences exist between various classifiers or not. It was found that 33.33% of the total pairs of hybrid classifiers show a remarkably different performance from one another.
Study on sentiment classification strategies based on the fuzzy logic with crow search algorithm

In recent times, sentiment analysis research has gained wide popularity. That situation causes the importance of online applications that allow users to express their opinions on events, services, or products through social media applications such as Twitter, Facebook, and Amazon. This paper proposes a novel sentiment classification method according to the fuzzy rule-based system (FRBS) with the crow search algorithm (CSA). FRBS is used to classify the polarity of sentences or documents, and the CSA is employed to optimize the best output from the fuzzy logic algorithm. The FRBS is applied to extract the sentiment and classify its polarity into negative, neutral, and positive. Sometimes, the outputs of the FRBS must be enhanced, especially since many variables are present and the rules between them overlap. For such cases, the CSA is used to solve this limitation faced by FRBS to optimize the outputs of FRBS and achieve the best result. This study compares the performance of the proposed model with different machine learning algorithms, such as SVM, maximum entropy, boosting, and SWESA. It tests the model on three famous data sets collected from Amazon, Yelp, and IMDB. Experimental results demonstrate the effectiveness of the proposed model and achieve competitive performance in terms of accuracy, recall, precision, and the F–score.
Consistency and permission in deontic justification logic

Abstract Different notions of the consistency of obligations collapse in standard deontic logic. In justification logics, which feature explicit reasons for obligations, the situation is different. Their strength depends on a constant specification and on the available set of operations for combining different reasons. We present different consistency principles in justification logic and compare their logical strength. We propose a novel semantics for which justification logics with the explicit version of axiom D, $\textbf {jd}$, are complete for arbitrary constant specifications. Consistency is sometimes formulated in terms of permission. We therefore study permission in the context of justification logic, introducing a notion of free-choice permission for the first time. We then discuss the philosophical implications with regard to some deontic paradoxes.
A multi-label feature selection method based on an approximation of interaction information

High-dimensional multi-label data is widespread in practical applications, which brings great challenges to the research field of pattern recognition and machine learning. Many feature selection algorithms have been proposed in recent years, among which the filtering feature selection algorithm is the most popular one because of its simplicity. Therefore, filtering feature selection has become a hot research topic, especially the multi-label feature selection algorithm based on mutual information. In the algorithm, the computation cost of high dimensional mutual information is expensive. How to approximate high order mutual information based on low order mutual information has become a major research direction. To our best knowledge, all existing feature selection algorithms that consider the label correlation will increase the computational cost greatly. Therefore, this paper proposes an approximation method of three-dimensional interaction information, which is applied to the calculation of correlation and redundancy. It can take the correlation of labels into account and don’t increase the computation cost significantly at the same time. Experiments analysis results show that the proposed method is effective.
Hierarchical classification based on coarse- to fine-grained knowledge transfer

Hierarchical classification has become one of the most popular research topics because the scale of data has increased exponentially. Top-down hierarchical classification is an effective classification method using the hierarchical class structure as important side information. However, inter-level error propagation is a crucial problem in the top-down strategy. In this paper, we propose a hierarchical classifier based on deep branch convolutional neural networks, which achieves hierarchical classification based on coarse- to fine-grained knowledge transfer. Specifically, we use a deep convolutional neural network to extract image rough and detailed features from shallow and deep networks. We then embed the branch network at different depths of the convolutional network for hierarchical classification. We splice features from the previous branch network to the current branch. It alleviates inter-level error propagation by knowledge transfer from coarse- to fine-grained. Experimental results on four datasets show that the proposed method outperforms nine popular classifiers for hierarchical classification. Especially on the CIFAR10 dataset, our method is about 5% better than the second-best method.
Multi-population modelling and forecasting life-table death counts

When modelling the age distribution of death counts for multiple populations, we should consider three features: (1) how to incorporate any possible correlation among multiple populations to improve point and interval forecast accuracy through multi-population joint modelling; (2) how to forecast age distribution of death counts so that the forecasts are non-negative and have a constrained integral; (3) how to construct a prediction interval that is well-calibrated in terms of coverage. Within the framework of compositional data analysis, we apply a log-ratio transform to transform a constrained space into an unconstrained space. We apply multivariate and multilevel functional time series methods to forecast period life-table death counts in the unconstrained space. Through the inverse log-ratio transformation, the forecast period life-table death counts are obtained. Using the age-specific period life-table death counts in England and Wales and Sweden obtained from the Human Mortality Database (2022), we investigate one-step-ahead to 30-step-ahead point and interval forecast accuracies of the proposed models and make our recommendations.
Few-shot object detection with semantic enhancement and semantic prototype contrastive learning

Few-shot object detection (FSOD), which aims to teach machines to detect objects belonging to novel classes via extremely few annotated data, has attracted extensive research interest. However, the performance of FSOD is still limited by the lack of data. Visual information of novel objects has significant intraclass variance under the few-shot setting, so single visual information cannot accurately represent the objects themselves. In contrast, humans are good at combining visual and semantic systems to recognize new concepts simultaneously. In this paper, we fully explore utilizing additional semantic knowledge to assist the FSOD task. Concretely, we first obtain the semantic representation of classes by the word embedding model learned from a large corpus of text. We then design a semantic enhancement (SE) module to enhance the incomprehensively visual representation of novel classes. To further improve the classification performance, we define a semantic prototype contrastive (SPC) loss to learn a more discriminative embedding space, where features to be detected belonging to the same class are compactly clustered around the corresponding semantic representation. Furthermore, we also introduce the semantic margin between different semantic representations for SPC loss to adaptively separate the margin between features belonging to different classes. Extensive experiments on the PASCAL VOC and MS-COCO benchmarks demonstrate that the proposed method achieves state-of-the-art performance.
Understanding the Challenges of Leveraging Information and Communications Technology in Education

This chapter will explore barriers to information and communication technology (ICT) adoption in the educational context. The first section of this chapter is a brief background to the subject of ICT in education. The second section will be a brief history of how educational theory has influenced ICT adoption in education. Section 3 will be a detailed literature review of barriers to technology adoption faced by educational administrators, instructional designers, educators, and learners. The final section introduces a case study of the Japanese context to illustrate the real-world challenges faced at the national level in providing the support needed to incorporate many of these technologies into an existing educational system.
Hybrid Method Based on Extreme Learning Machine for Fetal Electrocardiogram Extraction

A novel hybrid method on Fetal Electrocardiogram (FECG) extraction, which combined FastICA, Extreme Learning Machine (ELM) and adaptive comb filter (ACF), was proposed. Firstly, the baseline drift and other noise in raw maternal abdominal signals were suppressed utilizing conventional filtering method. Then the maternal electrocardiogram (MECG) estimation and FECG estimation containing residual MECG component were obtained from multi-channel maternal abdominal signals by FastICA. The non-linearly transform between MECG estimation and residual MECG component was fitted using ELM. By MECG estimation undergoing the non-linearly transform, the optimal estimation of residual MECG component was obtained. The noisy FECG was extracted by suppressing the estimated MECG component. At last, the FECG enhancement was performed utilizing ACF. The proposed FECG extraction method was evaluated on clinical data. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{SNR}_{\text{svd}},\text{SNR}_{\text{cor}}$</tex> , Se, PPV and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{F}_{1}$</tex> score of proposed hybrid FECG extraction method are 7.6560dB, 7.8415dB, 99.20%, 98.41% and 98.80%, respectively. The experiment results indicated it better than other conventional method.
N-FedAvg: Novel Federated Average Algorithm Based on FedAvg

As a safe and efficient distributed machine learning technology, federated learning has been widely used in all walks of life. However, various problems in the traditional FedAvg algorithm have brought a lot of inconvenience to the implementation of federated learning in related industries. Therefore, this paper addresses related problems by proposing a novel federated learning algorithm (N-FedAvg) based on FedAvg. N-FedAvg selects clients in sequence before each epoch, reducing the randomness when selecting clients, so that all clients' data can have the opportunity to participate in federated learning, and prevent local data of some clients from participating in the federation with a low probability. Also, when using gradient descent to optimize the objective function and approaching the global minimum of the loss function value, the learning rate should be made smaller to get the model as close as possible to this point, and cosine annealing can be achieved by the cosine function and produces good results. Finally, the sparsity strategy is essentially a model compression method, which not only transmits a small number of parameters, but also reduces the network bandwidth between the server and the clients, and can also prevent the leakage of global model parameters. Through comparative experimental analysis, this paper finds that the N-FedAvg algorithm proposed by us is 1.34% more accurate than the traditional FedAvg algorithm on the CIFAR-10 dataset, and the loss function value is 2.77% lower.
Metareasoning for Safe Decision Making in Autonomous Systems

Although experts carefully specify the high-level decision-making models in autonomous systems, it is infeasible to guarantee safety across every scenario during operation. We therefore propose a safety metareasoning system that optimizes the severity of the system's safety concerns and the interference to the system's task: the system executes in parallel a task process that completes a specified task and safety processes that each address a specified safety concern with a conflict resolver for arbitration. This paper offers a formal definition of a safety metareasoning system, a recommendation algorithm for a safety process, an arbitration algorithm for a conflict resolver, an application of our approach to planetary rover exploration, and a demonstration that our approach is effective in simulation.
Confidence-Based Robot Navigation Under Sensor Occlusion with Deep Reinforcement Learning

This paper considers the problem of prolonged occlusions on navigation sensors due to dust, smudges, soils, etc. Such uncontrollable occlusions often cause lower visibility as well as higher uncertainty that require considerably sophisticated behavior. To secure visibility (i.e., confidence about the world), we propose a confidence-based navigation method that encourages the robot to explore the uncertain region around the robot maximizing its local confidence. To effectively extract features from the variable size of sensor occlusions, we adopt a point-cloud based representation network. Our method returns a resilient navigation policy via deep reinforcement learning, autonomously avoiding collisions under sensor occlusions while reaching a goal. We evaluate our method in simulated and real-world environments with either static or dynamic obstacles under various sensor-occlusion scenarios. The experimental result shows that our method outperforms baseline methods under the highly occurring sensor occlusion, and achieves maximum 90% and 80% success rates in the tested static and dynamic environments, respectively.