Reviewerly/exHarmony huggingface dataset:
Exploring educational interactions in a pandemic situation through comics: an approach to the Spanish context

The Covid-19 pandemic has led to widespread changes in the ways teaching and learning take place under the pandemic. This has affected more significantly pre-service teachers, as they did not have the opportunity to experience their practicum period in a ‘normal’ context. Therefore, the aims of this paper are: (1) to explore how pre-service teachers experienced a period of uncertainty in teaching as a consequence of the Covid-19 pandemic; and (2) to explore how pre-service teachers imagined the teaching profession and the challenges they faced related to the pandemic. Results show that pre-service teachers believed that the educational relationship between teachers, the students and the subject is built through a personal relationship between teachers, students and the content to be taught. Results also demonstrate that the pandemic made it difficult for pre-service teachers to approach students and content, and create spaces of mutual trust between teacher and students.
How Catalytic Is Digital Technology in the Nexus between Migrants’ Remittance and Financial Development in Sub-Saharan African Countries?

Given the indisputable roles of remittance and financial development in countries’ economic performance, enhancing the nexus between the two variables has become pertinent. The remittance–financial development literature has surged, with a growing argument that making the relationship work is conditioned on mediating roles of certain economic indicators. Despite the overwhelming evidence of the transformative roles of digital technology, the assessment of its possible mediating role in the remittance–financial development nexus is lacking in the literature. Hence, using pooled data of 35 Sub-Saharan African (SSA) countries sourced from the World Bank’s Development Indicators, this study examined the mediating effect of digital technology in the relationship between remittance inflows and financial development. Using two indicators of ICT—fixed broadband and mobile cellular subscription—the results of the generalized method of moment analysis suggest that digital technology spurs remittance inflows to promote financial development in SSA. The results are consistent for both measures. These findings imply that remittance and digital technology are complementary in promoting financial development in the sub-region. Based on these outcomes, the study therefore advances the enactment of policies aimed at fostering diffusion of digital technology and achieving the sustainable development goal’s recommendation of lower transaction cost of remittances.
Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders

Explainable Artificial Intelligence (XAI) has gained significant attention in recent years due to concerns over the lack of interpretability of Deep Learning models, which hinders their decision-making processes. To address this issue, counterfactual explanations have been proposed to elucidate the reasoning behind a model’s decisions by providing what-if statements as explanations. However, generating counterfactuals traditionally involves solving an optimization problem for each input, making it impractical for real-time feedback. Moreover, counterfactuals must meet specific criteria, including being user-driven, causing minimal changes, and staying within the data distribution. To overcome these challenges, a novel model-agnostic approach called Real-Time Guided Counterfactual Explanations (RTGCEx) is proposed. This approach utilizes autoencoders to generate real-time counterfactual explanations that adhere to these criteria by optimizing a multiobjective loss function. The performance of RTGCEx has been evaluated on two datasets: MNIST and Gearbox, a synthetic time series dataset. The results demonstrate that RTGCEx outperforms traditional methods in terms of speed and efficacy on MNIST, while also effectively identifying and rectifying anomalies in the Gearbox dataset, highlighting its versatility across different scenarios.
Spatial Analysis of Structure and Metal Mineralization Based on Fractal Theory and Fry Analysis: A Case Study in Nenjiang−Heihe Metallogenic Belt

Regional tectonics can provide excellent transport channels and precipitation sites for mineralized hydrothermal fluid. Studying the spatial relationship and distribution trends of regional tectonics and metal mineralization has theoretical and practical significance for revealing regional mineralization regularities and guiding mineral exploration. This study considers the Nenjiang-Heihe metallogenic belt, through the fractal box dimension method and Fry analysis, to explore the spatial distribution characteristics and patterns of tectonics. The results were as follows. (1) NE and NW directions are the main tectonic directions in the study area, with high-density areas concentrated in the central-eastern and central-western regions, demonstrating an overall ring-like distribution pattern. (2) Fractal dimensions of the linear structures of the NE and NW directions and the entire study area are 1.543, 1.493, and 1.622, respectively, with a strong coupling relationship between the lineament fractal high-value area and rhombic-grid spatial distribution of known deposits. (3) Gold mineralization shows the NEE and NWW directions as two main mineralization trends; the intersection area is the gold-potential area. The main trend direction of the Cu-Mo metallogenic trend belt is the NNW direction; the intersection area with the NEE direction gold metallogenic trend belt is the Au-Cu-Mo potential mineralization area.
Testing and CA

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Analysis of Passives in English-to-Korean Machine Translation Systems’ Output*

Artificial intelligence (AI)-based tools for teaching and learning English as a foreign language have become increasingly functional, leading to the growing use of AI translators and chatbots in fields of English education and research. This study investigated the potential of English-to-Korean machine translators (MTs) as media of interactive language learning to support Korean EFL learners’ reading comprehension development. Using translation data from research articles on English passives involving NP movement, this study evaluated how well two popular MTs could translate English passive sentences into Korean. This study analyzed MTs’ output in two ways: (i) whether the product’s meaning was accurate, and (ii) what type of Korean passive the product was. First, it calculated rates of semantically accurate and grammatically appropriate translations, both actives and passives produced by the two MTs. Second, it calculated rates of three different types of Korean passives produced by MTs. Results demonstrated that currently available English-to-Korean MTs could produce semantically correct translations at high rates in the form of Korean passives. Active voice constructions also showed significant rates. These findings suggest that MTs have strong potential as tools for English reading pedagogy.
AI펭톡 활용 수업이 초등학생 영어 발음 능력 향상과 정의적 영역에 미치는 효과

The aim of this study was to investigate effects of English classes using AI PengTalk on English pronunciation ability of elementary students and affective factors of English pronunciation. Forty-two participants (5th graders) were divided into experimental (n = 21) and control (n = 21) groups. Their pronunciation scores of pre-and post-pronunciation tests focusing on evaluating segmentals, intonation, stress and rhythm, and speech rate were quantitatively analyzed. Data from a questionnaire about affective factors of English pronunciation including a journal writing were quantitatively and qualitatively analyzed. Findings of this study indicated that: 1) there were statistically significant differences in English pronunciation abilities between the two groups, 2) there were also statistically significant differences in affective factors such as self-confidence, interests, and attitudes, and 3) students indicated in their responses to open-ended questions and their selfevaluation journal that AI PengTalk classes helped them speak English well and enhanced their self-confidence, interests, and positive attitudes in their pronunciation. These results suggest that AI PengTalk need to be actively used in schools as a good tool for developing students’ English pronunciation abilities and that primary school teachers should focus on their pronunciation teaching for promoting students’ self-confidence, interests, and attitudes towards pronunciation learning.
An ensemble-based stegware detection system for information hiding malware attacks

Information hiding techniques like steganography are used by hackers to obfuscate malicious attack codes to carry malware scripts and deliver to crypto-miners in on-demand platforms like Cloud. Stegware is a type of information-hiding malware that employs steganography to avoid detection by modern malware detection systems. This work proposes a Stegware detection system that recognizes obfuscated payloads from input images and verifies whether the obfuscated payload is the target of any stegware attack. The proposed system detects the stegware in four phases: Obfuscated Payload Detection phase that detects the presence of any obfuscated item concealed inside the digital medium; Obfuscated Payload Extraction phase that decodes stego-repository images to extract the data that are steganographically obfuscated inside the input; Obfuscated Payload Classification phase that detects whether the extracted data is legitimate or malicious stegware file, using binary classifier; Calculation of malicious percentage phase that uses fuzzy C-means clustering algorithm to calculate the quantum and frequency of malicious activities. The proposed system is experimentally tested on real dataset and analyzed with existing models. The simulation results illustrate that the proposed Stegware detection system detects the steganographically-hidden attacks and identifies malicious activities in percentage terms as compared to other models.
Random Telegraph Noise-Based True Random Number Generator for Fully Integrated Systems

Generating streams of true random numbers is a critical component of many electronic and information systems. The design of fully integrated, area and power efficient true random number generators (TRNGs) is a challenge. We propose a fully integrated, lightweight implementation that uses the random telegraph noise (RTN) of standard MOSFET as entropy source. It is not analog-intensive, and without traditional post-processing algorithms, the generated random bit sequence passes the National Institute of Standards and Technology (NIST) tests.
Reparametric Sampling for Small Object Image Recognition

It is difficult to balance detection speed and detection accuracy in current methods for sampling on feature maps. We combine the parameterless interpolation algorithm used in the traditional method with the algorithm in deep learning to improve shortcomings of the traditional interpolation algorithm by introducing learnable parameters into the algorithm. The new algorithm guarantees the features of the original image by calculating the correlation between feature map channels and pixels to ensure the correct restoration of the image features during up-sampling. In this paper, we propose a new up-sampling operation model — Reparametric Blend Up-sampling (RBU), which uses the method of rearranging pixels to obtain a feature map that matches the up-sampling size, uses blending to compute inter-pixel and inter-channel correlations and back-propagates the model to learn the appropriate parameters to ensure feature extraction. In the field of object detection, this model replaces the original up-sampling operation and improves the mAP mean value by 1.1% and the detection of small target objects by 3.1% in the VOC dataset with the richest application scenarios.
An Explainable AI Model for ICU Admission Prediction of COVID-19 Patients

COVID-19 has overwhelmed hospitals all around the world, because of the very high number of patients requiring intubation. Predicting and anticipating which patients will be intubated can help hospitals better allocate their resources. For this, we created an explainable AI model that receives some medical data for each patient and returns a prediction on whether they will be intubated later on, while also explaining the argumentation process that led to that conclusion. This model can help medical professionals better manage the ICU beds and the patients between hospitals and warn them on the symptoms and signs to look for in patients worsening. To obtain the model, we first trained various ML algorithms on a dataset of COVID ICU admissions. Then, using the most accurate of the models, we obtained the sum of the most important rules, using R’s inTrees framework. With the rules obtained, we produced a program in Gorgias written in Prolog. We added some more rules and changed the ordering to come up with a program that requires some patient vitals and blood results as inputs and then returns the prediction along with its reasoning.
Phase-shifting by polarizer rotations in a right-angle prism interferometer

In this manuscript it is presented what we believe to be a novel and simple phase-shifting technique based on polarizer rotations, using a right angle prism as the interferometric device. It is possible to generate interference when the right angle edge of the prism is placed parallel to a linearly polarized collimated beam. In this configuration optical phase changes are presented due to the internal reflections in the prism; therefore, a linearly polarized beam turns into elliptical polarization. If the output elliptically polarized interference beam passed through a lineal polarizer, phase-shifting is generated if it rotates by an angle theta. This proposal will be sustained with simulated and experimental shifted interference patterns and with the calculation of wrapped and unwrapped phases.
USQA: A User Story Quality Analyzer prototype for supporting software engineering students

Abstract The Standish Group Reports 83.9% of IT Projects fail, and one of the top factors in failed projects is the incomplete requirements or user stories. Therefore, it is essential to teach undergraduate students from computer science degree programs how to create complete user stories. Computer science programs include some subjects or topics involving requirements or user stories collection and writing, such as Requirements Engineering, Software Engineering, Project Management, or Quality Software Assurance. For that reason, we designed a web application called User Story Quality Analyzer (USQA) that uses Natural Language Processing modules to detect errors regarding aspects of usefulness, completeness, and polysemes in the user stories creation. The tool was proved from three perspectives: (1) a reliability test, where 35 user stories developed by experts were tested in the app to prove the prototype's reliability; (2) usability and utility analysis; 48 students interacted with the tool and responded a Satisfaction Usability Scale and an open‐ended question, the students reported a high usability score; (3) finally, error classification, we gathered 159 user stories processed by the system, and we classified the students' common errors considering incompleteness and polysemes. After the evaluations, we concluded that USQA could evaluate the user stories as an expert, which could help the professors/teachers/instructors in their courses by providing feedback to the students when they are writing user stories.
Compression of Deep-Learning Models Through Global Weight Pruning Using Alternating Direction Method of Multipliers

Abstract Deep learning has shown excellent performance in numerous machine-learning tasks, but one practical obstacle in deep learning is that the amount of computation and required memory is huge. Model compression, especially in deep learning, is very useful because it saves memory and reduces storage size while maintaining model performance. Model compression in a layered network structure aims to reduce the number of edges by pruning weights that are deemed unnecessary during the calculation. However, existing weight pruning methods perform a layer-by-layer reduction, which requires a predefined removal-ratio constraint for each layer. Layer-by-layer removal ratios must be structurally specified depending on the task, causing a sharp increase in the training time due to a large number of tuning parameters. Thus, such a layer-by-layer strategy is hardly feasible for deep layered models. Our proposed method aims to perform weight pruning in a deep layered network, while producing similar performance, by setting a global removal ratio for the entire model without prior knowledge of the structural characteristics. Our experiments with the proposed method show reliable and high-quality performance, obviating layer-by-layer removal ratios. Furthermore, experiments with increasing layers yield a pattern in the pruned weights that could provide an insight into the layers’ structural importance. The experiment with the LeNet-5 model using MNIST data results in a higher compression ratio of 98.8% for the proposed method, outperforming existing pruning algorithms. In the Resnet-56 experiment, the performance change according to removal ratios of 10–90% is investigated, and a higher removal ratio is achieved compared to other tested models. We also demonstrate the effectiveness of the proposed method with YOLOv4, a real-life object-detection model requiring substantial computation.
Sampling technique for Fourier convolution theorem based k‐space filtering

Abstract This research presents a sampling technique for Fourier convolution theorem (FCT) based k‐space filtering. One polynomial function and three transfer functions were selected: (1) Gaussian, (2) Bessel, (3) Butterworth, and (4) Chebyshev. The functions were sampled on the image grid, and they are called filtering functions. Each filtering function was multiplied by the Sinc function to obtain the “ Sinc‐shaped convolving function .” The k‐space of the Sinc‐shaped convolving function is calculated by direct Fourier transform (FT) and it is featured by a central region. This central region of the k‐space is rectangular in its shape because it is consequential to the direct FT of the product between the filtering function and the Sinc function. Low‐pass and high‐pass filtering is obtained by inverse FT of the pointwise multiplication between the k‐space of the departing image and the k‐space of the Sinc‐shaped convolving function. A variety of cut‐off frequencies, bandwidth, sampling rates, and numbers of poles of the filters were verified as effective to filter the images. Filtering strength can be modified by fine‐tuning the size of the central rectangular k‐space region of the Sinc‐shaped convolving functions. K‐space analysis of departing images and filtered images provide additional evidence of effective filtering. Moreover, k‐space filtering was compared to Z‐space filtering using the extension of the FCT to Z‐space. The novelty of this research is the sampling technique used to determine the Sinc‐shaped convolving function. The sampling technique uses fine‐tuning of bandwidth and sampling rate to determine the strength of the k‐space filter.
The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA

The adoption of deep neural networks for profiling side-channel attacks opened new perspectives for leakage detection. Recent publications showed that cryptographic implementations featuring different countermeasures could be broken without feature selection or trace preprocessing. This success comes with a high price: an extensive hyperparameter search to find optimal deep learning models. As deep learning models usually suffer from overfitting due to their high fitting capacity, it is crucial to avoid over-training regimes, which require a correct number of epochs. For that, early stopping is employed as an efficient regularization method that requires a consistent validation metric. Although guessing entropy is a highly informative metric for profiling side-channel attacks, it is time-consuming, especially if computed for all epochs during training, and the number of validation traces is significantly large. This paper shows that guessing entropy can be efficiently computed during training by reducing the number of validation traces without affecting the efficiency of early stopping decisions. Our solution significantly speeds up the process, impacting the performance of the hyperparameter search and overall profiling attack. Our fast guessing entropy calculation is up to 16× faster, resulting in more hyperparameter tuning experiments and allowing security evaluators to find more efficient deep learning models.
Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement

In this paper, we propose a sequential variational autoencoder for video disentanglement, which is a representation learning method that can be used to separately extract static and dynamic features from videos. Building sequential variational autoencoders with a two-stream architecture induces inductive bias for video disentanglement. However, our preliminary experiment demonstrated that the two-stream architecture is insufficient for video disentanglement because static features frequently contain dynamic features. Additionally, we found that dynamic features are not discriminative in the latent space. To address these problems, we introduced an adversarial classifier using supervised learning into the two-stream architecture. The strong inductive bias through supervision separates dynamic features from static features and yields discriminative representations of the dynamic features. Through a comparison with other sequential variational autoencoders, we qualitatively and quantitatively demonstrate the effectiveness of the proposed method on the Sprites and MUG datasets.
Deep Learning Approach for Multi-class Semantic Segmentation of UAV Images

Image understanding plays a very crucial role in remote sensing applications. For this, image semantic segmentation is one of the approaches where each pixel of an image is assigned to particular classes based on various features. Aerial semantic segmentation suffers from the class imbalance problem. Proper differentiation of least represented categories is challenging and a goal for the state-of-art approach. In this work, we present a novel deep learning method to perform this task. We proposed a lightweight encoder-decoder network residual depth separable UNet (RDS-UNet) and conditional random field for effective segmentation on very high-resolution aerial images. We proposed patch-with-multi-class sampling to handle the class imbalance problem without increasing the computational overhead during the training process. We created a semi-precise annotated UAV dataset named NESAC UAV Seg for the aerial semantic segmentation task. We demonstrated the efficacy of our model using the publicly available benchmark Drone Deploy dataset and our NESAC UAV Seg dataset. Our model required approximately half the number of trainable parameters and floating point operations compared to other methods. A detailed ablation study is presented to showcase the effectiveness of various modules utilized in our network.