Reviewerly/exHarmony huggingface dataset:
Developing an optimized recurrent neural network model for air quality prediction using K-means clustering and PCA dimension reduction

Prediction is a means of forecasting a future value by using and analyzing historical or current data. A popular neural network architecture used as a prediction model is the Recurrent Neural Network (RNN) because of its wide application and very high generalization performance. This study aims to improve the RNN prediction model’s accuracy using k-means grouping and PCA dimension reduction methods by comparing the five distance functions. Data were processed using Python software and the results obtained from the PCA calculation yielded three new variables or principal components out of the five examined. This study used an optimized RNN prediction model with k-means clustering by comparing the Euclidean, Manhattan, Canberra, Average, and Chebyshev distance functions as a measure of data grouping similarity to avoid being trapped in the local optimal solution. In addition, PCA dimension reduction was also used in facilitating multivariate data analysis. The k-means grouping showed that the most optimal distance is the average function producing a DBI value of 0.60855 and converging at the 9th iteration. The RNN prediction model results evaluated based on the number of RMSE errors which was 0.83, while that of MAPE was 8.62%. Therefore, it was concluded that the K-means and PCA methods generated a more optimal prediction model for the RNN method.
Automatic feedback and assessment of team-coding assignments in a DevOps context

Abstract We describe an automated assessment process for team-coding assignments based on DevOps best practices. This system and methodology includes the definition of Team Performance Metrics measuring properties of the software developed by each team, and their correct use of DevOps techniques. It tracks the progress on each of metric by each group. The methodology also defines Individual Performance Metrics to measure the impact of individual student contributions to increase in Team Performance Metrics. Periodically scheduled reports using these metrics provide students valuable feedback. This process also facilitates the process of assessing the assignments. Although this method is not intended to produce the final grade of each student, it provides very valuable information to the lecturers. We have used it as the main source of information for student and team assessment in one programming course. Additionally, we use other assessment methods to calculate the final grade: written conceptual tests to check their understanding of the development processes, and cross-evaluations. Qualitative evaluation of the students filling relevant questionnaires are very positive and encouraging.
Data processing pipeline for cardiogenic shock prediction using machine learning

Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS.We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)-based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction.We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization.We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.
Robust Face Alignment via Inherent Relation Learning and Uncertainty Estimation

Human tends to locate the facial landmarks with heavy occlusion by their relative position to the easily identified landmarks. The clue is defined as the landmark inherent relation while it is ignored by most existing methods. In this paper, we present Dynamic Sparse Local Patch Transformer (DSLPT), a novel face alignment framework for the inherent relation learning and uncertainty estimation. Unlike most existing methods that regress facial landmarks directly from global features, the DSLPT first generates a rough representation of each landmark from a local patch cropped from the feature map and then adaptively aggregates them by a case dependent inherent relation. Finally, the DSLPT predicts the coordinate and uncertainty of each landmark by regressing their probability distribution from the output features. Moreover, we introduce a coarse-to-fine framework to incorporate with DSLPT for an improved result. In the framework, the position and size of each patch are determined by the probability distribution of the corresponding landmark predicted in the previous stage. The dynamic patches will ensure a fine-grained landmark representation for inherent relation learning so that a rough prediction result can gradually converge to the target facial landmarks. We integrate the coarse-to-fine model into an end-to-end training pipeline and carry out experiments on the mainstream benchmarks. The results demonstrate that the DSLPT achieves state-of-the-art performance with much less computational complexity. The codes and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Jiahao-UTS/DSLPT</uri> .
Social media marketing activities and tourists’ purchase intention

This study aims to examine the influence of brand image as a mediator between social media marketing activity and tourist intentions to visit tourist sites in Jordan. To test the hypotheses, we utilized the partial least square method and distributed a questionnaire survey to 400 visitors, receiving 289 responses for analysis using SMART-PLS4 software. The study made significant contributions to the literature on social media marketing by developing a research model that links social media usage to brand image and shopping intention, and by using statistical techniques to quantitatively assess the effect of social media marketing activity on the brand image as a mediator. The findings exposed that word of mouth, a construct of social media, significantly positively influenced tourists' purchase intention to visit sites in Jordan. This study sheds light on the importance of social media marketing and its impact on the tourism industry in Jordan, providing useful insights for practitioners and policymakers in the field.
The moderating role of reliability on the relationship between electronic word of mouth and cus-tomer purchase intention in Jordanian real estate enterprises

Jordanian real estate enterprises are experiencing difficult market circumstances and an increasingly competitive environment; following these issues, this study examines the leverage of electronic word of mouth (e-WOM) on customer purchase intention in a Jordanian context with specific reference to real estate enterprises, considering the moderating role of reliability. Based on earlier studies, a conceptual model for the study was created. This research includes e-WOM as an independent variable affecting customers' purchase intention as a dependent variable mediated by reliability. The investigation follows descriptive-analytical methods; based on a convenience sampling approach, 300 questionnaires were distributed through Google Forms; nonetheless and 250 responses were accepted. To analyze data and assess hypotheses, a structural Equation Modeling (SEM) using PLS was employed. Results demonstrated a significant effect of e-WOM and Reliability on purchasing intent of customers, and the moderating role of reliability in the relationship between e-WOM and purchase intent was also affirmed. The findings give Jordanian real estate businesses information on ways to use that are most effective e-WOM to persuade buyers to buy.
Distance learning technologies in a higher educational institution: experience in using and implementing e-learning courses in the educational process

Distance learning technologies occupy a strong position in the higher education system: they can serve as an instrumental basis for organizing distance learning, they can be actively used in combination with traditional forms and methods of teaching, and they can be used to build networking between universities and employer organizations. There is much experience in using distance learning technologies by university teachers when working with students, which needs to be rethought.&#x0D; The purpose of this study was to show how an educational platform, such as the S. Kuznets KNEU Personal Learning Systems, can be used to provide educational services in the face of unpredictable barriers in the current circumstances in Ukraine to substantiate the importance and feasibility of developing and implementing distance learning courses on this platform to increase the efficiency of distance learning.&#x0D; The study reveals the importance of using distance learning technologies in the educational process. Distance learning offers many advantages, such as geographical diversity, efficiency, continuity, individual approach and personalized program. The article analyzes the experience of using distance learning technologies on the example of Simon Kuznets Kharkiv National University of Economics. The existing difficulties of using distance learning technologies are specified. The algorithm for using the Moodle distance learning system as a tool environment for developing online courses by teachers, distinguished by its functionality, flexibility, reliability and ease of use, is prescribed.&#x0D; However, Ukraine's education system must catch up with the fundamentals underlying distance learning courses. It is necessary to train highly qualified teaching staff capable of working with students remotely to ensure the effectiveness of distance learning, provide students with updated learning materials, and make courses comprehensive, engaging, and easy to understand. However, the prospects in this area of education in Ukraine are prominent
Evaluation of Software Quality in Test-driven Development: A Perspective of Measurement and Metrics

A software product is expected to be subjected to critical evaluation on its quality attributes in order to ascertain that target quality requirements are met, and that those quality attributes responsible for revealing software quality are not omitted in the software development process. Software metrics are essential to accomplish the task. This paper has carried out an exploratory study of software measurement and software metrics in tandem. The study took into cognizance the interwoven nature of the duo in measuring and revealing software quality. The study formulated a model that expressed the mutual bonding that propels both measurement and metrics to describing software quality in numeric quantities of software attributes. The study identified six software attributes whose values are considered enough quantities to reveal the quality of a software product. The identification enabled the study to create a model equation aimed at giving a numeric value for the complete evaluation of a software system. The result of the implementation of the six software attributes into the model equation showed that two software products employed in the study are of high-quality, having quality values of 0.93 and 0.86 respectively. The attributes produced values that confirmed the maintainability (25 seconds &amp; 20 seconds respectively) and reliability (0.78 &amp; 0.80 respectively) of both software products among other differing features that characterize them.
Indonesian Hate Speech Detection Using Bidirectional Long Short-Term Memory (Bi-LSTM)

Abstract &#x0D; Social media is a platform that allows users to express themselves freely including spreading hate speech content. The government has issued the regulation in the UU ITE to handle and prevent hate speech on social media. The research was also conducted using the Bi-LSTM to classify the text into hate speech or not. Another research was purposed to detect hate speech and its categories using Bi-GRU. However, the performance of the model Bi-GRU is still lower than Bi-LSTM with an accuracy of 86.44% and 96.44%. Therefore, this study aims to build a model that can detect hate speech and its categories. The research offers Bi-LSTM as a classification model and IndoBERT as a tokenization model. The dataset used is a public dataset containing 13 thousand tweets. As a result, the best model obtained is using 20 epochs, 192 batch sizes, 1 layer Bi-LSTM with 40 nodes, and applying class weighing in the optimization process. The pre-train model from IndoBERT that is used to support the performance of the model in classifying is "indobenchmark/indobert-large-p2". The performance given by the purposed model is very good with an average accuracy, precision, and recall of 97.66%, 96.50%, and 85.25%.
Optical flow algorithms optimized for speed, energy and accuracy on embedded GPUs

Embedded computer vision is a hot field of research that requires trade-offs in order to balance execution time, power consumption and accuracy. In that field, dense optical flow estimation is a major tool used in many applications. Many algorithms have been designed, focusing on accuracy, very few works address trade-offs and implementation on embedded hardware. This paper tackles these trade-offs for embedded GPU through the example of the well-known TV-L $$^{1}$$ algorithm. Thanks to High Level Transforms—operator fusion and pipeline—and taking into account the iterative aspect of these algorithms, we achieve a speedup of $$\times \, 3.7$$ versus OpenCV. Moreover, we show that a 16-bit half precision implementation has a higher accuracy than the 32-bit precision one for the same frame processing time on NVIDIA Jetson boards. Furthermore, this work can be generalized to any kind of iterative stencil-based algorithms.
RU-Net: skull stripping in rat brain MR images after ischemic stroke with rat U-Net

Abstract Background Experimental ischemic stroke models play a fundamental role in interpreting the mechanism of cerebral ischemia and appraising the development of pathological extent. An accurate and automatic skull stripping tool for rat brain image volumes with magnetic resonance imaging (MRI) are crucial in experimental stroke analysis. Due to the deficiency of reliable rat brain segmentation methods and motivated by the demand for preclinical studies, this paper develops a new skull stripping algorithm to extract the rat brain region in MR images after stroke, which is named Rat U-Net (RU-Net). Methods Based on a U-shape like deep learning architecture, the proposed framework integrates batch normalization with the residual network to achieve efficient end-to-end segmentation. A pooling index transmission mechanism between the encoder and decoder is exploited to reinforce the spatial correlation. Two different modalities of diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) corresponding to two in-house datasets with each consisting of 55 subjects were employed to evaluate the performance of the proposed RU-Net. Results Extensive experiments indicated great segmentation accuracy across diversified rat brain MR images. It was suggested that our rat skull stripping network outperformed several state-of-the-art methods and achieved the highest average Dice scores of 98.04% (p &lt; 0.001) and 97.67% (p &lt; 0.001) in the DWI and T2WI image datasets, respectively. Conclusion The proposed RU-Net is believed to be potential for advancing preclinical stroke investigation and providing an efficient tool for pathological rat brain image extraction, where accurate segmentation of the rat brain region is fundamental.
A Lossless Audio Encryption Method based on Chebyshev Map

This paper presents a lossless and secure audio encryption method based on the chaotic Chebyshev map. Firstly, the input audio samples are preprocessed to obtain the integer and decimal parts. The integer parts are rescaled to the interval [0,255]. By iterating the Chebyshev map in the chaotic range using plaintext-dependent variables, the integer parts of the input audio sample are scrambled and then diffused. Finally, a post-processing operation is applied to the diffused audio samples. Keyspace and key sensitivity analysis, histogram analysis, the correlation between adjacent samples analysis, information entropy analysis, number of sample change rate analysis, and speed analysis results are presented. These security analysis results show that the proposed audio encryption method can be used in secure voice transmission applications.
Bayesian learning of parameterised quantum circuits

Abstract Currently available quantum computers suffer from constraints including hardware noise and a limited number of qubits. As such, variational quantum algorithms that utilise a classical optimiser in order to train a parameterised quantum circuit have drawn significant attention for near-term practical applications of quantum technology. In this work, we take a probabilistic point of view and reformulate the classical optimisation as an approximation of a Bayesian posterior. The posterior is induced by combining the cost function to be minimised with a prior distribution over the parameters of the quantum circuit. We describe a dimension reduction strategy based on a maximum a posteriori point estimate with a Laplace prior. Experiments on the Quantinuum H1-2 computer show that the resulting circuits are faster to execute and less noisy than the circuits trained without the dimension reduction strategy. We subsequently describe a posterior sampling strategy based on stochastic gradient Langevin dynamics. Numerical simulations on three different problems show that the strategy is capable of generating samples from the full posterior and avoiding local optima.
GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model

The detection of a fallen person (FPD) is a crucial task in guaranteeing individual safety. Although deep-learning models have shown potential in addressing this challenge, they face several obstacles, such as the inadequate utilization of global contextual information, poor feature extraction, and substantial computational requirements. These limitations have led to low detection accuracy, poor generalization, and slow inference speeds. To overcome these challenges, the present study proposed a new lightweight detection model named Global and Local You-Only-Look-Once Lite (GL-YOLO-Lite), which integrates both global and local contextual information by incorporating transformer and attention modules into the popular object-detection framework YOLOv5. Specifically, a stem module replaced the original inefficient focus module, and rep modules with re-parameterization technology were introduced. Furthermore, a lightweight detection head was developed to reduce the number of redundant channels in the model. Finally, we constructed a large-scale, well-formatted FPD dataset (FPDD). The proposed model employed a binary cross-entropy (BCE) function to calculate the classification and confidence losses. An experimental evaluation of the FPDD and Pascal VOC dataset demonstrated that GL-YOLO-Lite outperformed other state-of-the-art models with significant margins, achieving 2.4–18.9 mean average precision (mAP) on FPDD and 1.8–23.3 on the Pascal VOC dataset. Moreover, GL-YOLO-Lite maintained a real-time processing speed of 56.82 frames per second (FPS) on a Titan Xp and 16.45 FPS on a HiSilicon Kirin 980, demonstrating its effectiveness in real-world scenarios.
A Deep Learning Approach for Predicting Multiple Sclerosis

This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.
MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning

Evolutionary ensemble learning methods with Genetic Programming have achieved remarkable results on regression and classification tasks by employing quality-diversity optimization techniques like MAP-Elites and Neuro-MAP-Elites. The MAP-Elites algorithm uses dimensionality reduction methods, such as variational auto-encoders, to reduce the high-dimensional semantic space of genetic programming to a two-dimensional behavioral space. Then, it constructs a grid of high-quality and diverse models to form an ensemble model. In MAP-Elites, however, variational auto-encoders rely on Euclidean space topology, which is not effective at preserving high-quality individuals. To solve this problem, this paper proposes a principal component analysis method based on a cosine-kernel for dimensionality reduction. In order to deal with unbalanced distributions of good individuals, we propose a zero-cost reference points synthesizing method. Experimental results on 108 datasets show that combining principal component analysis using a cosine kernel with reference points significantly improves the performance of the MAP-Elites evolutionary ensemble learning algorithm.
EAF-WGAN: Enhanced Alignment Fusion-Wasserstein Generative Adversarial Network for Turbulent Image Restoration

Because of optical distortion induced by atmospheric turbulence and the limitations of optical devices, acquired images of space objects are blurred and degraded. This effect results in anamorphosis in the output of two-dimensional imaging systems. In this work, we present a novel Enhanced Alignment Fusion-Wasserstein Generative Adversarial Network, called EAF-WGAN, for turbulent image restoration. This model is characterized by the innovative use of two modules, including the Align Module (AM) and the Feature Fusion Module (FFM) in the generator, especially in the process of feature fusion, in which 3D convolution is used. Through 3D convolution, the temporal and spatial information of the input image is obtained. Therefore, the formation mode and intensity of turbulence are not considered and the image can be reconstructed. The ability of EAF-WGAN is proved by algorithmically simulated data, physically simulated data, and real-world data.