Reviewerly/exHarmony huggingface dataset:
Animated educational video using health belief model on the knowledge of anemia prevention among female adolescents: An intervention study

As the younger female generation, female adolescents should understand anaemia prevention. This study examined the effects of animated educational videos on the knowledge of anaemia prevention among female adolescents using the Health Belief Model (HBM).A quasi-experimental method with a randomised pre-test and post-test control group design was applied. Animated educational videos about anaemia prevention were used as the intervention. One hundred sixty-one female adolescents were recruited through multistage random sampling and divided into intervention (n=78) and control (n=83) groups. The intervention group received education via animated educational videos. The HBM questionnaire was used to measure the nine HBM indicators (r=0.8); the item categories were valid and reliable. Descriptive analyses, independent t-tests and repeated-measures ANOVA were used to analyse the data.The animated educational videos played thrice significantly increased the knowledge of the intervention group (mean score: pre-test, 94; post-test one, 99; post-test two, 102). The scores for anaemia examination barriers (P=0.001), anaemia susceptibility (P=0.001), anaemia severity (P=0.001), anaemia prevention benefits (P=0.001), anaemia examination benefits (P=0.001), self-efficacy for obtaining iron tablets (P=0.001), self-recognition of anaemia signs and symptoms (P=0.001), signs of anaemia prevention (P=0.001) and health motivation (P=0.001) significantly changed. Meanwhile, the knowledge of the control group did not significantly increase (pre-test, 93; post-test one, 94; post-test two, 97). The intervention group had significantly higher mean scores in both the first and second measurements than the control group (P=0.05).Animated educational videos significantly increased the knowledge of anaemia prevention, including the nine HBM indicators.
A Coq Formalization of Lebesgue Induction Principle and Tonelli’s Theorem

Lebesgue integration is a well-known mathematical tool, used for instance in probability theory, real analysis, and numerical mathematics. Thus, its formalization in a proof assistant is to be designed to fit different goals and projects. Once the Lebesgue integral is formally defined and the first lemmas are proved, the question of the convenience of the formalization naturally arises. To check it, a useful extension is Tonelli’s theorem, stating that the (double) integral of a nonnegative measurable function of two variables can be computed by iterated integrals, and allowing to switch the order of integration. This article describes the formal definition and proof in Coq of product sigma-algebras, product measures and their uniqueness, the construction of iterated integrals, up to Tonelli’s theorem. We also advertise the Lebesgue induction principle provided by an inductive type for nonnegative measurable functions.
Is There "A Best" Centric Relation Record? Centric Relation Records, Condyle Positions, and Their Practical Significance

To investigate if and how the positions of condyles in centric relation (CR) varied from each other using different CR records.Condylar positions in CR were repeatedly recorded by four licensed clinicians in 81 stomatognathically healthy, fully dentate subjects with six different types of CR records. The maxillary casts were mounted in articulators after individual facebow transfer, and the mandibular casts were mounted with a central bearing point (CBP) registration on the tip of the Gothic arch. Mean values from three registrations of the centric condylar positions with each of the six methods were calculated, and comparative calculations were made.The median spatial distances of the condylar positions between the CBP and all other CR records was 0.73 ± 0.24 mm (0 to 3.19 mm). The median spatial values between the condylar positions of the different CR records ranged from 0.48 to 0.79 mm. Statistically, the positions of almost all types of registration were slightly different from each other.Different CR records reproduce slightly different positions of the mandible, which is especially true for CBP registration, whereas reproducibilities and SDs between registrations are similar. Unrefined wax-wafer registrations using bimanual manipulation have proven particularly successful with regard to precision and time requirements, as has CBP registration with regard to condylar position.
Sine Cosine Algorithm with Tangent Search for Neural Networks Dropout Regularization

Convolutional neural networks belong to the group of deep learning methods, largely influenced by the structure and functioning of the human brain. Their primary usage is to perform image classifying tasks. All neural networks, including convolutional neural networks, are susceptible to the overfitting issue, which can occur when the network exhibits good performance on the train data while failing to accurately predict the new data when it is fed to the inputs. Few regularization approaches exist that may help in avoiding the overfit. This paper proposes a novel swarm intelligence optimization method to address the overfitting problem by choosing the adequate dropout parameter value. Swarm algorithms have previously been used to optimize the structure of neural networks; however, the full potential of these algorithms has yet not been thoroughly investigated. Scientists must invest a lot of time to choose the appropriate dropout value if they execute this task manually. In this research, an automated framework, that uses improved sine cosine metaheuristics to perform this task, is proposed. The proposed framework was tested on four standard benchmark datasets, namely MNIST, CIFAR10, Semeion, and UPS. The simulation results have been validated against the results generated by several other state-of-the-art swarm intelligence algorithms. The comparison shows that the proposed method outperforms other cutting-edge algorithms in terms of classification error, therefore achieving higher accuracy percentage.
HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues – especially when multiple users issue concurrent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FL requests</i> . Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications.
An image compression model via adaptive vector quantization: hybrid optimization algorithm

The Vector Quantization technique, employed in this paper is based on an upgraded Linde–Buzo–Gray (LBG) with a DCT method to store the image data. The code-books are optimized using a unique approach called hybrid optimization. Here, the database image is divided into a series of blocks called vectors. Then, a suitable codeword is chosen as the closest approximation of the input vector. The encoder sends the compressed stream, i.e. indices of these vectors to the decoder. The decoder subsequently decodes the index to obtain the compressed vector. Moreover, the Flower Pollination Adapted Rider Optimization (FPARA) model is used to improve the codebook for providing best image compression result. The optimization of code books is done to lower the summation of compression ratio and the error difference between the original and decompressed images. Therefore, the adopted approach is computed to the traditional schemes, and the simulation outcomes are validated.
Learning attentive attribute-aware node embeddings in dynamic environments

Abstract Learning node embeddings is fundamental for numerous applications, such as link prediction and node classification. Node embeddings seek to learn a low-dimensional representation for each node in the graph. Many existing node representation learning methods for dynamic attributed graphs focus on preserving the temporal proximity of the nodes with relatively shallow models. However, real-life graphs are complex and usually exhibit evolutionary patterns of node attributes and graph structure. Therefore, the current state-of-the-art models fail to capture the information in the dynamic attributed graphs and settle for sub-optimal results. In this paper, we propose a novel model for embedding nodes in dynamic attributed graphs that captures the full extent of all relevant node information and the graph interrelations as well as graph evolutionary patterns. During model training, attribute-aware node embedding is learned using both graph and node properties in a dynamic context. Experiments demonstrate that our proposed method is superior to the state-of-the-art models in link prediction tasks. In addition, it introduces a novel way of learning richer representations by fully exploiting node attributes, graph structure, and evolutionary patterns in dynamic attributed graphs.
IoT and Artificial Intelligence Techniques for Public Safety and Security

The Internet of Things (IoT) is transforming how organizations and industries communicate and conduct their everyday operations. Its application has proven to be ideal for industries that manage large amounts of assets and coordinate complex and dispersed operations. An expert system must be able to defend and explain a judgment to a user in AI. Experts in information security must be able to explain why a system is secure to the general public. Artificial intelligence is also expected to play an important role in ensuring long-term public safety and security for growth. The different emerging sectors of IoT and traditional towns have been transformed into high-tech smart cities due to AI. The emergence of smart cities has just one goal: to raise people’s living standards by incorporating technology into their daily lives. This chapter discusses a number of significant technologies as well as solutions to problems that citizens face as a result of a lack of digitalization. It addresses concerns such as public infrastructure, public safety, and security and provides ideal solutions. It focuses not only on AI but also on IoT, 112machine learning, deep learning, pattern reorganization, and big data analytics for the development of a smart city that is completely functional. The analysis identifies the major barriers to widespread adoption and proposes a research path to address each of them in a cost-effective manner. IoT for defense and public safety.
Nonlinear semi-analytical uncertainty propagation for conjunction analysis

Gaussian Mixture Models and State Transition Tensors are combined to propagate state uncertainty to demonstrate an analytical and accurate future uncertainty and conjunction assessment. The initial uncertainty distribution is split into a Gaussian Mixture Model to reduce the size of covariance associated with each mixture component. This reduction in size allows for a significantly more accurate nonlinear mapping of each individual component. For higher accuracy in the mixture component propagation, State Transition Tensors are used to capture the nonlinear effects due to higher-order dynamics, as shown already in work by Fujimoto and Scheeres. In this paper, this GMM-STT method is used to analyze collisions using different dynamical models. The first case study demonstrates the application of this method in two-body problem dynamics; the second also adds J2 secular effects; and the third brings in full J2 and Solar Radiation Pressure dynamics through a Simplified Dynamical System. This paper also includes a two-body problem repeating conjunction example to establish a relationship between the propagation time and the number of mixture components needed for an accurate probability of collision computation. In each example, as the number of mixture components increases, the probability of collision converges with the Monte Carlo truth, proving the feasibility of this method.
Development of a wearable force-feedback mechanism for free-range haptic immersive experience

The recent rise in popularity of head-mounted displays (HMDs) for immersion into virtual reality has resulted in demand for new ways to interact with virtual objects. Most solutions utilize generic controllers for interaction within virtual environments and provide limited haptic feedback. We describe the construction and implementation of an ambulatory (allowing walking) haptic feedback stylus with primary use in computer-aided design. Our stylus is a modified 3D Systems Touch force-feedback arm mounted on a wearable platform carried in front of a user. The wearable harness also holds a full-sized laptop, which drives the Meta Quest 2 HMD that is also worn by the user. This design provides six degrees-of-freedom without tethered limitations, while ensuring a high precision of force-feedback from virtual interaction. Our solution also provides an experience wherein a mobile user can explore different haptic feedback simulations and create, arrange, and deform general shapes.
Efficient Representations of Spatially Variant Point Spread Functions with Butterfly Transforms in Bayesian Imaging Algorithms

Bayesian imaging algorithms are becoming increasingly important in, e.g., astronomy, medicine and biology. Given that many of these algorithms compute iterative solutions to high-dimensional inverse problems, the efficiency and accuracy of the instrument response representation are of high importance for the imaging process. For this reason, point spread functions, which make up a large fraction of the response functions of telescopes and microscopes, are usually assumed to be spatially invariant in a given field of view and can thus be represented by a convolution. For many instruments, this assumption does not hold and degrades the accuracy of the instrument representation. Here, we discuss the application of butterfly transforms, which are linear neural network structures whose sizes scale subquadratically with the number of data points. Butterfly transforms are efficient by design, since they are inspired by the structure of the Cooley&ndash;Tukey Fast Fourier transform. In this work, we combine them in several ways into butterfly networks, compare the different architectures with respect to their performance and identify a representation that is suitable for the efficient respresentation of a synthetic spatially variant point spread function up to a 1% error.
Steady-state subradiance manipulated by the two-atom decay

We investigate theoretically the collective radiance characteristics of an atomic ensemble with the simultaneous decay of two atoms. We show that the two-atom decay can significantly suppress the steady-state collective radiance of the atoms, expanding the region of subradiance. In the steady-state subradiance regime, the system is in an entangled state, and the mean populations of the system in the excited state and ground state of the atoms are almost equal. The processes of the two-atom decay can be demonstrated by the population distribution of the system state on the Dicke ladder. Moreover, we show the correlation property of the emitted light from the atomic ensemble, where the correlation function is rewritten in the presence of the two-atom decay. We find that the emitted photons of steady state only show bunching in the case of two-atom decay. This work broadens the realm of collective radiance, with potential applications for quantum information processing.
Optimizing Multiple Entropy Thresholding by the Chaotic Combination Strategy Sparrow Search Algorithm for Aggregate Image Segmentation

Aggregate measurement and analysis are critical for civil engineering. Multiple entropy thresholding (MET) is inefficient, and the accuracy of related optimization strategies is unsatisfactory, which results in the segmented aggregate images lacking many surface roughness and aggregate edge features. Thus, this research proposes an autonomous segmentation model (i.e., PERSSA-MET) that optimizes MET based on the chaotic combination strategy sparrow search algorithm (SSA). First, aiming at the characteristics of the many extreme values of an aggregate image, a novel expansion parameter and range-control elite mutation strategies were studied and combined with piecewise mapping, named PERSSA, to improve the SSA’s accuracy. This was compared with seven optimization algorithms using benchmark function experiments and a Wilcoxon rank-sum test, and the PERSSA’s superiority was proved with the tests. Then, PERSSA was utilized to swiftly determine MET thresholds, and the METs were the Renyi entropy, symmetric cross entropy, and Kapur entropy. In the segmentation experiments of the aggregate images, it was proven that PERSSA-MET effectively segmented more details. Compared with SSA-MET, it achieved 28.90%, 12.55%, and 6.00% improvements in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM), and the feature similarity (FSIM). Finally, a new parameter, overall merit weight proportion (OMWP), is suggested to calculate this segmentation method’s superiority over all other algorithms. The results show that PERSSA-Renyi entropy outperforms well, and it can effectively keep the aggregate surface texture features and attain a balance between accuracy and speed.
Motivation and reading in high-immersion virtual reality

Many language learners lack the motivation to read complex texts. Because high-immersion Virtual Reality (VR) is increasingly reported to be highly motivating, the goal of our study was to compare English as a Second Language (ESL) learners’ (N=79) motivation while reading a story with subtitles in VR (experimental group) versus reading the same story screencast in two-dimensions (2D – control group). The Wilcoxon signed-rank test revealed that learners’ motivation in VR was significantly higher compared with the control group. Our finding confirmed that VR is highly motivational in the context of reading foreign texts and can be useful for Computer Assisted Language Learning (CALL) researchers, practitioners, and instructional designers. This analysis is a part of a larger study (Kaplan-Rakowski &amp; Gruber, 2023) on VR facilitating reading tasks.
CNN-based Visual Localization for Autonomous Vehicles under Different Weather Conditions

A Convolutional Neural Network (CNN) was developed for autonomous localization layer-by-layer in urban driving situations.• To check the positional accuracy of the CNN, RGB images are combined with depth images using the IHS method.• With an accuracy rate of 94.74%, the simulation results demonstrated the effectiveness of the suggested strategy.• The Simulation findings demonstrate the superiority of the suggested technique for different weather conditions.Autonomous vehicles (AV) are expected to improve, transform, and revolutionize ground transportation.Previous techniques are dependent on localization employing pricey inaccuracy Global Positioning sensor.Furthermore, the performance loss is caused by drifting errors of Simultaneous Localization and Mapping.Regarding categorizing and analyzing texture, cameras are much more accessible and practical.This work contributes to obtaining high accuracy for AV localization and reducing errors in predicted positions.Based on the light, accurate, and robust proposed Convolutional Neural Network (CNN), it will scale down the computational complexity and shorten the training time.Considering various weather and time of day conditions such as bright sunny, hard rain noon, and wet cloudy noon with a vision-only system Red, Green, and Blue (RGB) low-cost camera sensors.To check the positional accuracy of the CNN, RGB images are combined with depth images using the IHS method.The k-Mean technique evaluates the similarity between a specific image and all street images to obtain precise coordinates.The Simulation findings demonstrate the superiority of the suggested technique for different weather conditions, which has an accuracy of up to 94.74% and a Mean Squared Error MSE in a distance of 0 meters, as opposed to [19], where the MSE in the projected position is 4.8 meters.Another indication of the proposed method's effectiveness is that it yielded reliable results when its validity was tested on images from a dataset that had not been trained.
Novel high precision low-cost dual axis sun tracker based on three light sensors

This study aims to test a new type of dual axes solar tracker based on just three Light Dependent Resistors (LDR) as optical sensors in order to achieve a high precision sun tracking that satisfies the requirements of solar concentration systems. This new system is integrated into a solar concentrator that uses a set of Fresnel mirrors and an evacuated tube collector as an absorber so that it can be used as a solar water heater. The 3D-printed support that holds LDRs have three compartments to maintain the three LDRs firmly in their place. One is placed on the left and the other on the right side of the support. These two LDRs are responsible for the azimuthal tracking while the third LDR is placed in the center and its responsibility is elevation tracking. This central LDR is covered under an alveolus hollow tube with a length of 45mm crossed all along its center with a 0.4mm slot that lets sun rays reach the central LDR at the bottom of the alveolus hollow tube. A setup was made to collect data based on a pyranometer and three thermocouples positioned at specific points on the evacuated tube collector to record the temperature variation.