Reviewerly/exHarmony huggingface dataset:
The effectiveness application of learning model with augmented reality on deaf student’s geometry learning outcomes

Previous studies have reported that many deaf students have difficulty explaining their conceptual understanding of two-dimensional geometry even though they may be familiar with these basic geometric concepts. The purpose of this study was to answer the problems related to the effectiveness of learning model application with augmented reality on deaf student’s geometry learning outcomes, especially enhanced understanding of geometry concepts. A learning model with augmented reality (AR) that adapts the technology of the augmented reality (AR) framework, allows deaf students to see the real world while the virtual elements are combined with the real world. In this way, students can visualize abstract objects that were previously difficult for them to imagine so students’ conceptual understanding supporting by gained of the direct experiences and it is a new opportunity to support the mathematics learning process. Students were tested before and after learning with the application of augmented reality (AR) technology. Paired sample t-test using SPSS was used to compare pretest and posttest scores to measure the effectiveness of implementing the learning model with AR to enhance students’ understanding of geometry concepts. There was a statistically significant enhancement in a score (Sig.=0.000), which indicates that the application of learning models with augmented reality (AR) in the classroom helps the learning outcomes of deaf students, especially their understanding of geometry concepts. In addition, students’ positive responses indicate that the learning model with augmented reality (AR) provides an interesting way to learn two-dimensional concepts.
Reference-Guided Landmark Image Inpainting With Deep Feature Matching

Despite impressive progress made by recent image inpainting methods, they often fail to predict the original content when the corrupted region contains unique structures, especially for landmark images. Applying similar images as a reference is helpful but introduces a style gap of textures, resulting in color misalignment. To this end, we propose a style-robust approach for reference-guided landmark image inpainting, taking advantage of both the representation power of learned deep features and the structural prior from the reference image. By matching deep features, our approach builds style-robust nearest-neighbor mapping vector fields between the corrupted and reference images, in which the loss of information due to corruption leads to mismatched mapping vectors. To correct these mismatched mapping vectors based on the relationship between the uncorrupted and corrupted regions, we introduce mutual nearest neighbors as reliable anchors and interpolate around these anchors progressively. Finally, based on the corrected mapping vector fields, we propose a two-step warping strategy to complete the corrupted image, utilizing the reference image as a structural “blueprint”, avoiding the style misalignment problem. Extensive experiments show that our approach effectively and robustly assists image inpainting methods in restoring unique structures in the corrupted image.
A Multiobject Detection Scheme Based on Deep Learning for Infrared Images

Multi-object detection is one of essential prerequisites for autonomous driving under complicated traffic environments. Specially, for the cases of nighttime driving, the accuracy of object detector for those images captured by infrared cameras is inevitably affected by several unanticipated issues (such as dim and unexpected light sources), which may significantly degrade the performance of detecting multiple objects (e.g., pedestrians and vehicles). To address such problem, we propose a <i>Light-Weighted Multi-Object Detection Scheme for Infrared Images</i> (<i>LW-MODS-IRI</i>). First, we select an effective super-resolution (SR) method to enhance and repair those infrared images, which can facilitate restoring the details of images for better detection accuracy. Next, those enhanced images are trained by our LW-MODS-IRI scheme. In contrast with previous methods, we develop an adaptive computing policy for anchor clustering that aims at locating the bound of targets in a reasonable manner for higher detection precision. Moreover, a modified attention mechanism is exploited to enlarge the receptive fields for promoting the detection performance of those large objects. Furthermore, we propose an improved channel-level pruning technique to generate both compact and accurate model after the training with sparsity. Finally, the extensive experiment results on Teledyne FLIR ADAS dataset (FLIR) demonstrate that the LW-MODS-IRI scheme can have both higher inference accuracy (e.g., 10.7% more) and higher detection efficiency (e.g., 4.13 times faster) compared to the existing object detectors, which typically reveals its <i>practical viability</i> when it is applied to in-vehicle systems.
Hill climbing algorithm for Bayesian network structure

Bayesian Network (BN) model is a method developed to describe causal relationships between variables in a system. BN is a form of Probabilistic Graphical Model or a simple probabilistic graph built from Bayes probability theory and graph theory. There are two approaches used to construct the BN structure, namely the constraint-based method and the score-based method. The scored-based method is a method that assigns a score to each structure in BN and tries to maximize the scoring with several heuristic search algorithms. The scoring is executed through the usage of Bayesian Information Criterion (BIC) scoring function. In this study, scored-based totally is solved through the Hill Climbing (HC) algorithm. This algorithm is a value-based algorithm in a directed graph space and includes a heuristic search method that works greedily. The results of the study show that the use of the score-based method followed by the HC algorithm and the BIC scoring function can build the BN structure.
Blending Query Strategy of Active Learning for Imbalanced Data

When the data is imbalanced, often observed in the real-world, important minor class instances that are conducive to accurately predicting the decision boundary are less likely to be queried in the active learning for classification task.Therefore, mitigating the effect of the imbalance in learning is necessary for achieving better generalization.For the alleviation of this problem, this paper considers an active learning algorithm referred to as the blending minority preferential (BMP).The BMP systematically adapts to blend conventional query strategies with the proposed minority preferential queries on a randomly sampled pool dataset.The proposed minority preferential queries are conditioned on an unlabeled data instance predicted to belong to the minor class.A multi-armed bandit is involved in the blending of the two different types of queries for achieving high accuracy and balanced learning in obtaining each query set.The performance of the BMP is validated on datasets that include three imbalanced datasets having binary labels, eleven small structured datasets, modified Fashion-MNIST, CIFAR10 dataets, and two real datasets of the PlantSC and HAM10000.The comparison result shows that the BMP can mitigate imbalanced learning and achieves higher accuracy combined with higher G-mean, compared with conventional queries or other query-based imbalanced active learning.
Electrodermal Responses to Driving Maneuvers in a Motion Sickness Inducing Real-World Driving Scenario

Motion sickness is a phenomenon attracting increasing attention with the ever-growing popularity of highly automated driving. Understanding motion sickness is of significant interest in the context of self-driven vehicles because, in this case, all occupants of the vehicle are passengers and, therefore, more susceptible to motion sickness. In this article, we report the findings of a study wherein motion sickness was induced in 40% of the participants while driving in real-world conditions. By recording various psychophysiological parameters continuously (electrodermal activity, skin temperature, heart rate, and heart rate variability), we investigate the feasibility of using these to objectively assess motion sickness. Furthermore, the instantaneous physiological reactions of participants to unpleasant driving maneuvers are examined. The changes in the electrodermal activity show a strong correlation with the subjective ratings of motion sickness levels as reported by the participants. The phasic component of the electrodermal activity suggests differences between participants that are susceptible to motion sickness and those who are not. Several driving maneuvers (accelerations, cornering, and driving over speed bumps) were identified as events triggering significant electrodermal responses. These responses could be the result of a mismatch between visual and vestibular perception acting as an aversive, arousing stimulus. While, in this work, the driving maneuvers were partially overlapping and nonuniform, our results pave the way for future investigation of physiological responses to single driving events and their relation to motion sickness with the potential to identify real-time markers of possibly unpleasant driving maneuvers.
Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis With Rendering

Driving simulators play a large role in developing and testing new intelligent vehicle systems. The visual fidelity of the simulation is critical for building vision-based algorithms and conducting human driver experiments. Low visual fidelity breaks immersion for human-in-the-loop driving experiments. Conventional computer graphics pipelines use detailed 3D models, meshes, textures, and rendering engines to generate 2D images from 3D scenes. These processes are labor-intensive, and they do not generate photorealistic imagery. Here we introduce a hybrid generative neural graphics pipeline for improving the visual fidelity of driving simulations. Given a 3D scene, we partially-render only important objects of interest, such as vehicles, and use generative adversarial processes to synthesize the background and the rest of the image. To this end, we propose a novel image formation strategy to form 2D semantic images from 3D scenery consisting of simple object models without textures. These semantic images are then converted into photorealistic RGB images with a state-of-the-art Generative Adversarial Network (GAN) trained on real-world driving scenes. This replaces repetitiveness with randomly generated but photorealistic surfaces. Finally, the partially-rendered and GAN synthesized images are blended with a blending GAN. We show that the photorealism of images generated with the proposed method is more similar to real-world driving datasets such as Cityscapes and KITTI than conventional approaches. This comparison is made using semantic retention analysis and Frechet Inception Distance (FID) measurements.
Structure Preservation and Seam Optimization for Parallax-Tolerant Image Stitching

Parallax processing has long been a significant and challenging task in image stitching. In this paper, we study a new hybrid warping model based on multi-homography and structure preservation to achieve accurate alignment of regions at different depths while preserving local and global image structures. The homographies of different depth regions are estimated by dividing matching feature pairs into multiple layers. Then, layered warping is performed by determining the spatial relationships between the image mesh and these multi-homography, and then refining the local and global structural distortions through mesh optimization. Four constraints are considered during the local optimization process, including the local alignment error, global alignment error, and similarity error. In addition, we explore and introduce collinear structures into an objective function as a constraint for mesh optimization warping, which can preserve salient line structures while alleviating distortions in nonoverlapping areas. Furthermore, we develop an optimal seam search method based on seam error evaluation to improve the quality of the seams. Experimental results demonstrate that compared to existing methods, the proposed algorithm presents more accurate stitching results for images with large parallax and preserves salient image structures, and outperforming the existing methods both qualitatively and quantitatively.
Muster: Multi-source Streaming for Tile-based 360° Videos within Cloud Native 5G Networks

360&#x00B0; videos generally require a large amount of bandwidth between video servers and users, which puts much burden on the current CDN-based single-source video streaming solutions. The emerging cloud native 5G networks can bridge the distance between video servers and users by leveraging in-network single-source video streaming to enhance 360&#x00B0; video quality. Unfortunately, the restricted bandwidth of in-network servers becomes the main bottleneck. Although tile-based video streaming is promising to reduce video transmission size while keeping user QoE, it highly depends on the accuracy of user FoV prediction, which existing prediction methods cannot guarantee. Recently, some researchers advocate the idea of &#x201C;super FoV&#x201D; (i.e., an extended range of predicted FoV) to cope with the inaccurate FoV prediction, which however could lower the effect of tile-based video streaming. Alternatively, we present Muster, a multi-source streaming for tile-based 360&#x00B0; videos within cloud native 5G networks. We detail the system components, provide a comprehensive model, formulate joint server selection and tile requesting problems, and correspondingly propose efficient online algorithms with a performance guarantee. Small-scale testbed and large-scale simulation based evaluation confirm the superiority of the proposed algorithms.
A Survey on Layer 2 Solutions to Achieve Scalability in Blockchain

Blockchain acts as a distributed ledger technology (DLT), where it keeps a record of actions in the system where it is adopted. When implemented in networks, every activity of the system is logged as digital assets in a chronological fashion. These digital assets or transactions are later grouped together to form blocks as in a chain. Networks based on Blockchain technology work as peer-to-peer networks and each peer have a cryptographically protected copy of the entire chain of blocks, which in turn makes it a secure decentralized network. In short, Blockchain technology presents us with a novel idea of information storage by keeping a system of networks open and trustful, without giving space for a single point of control. Though Blockchain networks are too promising to be adopted as a perfect solution to areas such as Supply Chain Management, e-Governance, Healthcare, and IoT, it suffers from the issue of scalability. This paper aims to focus on the review of Layer 2 solutions for achieving scalability in Blockchain.
StereoPairFree: Self-Constructed Stereo Correspondence Network From Natural Images

This article proposes a variant of a convolutional neural network-based stereo matching method, StereoPairFree, that requires only normal and not left or right stereo images. The corresponding image patches for training stereo matching cost networks look similar, apart from some properties, such as different illumination, occlusion, distortion, and foreshortening. We propose a method for generating synthesized pairs of corresponding image patches for a given image patch. We also propose a multipatch matching cost network that exploits various input patch sizes. The proposed matching cost network is optimized by cross-based cost aggregation and semiglobal matching, followed by consistency checks and bilateral filtering. Hence, StereoPairFree does not require a single stereo pair but standard images to build a deep learning stereo matching method. It is the first stereo matching method constructed from normal images and significantly outperforms monocular stereo matching approaches (Hur and Roth, 2020), (Hur and Roth, 2021), (Schuster et al., 2020). We evaluated StereoPairFree with the KITTI-2012, KITTI-2015, and Middlebury datasets. It significantly outperformed the baseline and several deep learning-based methods using these datasets.
An Efficient Service Selection Algorithm for Cloud Computing

Cloud service providers (CSPs) are providing a variety of similar services in the cloud marketplace. It is very challenging for cloud customers to compare these services and choose an appropriate CSP. In this regard, multi-criteria decision-making (MCDM) algorithms are useful to select a suitable CSP among a set of CSPs and/or rank the CSPs. The selection or ranking of CSP is based on various quality of service (QoS) parameters and their weights. One of the popular MCDM algorithms is the preference ranking organization method for enrichment of evaluations (PROMETHEE), which results in the completeness of ranking. PROMETHEE compares the alternatives with respect to each criterion and represents them in the form of a pairwise comparison matrix. However, the comparison value is represented as either 0 or 1 without showing how far or close these two alternatives are with respect to that criterion. Therefore, in this paper, we propose an efficient service selection algorithm (SSA) by modifying the traditional PROMETHEE and by considering the differences between the alternatives with respect to criteria. The proposed algorithm is illustrated through a numerical example and compared with the PROMETHEE using four randomly generated datasets. The comparison results show the effectiveness of the proposed algorithm.
Comprehensive Forecasting-Based Analysis Using Hybrid and Stacked Stateful/Stateless Models

In recent times, deep learning has received immense growth and popularity. Deep learning models have been deployed to varied fields to elicit interesting results. Solar irradiance time-series forecasting has been one of such great avenues for exploring various deep learning models. In this paper, we explore the integration of deep recurrent neural networks based on stacked stateful and stateless LSTM, GRU, and Autoencoder models on time-series solar irradiance forecasting. We investigate the accuracy, performance, and reliability of the models based on the performance evaluation metrics. We produce time-series solar irradiance data of eight historical regions in eastern India for future research opportunities. Our experimental results validate that hybrid LSTM-based Autoencoder model performs the best in terms of accuracy and performance evaluation metrics compared to the other considered models.
360° Technology Applied to Touristic Marketing

360° technology for images or videos allows the covering of a 360° vision angle in two axles (X and Y), and it can be seen on a screen in an interactive way. This means that we can see everything that is around the point where the picture or video was taken, up, down, in front, behind and the sides, which allows us to reproduce the stereoscopic human vision. In this article, the usage of this technology is proposed as a tool to enhance the touristic marketing area, giving the users an innovative expérience.
A Document Image Generation Scheme Based on Face Swapping and Distortion Generation

Nowadays, digital images are widely used in various services. The emergence of more and more image editing algorithms has made image forensic approaches to be severely challenged. Driven by the emergence of forged images, more and more image forensic methods are proposed to evaluate the authenticity of digital images. However, in some privacy-related image forensics areas, the scarcity of data affects their development. In this paper, we investigate a document image generation scheme based on face swapping and distortion generation to generate document image databases at a low cost. First, we propose an IDNet for editing face content and text content in digital document images. Second, we propose a Distortion Simulation Network (DSNet) for simulating print-and-scan distortion, and the generated data can be used to study a novel document attack type called recapture attack. Third, we use the database generated by our proposed method to assist in training document image recapture forensic networks. A qualitative comparison with existing methods illustrates that the image content generated by our proposed method maintains more complete semantic information and higher image quality. Quantitative results have confirmed that with the addition of the generated database, the performance of the model improves by about 8% measured in AUC.
Local PatchMatch Based on Superpixel Cut for Efficient High-resolution Stereo Matching

Obtaining the accurate disparity of each pixel quickly is the goal of stereo matching, but it is very difficult for the 3D labels-based methods due to huge search space of 3D labels, especially for highresolution images.We present an novel two-stage optimization strategy to get the accurate disparity map for highresolution stereo image efficiently, which includes feature points optimization and superpixel optimization.In the first stage, we construct the support points including edge points and robust points for triangulation, which is used to extract feature points and then perform spatial propagation and random refinement to get the candidate 3D label sets.In the stage of superpixel optimization, we update per pixel labels of the corresponding superpixels using the candidate label sets, and then perform spatial propagation and random refinement.In order to provide more prior information to identify weak texture and textureless areas, we design the weight combination of "intensity + gradient + binary image" for constructing an optimal minimum spanning tree (MST) to compute the aggregated matching cost, and obtain the labels of minimum aggregated HIGHLIGHTS• A new method of extracting the feature points in superpixels is proposed to compute the candidate disparities using non-local cost aggregation.• A novel two-stage optimization framework for superpixel proposals by first getting the labels of feature points quickly and then consists the candidate label sets for updating the labels of pixels within the corresponding superpixel instead of assigning a label randomly to those pixels.• The weight combination of intensity, gradient and binary image is designed for constructing an optimal minimum spanning tree to compute the aggregated matching cost.
A Study on Particle Swarm Algorithm Based on Restart Strategy and Adaptive Dynamic Mechanism

Aiming at the problems of low path success rate, easy precocious maturity, and easily falling into local extremums in the complex environment of path planning of mobile robots, this paper proposes a new particle swarm algorithm (RDS-PSO) based on restart strategy and adaptive dynamic adjustment mechanism. When the population falls into local optimal or premature convergence, the restart strategy is activated to expand the search range by re-randomly initializing the group particles. An inverted S-type decreasing inertia weight and adaptive dynamic adjustment learning factor are proposed to balance the ability of local search and global search. Finally, the new RDS-PSO algorithm is combined with cubic spline interpolation to apply to the path planning and smoothing processing of mobile robots, and the coding mode based on the path node as a particle individual is constructed, and the penalty function is selected as the fitness function to solve the shortest collision-free path. The comparative results of simulation experiments show that the RDS-PSO algorithm proposed in this paper solves the problem of falling into local extremums and precocious puberty, significantly improves the optimization, speed, and effectiveness of the path, and the simulation experiments in different environments also show that the algorithm has good robustness and generalization.