Reviewerly/exHarmony huggingface dataset:
Accelerated Earth-Rockfill Dam Compaction by Collaborative Operation of Unmanned Roller Fleet

The use of unmanned rollers for the compaction of the earth-rockfill dams is of great significance. However, collaborative control of multiple unmanned rollers at the construction site, enabling each unmanned roller to operate on the optimal rolling path with optimal rolling parameters to improve compaction efficiency, is an unaddressed challenge. There are three technical barriers, i.e., how to plan the optimal rolling path, how to determine the optimal rolling parameters, and how to control the unmanned roller fleet (URF) based on the optimal rolling plan. This study aims to solve these technical barriers. First, a Stripe-genetic algorithm (GA) path planning method that can balance the task load of multiple unmanned rollers is proposed. Second, a spatial global rolling parameter decision (SGRPD) method is proposed to maximize the stripe compaction efficiency. Finally, a URF-based intelligent compaction system composed of the control module, communication module, remote server, and cloud platform integrated with Stripe-GA and SGRPD is proposed. Field experiments conducted on the construction site of a 295-m high earth-rockfill dam have verified the effectiveness and advantages of the proposed system. The results demonstrated that the proposed system could operate multiple unmanned rollers to achieve higher compaction quality and efficiency by compaction with optimal rolling path and parameters. Compared with the monitoring-based manual compaction method, its compaction quality and efficiency are improved by 2.57% and 24.18%, respectively. The proposed system has the potential to be applied to earthwork constructions, such as roads and airports.
The Use of Mixed, Augmented and Virtual Reality in History of Art Teaching: A Case Study

The incorporation of mixed, virtual, and augmented reality into the educational context takes place in this study through the development of a 3D object shaped by the artistic expressions of the Church of the Annunciation in Seville. In a study of an experimental and exploratory nature with a single group, we worked with a total of 20 students enrolled in a Master’s degree, taught at Seville University, under the title of “Arte: idea y producción” (Art: idea and production). A questionnaire based on the “Technology Acceptance Model” (TAM) was used to ascertain the degree of acceptance that the utilized technology had created among students after their participation in the experience. Likewise, the questionnaire permitted understanding of the assessment made by students concerning the presented objects. Among the results obtained, it is noticeable that the participating students show a high level of acceptance of augmented and virtual reality technologies, alongside favorable attitudes towards their utilization and the intention to use them. It is worth highlighting as a significant conclusion that the exploratory study was performed within a real classroom situation, suggesting that both technologies can be applied in formal training environments.
Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network

Mammography is a first-line imaging examination approach used for early breast tumor screening. Computational techniques based on deep-learning methods, such as convolutional neural network (CNN), are routinely used as classifiers for rapid automatic breast tumor screening in mammography examination. Classifying multiple feature maps on two-dimensional (2D) digital images, a multilayer CNN has multiple convolutional-pooling layers and fully connected networks, which can increase the screening accuracy and reduce the error rate. However, this multilayer architecture presents some limitations, such as high computational complexity, large-scale training dataset requirements, and poor suitability for real-time clinical applications. Hence, this study designs an optimal multilayer architecture for a CNN-based classifier for automatic breast tumor screening, consisting of three convolutional layers, two pooling layers, a flattening layer, and a classification layer. In the first convolutional layer, the proposed classifier performs the fractional-order convolutional process to enhance the image and remove unwanted noise for obtaining the desired object’s edges; in the second and third convolutional-pooling layers, two kernel convolutional and pooling operations are used to ensure the continuous enhancement and sharpening of the feature patterns for further extracting of the desired features at different scales and different levels. Moreover, there is a reduction of the dimensions of the feature patterns. In the classification layer, a multilayer network with an adaptive moment estimation algorithm is used to refine a classifier’s network parameters for mammography classification by separating tumor-free feature patterns from tumor feature patterns. Images can be selected from a curated breast imaging subset of a digital database for screening mammography (CBIS-DDSM), and K-fold cross-validations are performed. The experimental results indicate promising performance for automatic breast tumor screening in terms of recall (%), precision (%), accuracy (%), F1 score, and Youden’s index.
Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China

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A blockchain-based protocol for tracking user access to shared medical imaging

Modern healthcare systems are complex and regularly share sensitive data among multiple stakeholders, such as doctors, patients, and pharmacists. Patients’ data has increased and requires safe methods for its management. Research works related to blockchain, such as MIT MedRec, have strived to draft trustworthy and immutable systems to share data. However, blockchain may be challenging in healthcare scenarios due to issues about privacy and control of data sharing destinations. This paper presents a protocol for tracking shared medical data, which includes images, and controlling the medical data access by multiple conflicting stakeholders. Several efforts rely on blockchain for healthcare, but just a few are concerned about malicious data leakage in blockchain-based healthcare systems. We implement a token mechanism stored in DICOM files and managed by Hyperledger Fabric Blockchain. Our findings and evaluations revealed low chances of a hash collision, such as employing a fitting-resistance birthday attack. Although our solution was devised for healthcare, it can inspire and be easily ported to other blockchain-based application scenarios, such as Ethereum or Hyperledger Besu for business networks.
Static and Dynamic Isolated Indian and Russian Sign Language Recognition with Spatial and Temporal Feature Detection Using Hybrid Neural Network

The Sign Language Recognition system intends to recognize the Sign language used by the hearing and vocally impaired populace. The interpretation of isolated sign language from static and dynamic gestures is a difficult study field in machine vision. Managing quick hand movement, facial expression, illumination variations, signer variation, and background complexity are amongst the most serious challenges in this arena. While deep learning-based models have been used to accomplish the entirety of the field's state-of-the-art outcomes, the previous issues have not been fully addressed. To overcome these issues, we propose a Hybrid Neural Network Architecture for the recognition of Isolated Indian and Russian Sign Language. In the case of static gesture recognition, the proposed framework deals with the 3D Convolution Net with an atrous convolution mechanism for spatial feature extraction. For dynamic gesture recognition, the proposed framework is an integration of semantic spatial multi-cue feature detection, extraction, and Temporal-Sequential feature extraction. The semantic spatial multi-cue feature detection and extraction module help in the generation of feature maps for Full-frame, pose, face, and hand. For face and hand detection, GradCam and Camshift algorithm have been used. The temporal and sequential module consists of a modified auto-encoder with a GELU activation function for abstract high-level feature extraction and a hybrid attention layer. The hybrid attention layer is an integration of segmentation and spatial attention mechanism. The proposed work also involves creating a novel multi-signer, single, and double-handed Isolated Sign representation dataset for Indian and Russian Sign Language. The experimentation was done on the novel dataset created. The accuracy obtained for Static Isolated Sign Recognition was 99.76%, and the accuracy obtained for Dynamic Isolated Sign Recognition was 99.85%. We have also compared the performance of our proposed work with other baseline models with benchmark datasets, and our proposed work proved to have better performance in terms of Accuracy metrics.
Non-linear perceptual multi-scale network for single image super-resolution

Recently, deep convolutional neural networks (CNNs) have been widely explored in single image super-resolution (SISR) and achieved remarkable progress. However, most of the existing CNN-based SISR networks with a single-stream structure fail to make full use of the multi-scale features of low-resolution (LR) image. While those multi-scale SR models often integrate the information with different receptive fields by means of linear fusion, which leads to the redundant feature extraction and hinders the reconstruction performance of the network. To address both issues, in this paper, we propose a non-linear perceptual multi-scale network (NLPMSNet) to fuse the multi-scale image information in a non-linear manner. Specifically, a novel non-linear perceptual multi-scale module (NLPMSM) is developed to learn more discriminative multi-scale feature correlation by using high-order channel attention mechanism, so as to adaptively extract image features at different scales. Besides, we present a multi-cascade residual nested group (MC-RNG) structure, which uses a global multi-cascade mechanism to organize multiple local residual nested groups (LRNG) to capture sufficient non-local hierarchical context information for reconstructing high-frequency details. LRNG uses a local residual nesting mechanism to stack NLPMSMs, which aims to form a more effective residual learning mechanism and obtain more representative local features. Experimental results show that, compared with the state-of-the-art SISR methods, the proposed NLPMSNet performs well in both quantitative metrics and visual quality with a small number of parameters.
Global path planning based on BIM and physics engine for UGVs in indoor environments

This paper proposes a global path planning (GPP) system based on building information modeling (BIM) and physics engine for unmanned ground vehicles (UGVs) operations in indoor environments. Firstly, UGV configuration is integrated into BIM as a knowledge base of GPP system by customized IFC structure, with which a multi-layer map generation method is proposed with improved logic and efficiency. Secondly, a UGV-centric A* path planning algorithm is designed by considering UGV's properties, including mobility-based primitive expansion, geometry-based collision checking, mobility-based cost setting, and mobility-based analytical expansion. Finally, a reliable trajectory generation method is developed based on physics engine, followed with a novel spatiotemporal coordination method for efficient collision avoidance. The whole GPP system is validated in a representative university building floor and a common inspection UGV. It is demonstrated that UGV-integrated BIM improves the resources interoperability for the whole system, and the map components are clarified clearly for efficient generation and maintenance. Besides, the UGV-centric A* performs a 100% success rate in congested environments where traditional A* always fail. It can even reduce almost 50% of the trajectory time and steering jerk in open scenarios than traditional A*, but still with an acceptable computation speed (less than 0.6 s). Finally, trajectory coordination saves 50% of path traveling time compared with the general queue method.
BINER: A low-cost biomedical named entity recognition

A primary focus of the healthcare industry is to improve patient experience and quality of service. Practitioners and health workers are generating large volumes of text that are captured in Electronic Medical Records, clinical reports, and publications. Additionally, patients post millions of comments on social media related to healthcare, on diverse topics such as hospital services, disease symptoms, and drugs effects. Unifying various data sources can guide physicians and healthcare workers to avoid unnecessary, irrelevant information and expedite access to helpful information. The main challenge to creating Biomedical Natural Language Understanding is the lack of standard datasets and the extensive computational resources needed to develop different models. This paper proposes a model trained on low-tier GPU computers, producing comparable results to larger models like BioBERT. We propose BINER, a Biomedical Named Entity Recognition architecture using limited data and computational resources.
A Novel Dynamic Mathematical Model Applied in Hash Function Based on DNA Algorithm and Chaotic Maps

This paper aims to improve SHA-512 security without increasing complexity; therefore, we focused on hash functions depending on DNA sequences and chaotic maps. After analysis of 45 various chaotic map types, only 5 types are selected in this proposal—namely, improved logistic, cosine logistic map, logistic sine system, tent sine system, and hybrid. Using DNA features and binary coding technology with complementary rules to hide information is a key challenge. This article proposes improving SHA-512 in two aspects: the modification of original hash buffer values, and the modification of additive constants Kt. This proposal is to make hash buffer values (a, b, c, d, e, f, g, and h) and Kt dependent on one-dimensional discrete chaotic maps and DNA sequences instead of constant. This modification complicates the relationship between the original message and hash value, making it unexpected. The performance of the proposed hash function is tested and analyzed the confusion, diffusion, and distributive and compared with the original SHA-512. The performance of security is analyzed by collision analysis, for which the maximum number of hits is only three, showing that the proposed hash function enhances the security and robustness of SHA-512. The statistical data and experimental analysis indicate that the proposed scheme has good properties and satisfies high-performance requirements for secure hash functions.
DR-GAN: Distribution Regularization for Text-to-Image Generation

This article presents a new text-to-image (T2I) generation model, named distribution regularization generative adversarial network (DR-GAN), to generate images from text descriptions from improved distribution learning. In DR-GAN, we introduce two novel modules: a semantic disentangling module (SDM) and a distribution normalization module (DNM). SDM combines the spatial self-attention mechanism (SSAM) and a new semantic disentangling loss (SDL) to help the generator distill key semantic information for the image generation. DNM uses a variational auto-encoder (VAE) to normalize and denoise the image latent distribution, which can help the discriminator better distinguish synthesized images from real images. DNM also adopts a distribution adversarial loss (DAL) to guide the generator to align with normalized real image distributions in the latent space. Extensive experiments on two public datasets demonstrated that our DR-GAN achieved a competitive performance in the T2I task. The code link: https://github.com/Tan-H-C/DR-GAN-Distribution-Regularization-for-Text-to-Image-Generation.
Designing a double-way spread permutation framework utilizing chaos and S-box for symmetric image encryption

Recently, many chaos-based image cryptosystems have been investigated. However, some drawbacks of existing schemes are as follows: (1) the high computational complexity and bad scrambling effect of permutation algorithm reduce the security and practicality of the encryption system; (2) the insufficient association between encryption algorithm and plaintext makes it suffer from low plaintext sensitivity and inability to effectively resist chosen plaintext attack; (3) some algorithms are one-time-pad-like encryption systems, which reduce their practical value; (4) adequate plaintext relation elevates the plaintext sensitivity but also increases the ciphertext sensitivity of a cryptosystem and make it possess low robustness of defending noise and data loss attacks. To address the defects of existing works, a symmetric plaintext-related image cryptosystem based on a new scrambling framework using S-box is proposed. Firstly, substitution-box(S-box) of AES is first used to develop a new scrambling framework, namely double-way spread permutation framework (DWSPF), which can be used to scramble color images and gray images of any size. Then, the present study develops a new method of plaintext association that one parameter carried the information of original image and used to control the encryption process is padded to the encrypted array which is transmitted to the receiver. Such a strategy can realize the sufficient plaintext relation without the use of one-time-pad encryption technology and avoid high ciphertext sensitivity, which can well resist noise attacks and information loss attacks. Sufficient experiments results demonstrate that the proposed permutation framework and cryptosystem possess excellent security and efficiency compared with the state-of-art works.
Just-In-Time Defect Prediction on JavaScript Projects: A Replication Study

Change-level defect prediction is widely referred to as just-in-time (JIT) defect prediction since it identifies a defect-inducing change at the check-in time, and researchers have proposed many approaches based on the language-independent change-level features. These approaches can be divided into two types: supervised approaches and unsupervised approaches, and their effectiveness has been verified on Java or C++ projects. However, whether the language-independent change-level features can effectively identify the defects of JavaScript projects is still unknown. Additionally, many researches have confirmed that supervised approaches outperform unsupervised approaches on Java or C++ projects when considering inspection effort. However, whether supervised JIT defect prediction approaches can still perform best on JavaScript projects is still unknown. Lastly, prior proposed change-level features are programming language–independent, whether programming language–specific change-level features can further improve the performance of JIT approaches on identifying defect-prone changes is also unknown. To address the aforementioned gap in knowledge, in this article, we collect and label the top-20 most starred JavaScript projects on GitHub. JavaScript is an extremely popular and widely used programming language in the industry. We propose five JavaScript-specific change-level features and conduct a large-scale empirical study (i.e., involving a total of 176,902 changes) and find that (1) supervised JIT defect prediction approaches (i.e., CBS+) still statistically significantly outperform unsupervised approaches on JavaScript projects when considering inspection effort; (2) JavaScript-specific change-level features can further improve the performance of approach built with language-independent features on identifying defect-prone changes; (3) the change-level features in the dimension of size (i.e., LT), diffusion (i.e., NF), and JavaScript-specific (i.e., SO and TC) are the most important features for indicating the defect-proneness of a change on JavaScript projects; and (4) project-related features (i.e., Stars, Branches, Def Ratio, Changes, Files, Defective, and Forks) have a high association with the probability of a change to be a defect-prone one on JavaScript projects.
MSL3D: 3D object detection from monocular, stereo and point cloud for autonomous driving

In this paper, we propose a novel deep architecture by combining multiple sensors for 3D object detection, named MSL3D. While recently LiDAR-Camera methods introduce additional semantic cues, working with fewer false detections, there is still a performance gap compared LiDAR-only methods. We argue that this gap is caused for two reasons: 1) the 3D spherical receptive fields of the set abstraction of the point clouds are not aligned with the 2D pixel-level receptive fields of the image. 2) the premature introduction of image information makes it is difficult to apply data augmentation both LiDAR and image synchronously. For the first problem, we extend 3D set abstraction to a 2D set abstraction that can transform the 2D image features to the 3D sphere to unify the receptive field of multi-modal data. For the second problem, we design a novel two-stage 3D detection framework that employs the LiDAR-only backbone in the first stage to estimate high-recall and high-quality proposals and then integrates the image and point clouds information for box refinement and confidence prediction. Besides, we add two auxiliary networks to effectively learn image features and point cloud features when using different multi-modal data augmentation strategies synchronously. Moreover, we design a consistency-structure generator using stereo images to determine whether any of a point in the 3D space belongs to the contour of the object, thereby supplementing the sparse point cloud information. Extensive experiments on the popular KITTI 3D objects detection dataset show that our proposed MSL3D achieves better performance comparing with other LiDAR-Only or LiDAR-Camera fusion approaches.
Example-based color transfer with Gaussian mixture modeling

Color transfer, which plays a key role in image editing, has attracted noticeable attention recently. It has remained a challenge to date due to various issues such as time-consuming manual adjustments and prior segmentation issues. In this paper, we propose to model color transfer under a probability framework and cast it as a parameter estimation problem. In particular, we relate the transferred image with the example image under the Gaussian Mixture Model (GMM) and regard the transferred image color as the GMM centroids. We employ the Expectation-Maximization (EM) algorithm (E-step and M-step) for optimization. To better preserve gradient information, we introduce a Laplacian based regularization term to the objective function at the M-step which is solved by deriving a gradient descent algorithm. Given the input of a source image and an example image, our method is able to generate multiple color transfer results with increasing EM iterations. Extensive experiments show that our approach generally outperforms other competitive color transfer methods, both visually and quantitatively.