Reviewerly/exHarmony huggingface dataset:
Mineral Texture Identification Using Local Binary Patterns Equipped with a Classification and Recognition Updating System (CARUS)

In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks.
Deep optical reservoir computing and chaotic synchronization predictions based on the cascade coupled optically pumped spin-VCSELs

In this work, we utilize two cascade coupling modes (unidirectional coupling and bidirectional coupling) to construct a four-layer deep reservoir computing (RC) system based on the cascade coupled optically-pumped spin-VCSEL. In such a system, there are double sub-reservoirs in each layer, which are formed by the chaotic x-PC and y-PC emitted by the reservoir spin-VCSEL in each layer. Under these two coupling modes, the chaotic x-PC and y-PC emitted by the driving optically-pumped spin-VCSEL (D-Spin-VCSEL), as two learning targets, are predicted by utilizing the four-layer reservoirs. In different parameter spaces, it is further explored that the outputs of the double sub-reservoirs in each layer are respectively synchronized with the chaotic x-PC and y-PC emitted by the D-Spin-VCSEL. The memory capacities (MCs) for the double sub-reservoirs in each layer are even further investigated. The results show that under two coupling modes, the predictions of the double sub-reservoirs with higher-layer for these two targets have smaller errors, denoting that the higher-layer double sub-reservoirs possess better predictive learning ability. Under the same system parameters, the outputs of the higher-layer dual parallel reservoirs are better synchronized with two chaotic PCs emitted by the D-Spin-VCSEL, respectively. The larger MCs can also be obtained by the higher-layer double reservoirs. In particular, compared with the four-layer reservoir computing system under unidirectional coupling, the four-layer reservoir computing system under bidirectional coupling shows better predictive ability in the same parameter space. The chaotic synchronizations predicted by each layer double sub-reservoirs under bidirectional coupling can be obtained higher qualities than those under unidirectional coupling. By the optimization of the system parameters, the outputs of the fourth-layer double sub-reservoirs are almost completely synchronized with the chaotic x-PC and y-PC emitted by the D-Spin-VCSEL, respectively, due to their correlation coefficient used to measure synchronization quality can be obtained as 0.99. These results have potential applications in chaotic computation, chaotic secure communication and accurate prediction of time series.
Security Responses in Software Development

The pressure on software developers to produce secure software has never been greater. But what does security look like in environments that do not produce security-critical software? In answer to this question, this multi-sited ethnographic study characterizes security episodes and identifies five typical behaviors in software development. Using theory drawn from information security and motivation research in software engineering, this article characterizes key ways in which individual developers form security responses to meet the demands of particular circumstances, providing a framework managers and teams can use to recognize, understand, and alter security activity in their environments.
Fractal graph convolutional network with MLP-mixer based multi-path feature fusion for classification of histopathological images

The spatial information among different tissue components and multi-level features is important in histopathological images for pathologists to diagnose cancers. Graph convolutional network (GCN) can effectively learn these spatial features to improve the performance of computer-aided diagnosis (CAD) for histopathological images. The newly proposed GCN-based framework can effectively avoid the complex image preprocessing for graph construction, which integrates convolutional neural network (CNN) and GCN into a framework (named CNN-GCN). However, existing GCN generally cannot well learn multi-level graph features to further promote spatial feature representation. In this work, a new fractal GCN (FGCN) is proposed, which integrates the graph convolution into the fractal structure with different paths to learn multi-level graph representations. Moreover, a novel MLP-mixer-based Multi-path Feature Fusion Unit (MMFFU) is developed in FGCN to fuse these multi-level graph features from multiple paths. In MMFFU, the Mixer layer of MLP-mixer and non-local attention operation are designed to enhance the information communication of features, which further improves feature representation. The proposed FGCN is then embedded into the CNN-GCN framework (named CNN-FGCN) to perform the end-to-end classification of histopathological images. The experimental results on two public histopathological image datasets indicate the effectiveness of CNN-FGCN.
SelfNet: A semi-supervised local Fisher discriminant network for few-shot learning

Few-shot learning, employing small-scale labeled samples to recognize new objects, has received substantial research interest. The prototypical network (ProtoNet) is a simple yet effective meta-learning method to solve this problem. In the few-shot scenario, however, the scarcity of data usually has a negative impact on the representational ability of prototypes. In this paper, a unique semi-supervised few-shot learning architecture, referred to as Semi-supervised local Fisher discriminant network (SelfNet), which integrates few-shot learning with subspace learning, is proposed. Using the union of the support set and the additional unlabeled set, a feature projection module is constructed to achieve the subspace projection. Additionally, a pseudo-labeling strategy, which adds the unlabeled samples with high prediction confidence to the support set, is employed to refine the original prototypes. Experimental results on two few-shot classification benchmarks demonstrate that SelfNet can achieve superior performance to the state-of-the-arts, indicating the benefits of utilizing unlabeled samples for feature projection.
ETS-3D: An Efficient Two-Stage Framework for Stereo 3D Object Detection

• An efficient end-to-end two-stage framework for stereo 3D object detection, called ETS3D, is proposed. • A light-weight stereo feature extraction module is designed to produce the weighted and fused features with depth information. • The proposed method achieves fast detection and competitive accuracy on the challenging KITTI benchmark. We propose an efficient two-stage framework for stereo 3D object detection, called ETS-3D. Contrary to many recent approaches that rely on depth maps predicted using time-consuming stereo matching models, our approach utilizes the well-designed features to generate high-quality 3D proposals in stage-1, without explicitly exploiting predicted depth map. Specifically, we leverage pixel-wise correlation to produce normalized cost volumes to weight the left image features, and fuse multi-scale weighted features to obtain the weighted and fused features for 3D proposal generation. To maintain fast computation, only the filtered positive 3D proposals are fed into the stage-2 sub-network for further proposal refinement and quality prediction. Furthermore, we reconstruct the 3D proposal features in stage-2 to make use of different feature representations, achieving more accurate detection results. The experimental results on the KITTI 3D object detection benchmark demonstrate that our method achieves state-of-the-art performance, and can run at more than 10 fps.
TDFL: Truth Discovery Based Byzantine Robust Federated Learning

Federated learning (FL) enables data owners to train a joint global model without sharing private data. However, it is vulnerable to Byzantine attackers that can launch poisoning attacks to destroy model training. Existing defense strategies rely on the additional datasets to train trustable server models or trusted execution environments to mitigate attacks. Besides, these strategies can only tolerate a small number of malicious users or resist a few types of poisoning attacks. To address these challenges, we design a novel federated learning method <b>TDFL</b> , <b>T</b> ruth <b>D</b> iscovery based <b>F</b> ederated <b>L</b> earning, which can defend against multiple poisoning attacks without additional datasets even when the Byzantine users are <inline-formula><tex-math notation="LaTeX">$\geq 50\%$</tex-math></inline-formula> . Specifically, the TDFL considers different scenarios with different malicious proportions. For Honest-majority setting (Byzantine <inline-formula><tex-math notation="LaTeX">$&lt; 50\%$</tex-math></inline-formula> ), we design a special robust truth discovery aggregation scheme to remove malicious model updates, which can assign weights according to users’ contribution; for Byzantine-majority setting (Byzantine <inline-formula><tex-math notation="LaTeX">$\geq 50\%$</tex-math></inline-formula> ), we use maximum clique-based filter to guarantee global model quality. To the best of our knowledge, this is the first study that uses truth discovery to defend against poisoning attacks. It is also the first scheme which can achieve strong robustness under multiple kinds of attacks launched by high proportion attackers without root datasets. Extensive comparative experiments are designed with five state-of-the-art aggregation rules under five types of classical poisoning attacks on different datasets. The experimental results demonstrate that TDFL is practical and achieves reasonable Byzantine-robustness.
Structure-Texture Parallel Embedding for Remote Sensing Image Super-Resolution

The structure and texture of images are crucial for remote sensing image super-resolution. Generative adversarial networks (GANs) recover image details through adversarial training. However, the recovered images always have structural distortions on the one hand, and GANs are difficult to train on the other hand. In addition, some methods assist reconstruction by introducing prior information of the image, but this brings additional computational cost. To address this issue, we propose a novel structure-texture parallel embedding (SPE) method for super-resolution (SR) of remote sensing images. Our method does not require additional image priors to reconstruct high-quality images. Specifically, we use the global structure information and local texture information of the image in the ascending space to guide the reconstruction result of the image. Firstly, we design a structure preserving block (SPB) to extract global structural features in the ascending space of the image, so as to obtain global structure information for a priori representation. Then, we design a local texture attention module (LTAM) to restore richer texture details. We have conducted lots of experiments on Draper public dataset. Experimental results show that our proposed method not only achieves a better trade-off between computational cost and performance, but also outperforms the existing several SR methods in terms of objective index evaluation and subjective visual effects.
UAV Remote Sensing Image Dehazing Based on Double-Scale Transmission Optimization Strategy

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often have texture detail loss and color distortion problems, especially in highlighted regions. This is mainly due to the rich texture and low intensity of UAV remote sensing images being ignored, which results in incorrect transmission estimation. In this paper, we propose a UAV remote sensing image dehazing method based on double-scale transmission optimization strategy. First, we propose a double-scale optimization strategy to estimate the transmission map with more accurate texture details and color preservation, especially in highlighted regions of hazy UAV images that are most severely distorted. Second, a UAV-adaptive haze-line prior algorithm is proposed to address the large scene depth and low intensity of UAV remote sensing images. Finally, we introduce a luminance-weighted frequency domain saliency model to avoid texture detail loss and color distortions for better transmission optimization, especially in highlighted regions. Compared with state-of-the-art methods, our method shows better detail performance and visual effects, especially for UAV images with highlighted regions.
Crafting Adversarial Perturbations via Transformed Image Component Swapping

Adversarial attacks have been demonstrated to fool the deep classification networks. There are two key characteristics of these attacks: firstly, these perturbations are mostly additive noises carefully crafted from the deep neural network itself. Secondly, the noises are added to the whole image, not considering them as the combination of multiple components from which they are made. Motivated by these observations, in this research, we first study the role of various image components and the impact of these components on the classification of the images. These manipulations do not require the knowledge of the networks and external noise to function effectively and hence have the potential to be one of the most practical options for real-world attacks. Based on the significance of the particular image components, we also propose a transferable adversarial attack against unseen deep networks. The proposed attack utilizes the projected gradient descent strategy to add the adversarial perturbation to the manipulated component image. The experiments are conducted on a wide range of networks and four databases including ImageNet and CIFAR-100. The experiments show that the proposed attack achieved better transferability and hence gives an upper hand to an attacker. On the ImageNet database, the success rate of the proposed attack is up to 88.5%, while the current state-of-the-art attack success rate on the database is 53.8%. We have further tested the resiliency of the attack against one of the most successful defenses namely adversarial training to measure its strength. The comparison with several challenging attacks shows that: (i) the proposed attack has a higher transferability rate against multiple unseen networks and (ii) it is hard to mitigate its impact. We claim that based on the understanding of the image components, the proposed research has been able to identify a newer adversarial attack unseen so far and unsolvable using the current defense mechanisms.
HealthNet: A Health Progression Network via Heterogeneous Medical Information Fusion

Numerous electronic health records (EHRs) offer valuable opportunities for understanding patients' health status at different stages, namely health progression. Extracting the health progression patterns allows researchers to perform accurate predictive analysis of patient outcomes. However, most existing works on this task suffer from the following two limitations: 1) the diverse dependencies among heterogeneous medical entities are overlooked, which leads to the one-sided modeling of patients' status and 2) the extraction granularity of patient's health progression patterns is coarse, limiting the model's ability to accurately infer the patient's future status. To address these challenges, a pretrained Health progression network via heterogeneous medical information fusion, HealthNet, is proposed in this article. Specifically, a global heterogeneous graph in HealthNet is built to integrate heterogeneous medical entities and the dependencies among them. In addition, the proposed health progression network is designed to model hierarchical medical event sequences. By this method, the fine-grained health progression patterns of patients' health can be captured. The experimental results on real disease datasets demonstrate that HealthNet outperforms the state-of-the-art models for both diagnosis prediction task and mortality prediction task.
Divide, Conquer and Merge for Internet-of-Things

Divide, Conquer and Merge (DCM) is a well-known algorithmic paradigm. It has been used to solve both core problems as well as to address the scalability issue in other algorithms. It is widely used in networks or distributed computation to solve the scalability problem arising due to highly dense and wide structures. However, from the implementation point of view, all these application of DCM were designed considering the traditional Asynchronous-Transmission (AT) based communication technologies. In this work, we study the application of DCM in the context of Synchronous-Transmission (ST) which bears fundamentally different dynamics compared to the AT. Specifically, we study the application of DCM over an existing state-of-the-art ST-based strategy MiniCast and demonstrate the performance gain in calculation of aggregation. Our work, shows that applying DCM on MiniCast makes MiniCast based aggregation mechanism upto six times faster and make it consume upto five times lesser energy compared to the case when DCM is not applied.
Digital Teaching Competence in Teacher Training as an Element to Attain SDG 4 of the 2030 Agenda

Improving the digital competences of teachers, students, and schools contributes to achieving Sustainable Development Goal 4 of the 2030 Agenda. That goal consists in enhancing the quality of education and offering learning opportunities. The knowledge of digital resources that teachers had and used during the COVID-19 confinement has contributed to the current awareness of the need to improve teachers’ digital competence. The main objective of this paper is to analyse the digital resources that teachers participating in this research used the most in Spain between March and June 2020 due to the lockdown experienced in that country. A validated questionnaire, which was administered online in schools and high schools of the Community of Madrid, was answered by 97 teachers who were able to teach from home. The results reveal their assessment of the technological resources they used. In their opinion, the most useful were digital platforms, online classes, and videos, whereas the least useful were online tests and forums. Their answers also show that the training they received was mainly through Internet tutorials and consultations with colleagues. In order to ensure quality education, it is important to improve teachers’ digital competence as a part of their training.
Transformer-Based T2-weighted MRI Synthesis from T1-weighted Images

Multi-modality magnetic resonance (MR) images provide complementary information for disease diagnoses. However, modality missing is quite usual in real-life clinical practice. Current methods usually employ convolution-based generative adversarial network (GAN) or its variants to synthesize the missing modality. With the development of vision transformer, we explore its application in the MRI modality synthesis task in this work. We propose a novel supervised deep learning method for synthesizing a missing modality, making use of a transformer-based encoder. Specifically, a model is trained for translating 2D MR images from T1-weighted to T2-weighted based on conditional GAN (cGAN). We replace the encoder with transformer and input adjacent slices to enrich spatial prior knowledge. Experimental results on a private dataset and a public dataset demonstrate that our proposed model outperforms state-of-the-art supervised methods for MR image synthesis, both quantitatively and qualitatively. Clinical relevance- This work proposes a method to synthesize T2-weighted images from T1-weighted ones to address the modality missing issue in MRI.
Perceptual cGAN for MRI Super-resolution

Capturing high-resolution magnetic resonance (MR) images is a time consuming process, which makes it unsuitable for medical emergencies and pediatric patients. Low-resolution MR imaging, by contrast, is faster than its high-resolution counterpart, but it compromises on fine details necessary for a more precise diagnosis. Super-resolution (SR), when applied to low-resolution MR images, can help increase their utility by synthetically generating high-resolution images with little additional time. In this paper, we present an SR technique for MR images that is based on generative adversarial networks (GANs), which have proven to be quite useful in producing sharp-looking details in SR. We introduce a conditional GAN with perceptual loss, which is conditioned upon the input low-resolution image, which improves the performance for isotropic and anisotropic MRI super-resolution. Clinical Relevance- MR image super-resolution has the potential for improving image acquisition speed to save the time of the clinicians, while guaranteeing high-quality images.
Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images

Automatic lesion segmentation in breast ultrasound (BUS) images aids in the diagnosis of breast cancer, the most common type of cancer in women. Accurate lesion segmentation in ultrasound images is a challenging task due to speckle noise, artifacts, shadows, and lesion variability in size and shape. Recently, convolutional neural networks have demonstrated impressive results in medical image segmentation tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the networks' performance comparison. This work presents a benchmark of seven state-of-the-art methods for the automatic breast lesion segmentation task. The methods were evaluated on a multi-center BUS dataset composed of three public datasets. Specifically, the U-Net, Dynamic U-Net, Semantic Segmentation Deep Residual Network with Variational Autoencoder (SegResNetVAE), U-Net Transformers, Residual Feedback Network, Multiscale Dual Attention-Based Network, and Global Guidance Network (GG-Net) architectures were evaluated. The training was performed with a combination of the cross-entropy and Dice loss functions and the overall performance of the networks was assessed using the Dice coefficient, Jaccard index, accuracy, recall, specificity, and precision. Despite all networks having obtained Dice scores superior to 75%, the GG-Net and SegResNetVAE architectures outperform the remaining methods, achieving 82.56% and 81.90%, respectively. Clinical Relevance- The results of this study allowed to prove the potential of deep neural networks to be used in clinical practice for breast lesion segmentation also suggesting the best model choices.