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BORON is a lightweight substitution–permutation network cipher proposed in 2017. We reduce the number of guessed key bits by key-bridging technology and first utilize Fast Walsh Transform on BORON to minimize the time complexity. Finally, this paper gives the better key-recovery attack against block cipher BORON than previously proposed by 2 rounds: we realize a 11-round key-recovery attack on BORON-80 and 13-round key-recovery attack on BORON-128. The attacks proposed in this paper are the best attacks against BORON-80/128 to date. [ABSTRACT FROM AUTHOR]

Vessel segmentation in optical coherence tomography angiography (OCTA) is crucial for the detection and diagnosis of various eye diseases. However, it is hard to distinguish intricate vessel morphology and quantify the density of blood vessels due to the large variety of vessel sizes, significant background noise, and small datasets. To this end, a retinal angiography multi-scale segmentation network, integrated with the inception and squeeze-and-excitation modules, is proposed to address the above challenges under the one-shot learning paradigm. Specifically, the inception module extends the receptive field and extracts multi-scale features effectively to handle diverse vessel sizes. Meanwhile, the squeeze-and-excitation module modifies channel weights adaptively to improve the vessel feature extraction ability in complex noise backgrounds. Furthermore, the one-shot learning paradigm is adapted to alleviate the problem of the limited number of images in existing retinal OCTA vascular datasets. Compared with the classic U2-Net, the proposed model gains improvements in the Dice coefficient, accuracy, precision, recall, and intersection over union by 3.74%, 4.72%, 8.62%, 4.87%, and 4.32% respectively. The experimental results demonstrate that the proposed one-shot learning method is an effective solution for retinal angiography image segmentation. [ABSTRACT FROM AUTHOR]

Demirci–Selçuk meet-in-the-middle (DS-MITM) attack is an effective method for cryptanalysis. As far as we know, the published automatic results of DS-MITM attack are all for byte-oriented ciphers. In this article, we first propose the automatic analysis method of DS-MITM attack for bit-oriented ciphers based on constraint programming, which is integrated with key-bridging technique. Based on the automatic modeling method, we propose the first result of DS-MITM attack on SIMON, which is a family of lightweight block ciphers proposed by the National Security Agency (NSA) in 2013. [ABSTRACT FROM AUTHOR]

Sign languages generally consist of a sequence of upper body gestures and are cooperative processes among various parts such as the hands, arms, and face. Therefore, the dynamics of the parts as well as the holistic appearance of the upper body and individual parts are essential for robust recognition. In this paper, a global–local representation (GLR) module is proposed to boost the spatiotemporal feature modeling. The GLR module is composed of global shift and local shift along the height, width, and temporal dimensions. Specifically, the global shift is applied to the entire feature map for holistic representation, while the local shift restricts itself to local patches to capture detailed features. Furthermore, a novel cross-scale aggregation module is designed to combine the global and local information in different dimensions. Extensive experimental results on three large-scale benchmarks, including WLASL, INCLUDE and LSA64, demonstrate that the proposed method achieves state-of-the-art recognition performance. [ABSTRACT FROM AUTHOR]

Universal lesion detection (ULD) in computed tomography (CT) images is an important and challenging prerequisite for computer-aided diagnosis (CAD) to find abnormal tissue, such as tumors of lymph nodes, liver tumors, and lymphadenopathy. The key challenge is that lesions have a tiny size and high similarity with non-lesions, which can easily lead to high false positives. Specifically , non-lesions are nearby normal anatomy that include the bowel, vasculature, and mesentery, which decrease the conspicuity of small lesions since they are often hard to differentiate. In this study, we present a novel scale-attention module that enhances feature discrimination between lesion and non-lesion regions by utilizing the domain knowledge of radiologists to reduce false positives effectively. Inspired by the domain knowledge that radiologists tend to divide each CT image into multiple areas, then detect lesions in these smaller areas separately, a local axial scale-attention (LASA) module is proposed to re-weight each pixel in a feature map by aggregating local features from multiple scales adaptively. In addition, to keep the same weight, a combination of axial pixels in the height- and width-axes is designed, attached with position embedding. The model can be used in CNNs easily and flexibly. We test our method on the DeepLesion dataset. The sensitivities at 0.5, 1, 2, 4, 8, and 16 false positives (FPs) per image and average sensitivity at [0.5, 1, 2, 4] are calculated to evaluate the accuracy. The sensitivities are 78.30%, 84.96%, 89.86%, 93.14%, 95.36%, and 95.54% at 0.5, 1, 2, 4, 8, and 16 FPs per image; the average sensitivity is 86.56%, outperforming the former methods. The proposed method enhances feature discrimination between lesion and non-lesion regions by adding LASA modules. These encouraging results illustrate the potential advantage of exploiting the domain knowledge for lesion detection. [ABSTRACT FROM AUTHOR]

A novel metastable austenitic stainless steel denoted as 13Cr12Mn is obtained by adding 12 wt% Mn to 13Cr–5Ni–2Mo super‐martensitic stainless steel. The designed 13Cr12Mn exhibits a yield strength of 899 MPa, a tensile strength of 1068 MPa, and an elongation of 21.2%. The in situ hydrogen‐charging incremental step loading technique experiments are conducted to evaluate hydrogen embrittlement. The hydrogen‐induced cracking threshold stress of 13Cr12Mn is determined to be 961 MPa, which exceeds that of tempered S13Cr with a similar yield strength by 400 MPa. In addition, 13Cr12Mn also shows good resistance to general corrosion and pitting corrosion in alkaline solutions. [ABSTRACT FROM AUTHOR]

Skeleton data is widely used in human action recognition for easy access, computational efficiency and environmental robustness. Recently, encoding skeleton sequences into color images becomes a popular preprocessing procedure to make use of the spatial modeling ability of convolutional neural network (CNN). Furthermore, inspired by relevant work in other fields, attention mechanism has been introduced to CNN based skeleton action recognition. In this paper, we propose a two-branch hierarchical attention model (HAN) for skeleton based action recognition. The proposed model consists of a base branch for spatial-temporal feature extraction and an attention branch for feature enhancement. In attention branch, we utilize auxiliary feature instead of intermediate feature to generate attention maps. Specifically, variance vectors of skeleton sequences are fused as motion saliency matrices to determine the contributions of each joint. Then the motion saliency matrices are sent into the hierarchical attention branch to obtain multiple attention maps. In order to make better use of the attention maps, two distinct combination schemes are proposed to link the two branches at feature extraction blocks and the fully connected layer. The entire model is integrated into an end-to-end trainable network. The efficacy of the proposed HAN has been verified on three benchmark datasets: NTU RGB+D Dataset, UTD-MHAD Dataset and SYSU-3D Dataset. The comparison results show that our approach outperforms the state-of-the-art methods on all datasets. [ABSTRACT FROM AUTHOR]

Analysis of human interaction is one important research topic of human motion analysis. It has been studied either using first-person vision (FPV) or third-person vision (TPV). However, the joint learning of both types of vision has so far attracted little attention. One of the reasons is the lack of suitable datasets that cover both FPV and TPV. In addition, existing benchmark datasets of either FPV or TPV have several limitations, including the limited number of samples, participant subjects, interaction categories, and modalities. In this work, we contribute a large-scale human interaction dataset, namely FT-HID dataset. FT-HID contains pair-aligned samples of first-person and third-person visions. The dataset was collected from 109 distinct subjects and has more than 90K samples for three modalities. The dataset has been validated by using several existing action recognition methods. In addition, we introduce a novel multi-view interaction mechanism for skeleton sequences, and a joint learning multi-stream framework for first-person and third-person visions. Both methods yield promising results on the FT-HID dataset. It is expected that the introduction of this vision-aligned large-scale dataset will promote the development of both FPV and TPV, and their joint learning techniques for human action analysis. [ABSTRACT FROM AUTHOR]

Previous studies have clarified that there are certain regularities in the spatial organization of traditional Chinese rural market towns as viewed from the perspective of the economic geography and local society. Nevertheless, the results of some studies concerning distribution patterns and factors influencing these patterns are contradictory, and there are few comprehensive analyses of the influence of interconnected variables. Taishan County in the Pearl River Delta of Guangdong Province is used as an example, and the results of the identification of the distribution pattern of market towns within this county are determined as clustered by using the Voronoi method and the calculated coefficients of variation (Cv). The correlation between the market towns and the physical and social environment is quantified and illustrated through Geographic Information Systems (GIS), logistic regression analysis, and graphic methods, and the application of nuclear density change rates clarifies the development trajectory, which explains the phenomenon of market town clustering with ecological and cultural significance. Overall, the results indicate traditional preferences for sites characterized by low elevation, little slope, proximity to water, and productive agricultural land, while at the local scale, the spatial–temporal arrangement of market towns reflects partitioning and interactions between distinct clans. Further integrating the perspective of environmental history, we propose that the structural relationships of natural ecology, subsistence mode, and social organization crucially constitute the site selection and layout logic of market towns. [ABSTRACT FROM AUTHOR]

Quality checks are important to ensure the accuracy and reliability of spatial data. However, current methods primarily focus on attributes and geometric information, and methods to verify the accuracy of land-type classification for vector patches are lacking. Therefore, in this paper, we proposed a framework for the automatic verification of the land type of vector patches, including the segmentation of complex vector patches, automatic acquisition of training samples, and automatic extraction of suspicious patches based on deep learning scene interpretation results. First, an innovative method for segmentation based on an oriented bounding box was proposed. Then, scene interpretation from the segmented processing units was performed. Finally, the scene interpretation results and the original data for vector patches were combined, and segmentation units that do not match the category information were automatically identified. The suspicious patches were extracted, and the land-type authenticity of vector patches was automatically checked. Experimental results showed that, in the study area, the accuracy, precision, recall, and F1 values for the model based on first-level land categories were 0.989, 0.842, 0.906, and 0.873, respectively, and those for second-level land categories were 0.994, 0.770, 0.891, and 0.826, respectively. Accordingly, the newly developed method provides reliable technical support for checking the land-type authenticity of vector patches. [ABSTRACT FROM AUTHOR]

In article number 2200582, Yu Yan, Yanjing Su, and co-workers design a novel Mn-added 13Cr austenitic stainless steel with high strength through a partial-recrystallization technique and test with in-situ hydrogen-charging incremental step loading technique, which increases hydrogen-induced cracking threshold stress by more than 30% while retains good general corrosion performance. [Extracted from the article]

The low yield strength (~300 MPa) of twinning-induced plasticity (TWIP) steels greatly limits their structural applications in the industrial field. Conventional strengthening mechanisms usually cause an enhancement of yield strength but also a severe loss of ductility. In this research, gradient substructures were introduced in the Fe-22Mn-0.6C TWIP steels by different pre-torsional deformation in order to overcome the above limitations. The substructure evolution, mechanical properties, and their origins in gradient-substructured (GS) TWIP steels were measured and compared by electron backscattered diffraction (EBSD), monotonous and loading-unloading-reloading (LUR) tensile tests. It was found that a simple torsional treatment could prepare gradient twins and dislocations in coarse-grained TWIP steel samples depending on torsional strain. The uniaxial tensile tests indicated that a superior combination of high yield strength, high ultimate strength, and considerable ductility was simultaneously obtained in the GS samples. The high yield strength and high ultimate tensile strength were attributed to synergetic strengthening mechanisms, viz., dislocation strengthening, due to the accumulation of high density of dislocations, and very high back stress strengthening due to gradient substructure distribution, which was accommodated through pile-ups of extra geometrically necessary dislocations (GNDs) across the sample-scale. Additionally, high ductility originated from gradient substructure-induced back stress hardening. The present study is also beneficial to the design efforts of high strength and high ductility of other heterogeneous-structured TWIP alloy systems. [ABSTRACT FROM AUTHOR]

Accurate extraction of buildings using high spatial resolution imagery is essential to a wide range of urban applications. However, it is difficult to extract semantic features from a variety of complex scenes (e.g., suburban, urban and urban village areas) because various complex man-made objects usually appear heterogeneous with large intra-class and low inter-class variations. The automatic extraction of buildings is thus extremely challenging. The fully convolutional neural networks (FCNs) developed in recent years have performed well in the extraction of urban man-made objects due to their ability to learn state-of-the-art features and to label pixels end-to-end. One of the most successful FCNs used in building extraction is U-net. However, the commonly used skip connection and feature fusion refinement modules in U-net often ignore the problem of feature selection, and the ability to extract smaller buildings and refine building boundaries needs to be improved. In this paper, we propose a trainable chain fully convolutional neural network (CFCN), which fuses high spatial resolution unmanned aerial vehicle (UAV) images and the digital surface model (DSM) for building extraction. Multilevel features are obtained from the fusion data, and an improved U-net is used for the coarse extraction of the building. To solve the problem of incomplete extraction of building boundaries, a U-net network is introduced by chain, which is used for the introduction of a coarse building boundary constraint, hole filling, and "speckle" removal. Typical areas such as suburban, urban, and urban villages were selected for building extraction experiments. The results show that the CFCN achieved recall of 98.67%, 98.62%, and 99.52% and intersection over union (IoU) of 96.23%, 96.43%, and 95.76% in suburban, urban, and urban village areas, respectively. Considering the IoU in conjunction with the CFCN and U-net resulted in improvements of 6.61%, 5.31%, and 6.45% in suburban, urban, and urban village areas, respectively. The proposed method can extract buildings with higher accuracy and with clearer and more complete boundaries. [ABSTRACT FROM AUTHOR]