Shuffle Operation Research Articles

Mini-Reactor Proliferation-Resistant Fuel with Burnable Gadolinia in Once-Through Operation Cycle Performance Verification

A miniature nuclear reactor is desirable for deployment as a localized nuclear power station in support of a carbon-free power supply. Coupling aspects of proliferation-resistant fuel with natural burnable absorber loading are evaluated for once-through operation cycle performance to minimize the need for refueling and fuel shuffling operations. The incorporation of 0.075 wt.% 237Np provides favorable plutonium isotopic vectors throughout an operational lifetime of 5.5 years. providing 35 MWe. Core performance was assessed using a verification-by-comparison approach for core designs with or without 237Np and/or gadolinia burnable absorber. Burnup Monte Carlo calculations were performed via MCOS coupling of MCNP and ORIGEN to an achievable burnup of ~62.5 GWd/t. The results demonstrate a minimal penalty to reactor performance due to the addition of these materials as compared against the reference design. Coupling of a proliferation-resistant fuel concept with a uniform loading of natural gadolinia burnable absorber for LEU+ fuel (7.5 wt.% 235U/U UO2) provides favorable excess reactivity considerations with minimized concerns for additional residual waste and more uniform distribution of un-depleted 235U in discharged fuel assemblies.

Optimizing Dilithium Implementation with AVX2/-512

Dilithium is a signature scheme that is currently being standardized to the Module-Lattice-Based Digital Signature Standard by NIST. It is believed to be secure even against attacks from large-scale quantum computers based on lattice problems. The implementation efficiency is important for promoting the migration of current cryptography algorithms to post-quantum cryptography algorithms. In this paper, we optimize the implementation of Dilithium with several new approaches proposed. Firstly, we improve the efficiency of parallel NTT implementations. The overhead of shuffling operations is reduced in our implementations, and fewer loading instructions are invoked for the precomputations. Then, we optimize the sampling and bit-packing of polynomial coefficients in Dilithium. We can handle double the number of coefficients within one register using a new approach for the sampling of secret key polynomials. The approaches proposed in this paper are applicable to implementations under AVX2 and AVX-512 instruction sets. Take Dilithium2 as an illustration, our AVX2 implementation demonstrates improvements of 22.7%, 16.9%, and 13.5% for KeyGen, Sign, and Verify compared to the previous implementation.

Different life cycles of rice pests’ images recognition based on adaptive lightweight DC-ghost module

Timely protection and treatment of rice crops require accurate identification of pests. For this purpose, this paper introduces a lightweight network called DC-GhostNet (DO-Channel Shuffle GhostNet). This network combines DO convolution, feature extraction and fusion, and IECA lightweight attention mechanism with adaptive k-values to identify rice pests effectively. In the Ghost Module, the DO convolution is used to replace the DW convolution in the Ghost convolution to enhance the feature extraction more efficiently, without increasing the model parameters and computational effort. To conquer the challenge of rice pest image feature extraction in different complex scenes, the channel shuffle operation is added at the end of the Ghost Module to extract deep global features and optimized them. The model is then subjected to a transfer learning approach to highly boost the network’s performance. Experiments show that our method’s accuracy, precision, recall, and F1 scores on the test dataset of 15 types of rice pests are, 89.15 %, 87.13 %, 85.77 %, and 86.45 %, respectively, which is 7.44 percentage points, 6.42 percentage points, 11.96 percentage points, and 9.35 percentage points higher than the baseline GhostNet. The DC-GhostNet model contains 4.35 M parameters and 141.99 M floating point operations FLOPs, which are 16 % and 9 % less than GhostNet, respectively. The performance of the proposed model under different life cycles of ovum, larvae, and adults is also optimal compared to other networks, and the final inference time of the model for a single image is under 13.1 ms, demonstrating both its speedy detection and lightweight deployment capabilities.

Research on image classification based on residual group multi-scale enhanced attention network

To address the challenges associated with insufficient feature extraction and gradient degradation encountered when dealing with deepening network structures in image classification tasks, this paper presents a ResGMEANet(Residual Group Multi-scale Enhanced Attention Network). The model introduces a multi-scale attention enhancement module. This design draws inspiration from the original model's capability to independently capture feature correlations in channels and spaces. By implementing shuffle operations and feature transformations within the group, our method expands the receptive field through the utilization of multiple convolution kernels. Additionally, we incorporate an improved tensor synthesis attention, building upon the traditional convolution attention, to derive attention feature maps after feature enhancement. Evaluation on the CIFAR-10 and CIFAR-100 datasets shows that ResGMEANet outperforms both the original backbone model and several existing mainstream methods in classification accuracy. This work aims to provide a new perspective for the future by combining residual neural networks with different attention mechanisms.

LDSG-Net: an efficient lightweight convolutional neural network for acute hypotensive episode prediction during ICU hospitalization

Objective. Acute hypotension episode (AHE) is one of the most critical complications in intensive care unit (ICU). A timely and precise AHE prediction system can provide clinicians with sufficient time to respond with proper therapeutic measures, playing a crucial role in saving patients’ lives. Recent studies have focused on utilizing more complex models to improve predictive performance. However, these models are not suitable for clinical application due to limited computing resources for bedside monitors. Approach. To address this challenge, we propose an efficient lightweight dilated shuffle group network. It effectively incorporates shuffling operations into grouped convolutions on the channel and dilated convolutions on the temporal dimension, enhancing global and local feature extraction while reducing computational load. Main results. Our benchmarking experiments on the MIMIC-III and VitalDB datasets, comprising 6036 samples from 1304 patients and 2958 samples from 1047 patients, respectively, demonstrate that our model outperforms other state-of-the-art lightweight CNNs in terms of balancing parameters and computational complexity. Additionally, we discovered that the utilization of multiple physiological signals significantly improves the performance of AHE prediction. External validation on the MIMIC-IV dataset confirmed our findings, with prediction accuracy for AHE 5 min prior reaching 93.04% and 92.04% on the MIMIC-III and VitalDB datasets, respectively, and 89.47% in external verification. Significance. Our study demonstrates the potential of lightweight CNN architectures in clinical applications, providing a promising solution for real-time AHE prediction under resource constraints in ICU settings, thereby marking a significant step forward in improving patient care.

Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction

The variational quantum eigensolver (VQE) is a leading strategy that exploits noisy intermediate-scale quantum (NISQ) machines to tackle chemical problems. It is expected to demonstrate quantum advantage when handling a large number of qubits, where the density matrix cannot be processed efficiently on classical computers. To gain such computational advantages on large-scale problems, a feasible solution is the QUantum DIstributed Optimization (QUDIO) scheme, which partitions the original problem into K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{K}$$\\end{document} subproblems and allocates them to K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{K}$$\\end{document} quantum machines followed by the parallel optimization. Despite the provable acceleration ratio, the efficiency of QUDIO may heavily degrade by the synchronization operation. To conquer this issue, here we propose Shuffle-QUDIO to involve shuffle operations into local Hamiltonians during the quantum distributed optimization. Compared with QUDIO, Shuffle-QUDIO significantly reduces the communication frequency among quantum processors and simultaneously achieves better trainability. Particularly, we prove that Shuffle-QUDIO enables a faster convergence rate over QUDIO. Extensive numerical experiments are conducted to verify that Shuffle-QUDIO allows both a wall-clock time speedup and low approximation error in the tasks of estimating the ground state energy of a molecule. We empirically demonstrate that our proposal can be seamlessly integrated with other acceleration techniques, such as operator grouping, to further improve the efficacy of VQE.

Insulating Glove Wearing State Detection for Substation Personnel Based on Faster‐YOLOv8

Electrical workers must wear insulating gloves during daily maintenance. Detecting the condition of these gloves is significant for power safety. Due to the limitations of existing detection algorithms in accuracy and lightweight on power construction sites, this paper proposes a Faster‐YOLOv8 algorithm to detect the wearing condition of insulating gloves. First, it replaces the Bottleneck in the C2f module with the Faster Block and introduces a new C2Faster module. This reduces model parameters while improving performance. Second, the feature pyramid is improved to enhance the combination of deep and shallow semantic information. This improvement aims to improve the network's focus on detecting small targets. Finally, the GSConv is introduced into the neck network to reduce the model parameters and calculation. Additionally, GSConv enhances feature information exchange through shuffle operation. The effectiveness of the Faster‐YOLOv8 algorithm is verified through comparisons with mainstream algorithms such as the Faster‐RCNN algorithm, SSD algorithm, YOLOv5 algorithm, and YOLOv7‐tiny algorithm. The results demonstrate that the Faster‐YOLOv8 algorithm outperforms the above algorithms in terms of detection accuracy. Compared with the original YOLOv8 network model, the mAP is improved by 3.8%, the mAP of the wrong glove increased by 9.8%, the model size is reduced by 33.3%, and the detection speed can reach 81 FPS. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation.

Biomedical image segmentation is essential in clinical practices, offering critical insights for accurate diagnosis and strategic treatment approaches. Nowadays, self-attention-based networks have achieved competitive performance in both natural language processing and computer vision, but the computational cost has reduced their popularity in practical applications. The recent study of Convolutional Neural Network (CNN) explores linear functions within modified CNN layer demonstrating pure CNN-based networks can still achieve competitive results against Vision Transformer (ViT) in biomedical image segmentation, with fewer parameters. The modified CNN, i.e., Depthwise CNN, however, leaves the features cleaved off in the channel dimension and prevents the extraction of features in the perspective of channel interaction. To effectively further explore the feature learning power of modified CNN with biomedical image segmentation, we design a lightweight multi-convolutional multi-scale convolutional network block (MSConvNeXt) for U-shape symmetrical network. Specifically, a network block consisting of a depthwise CNN, a deformable CNN, and a dilated CNN is proposed to capture semantic feature information effectively while with low computational cost. Furthermore, channel shuffling operation is proposed to dynamically promote an efficient feature fusion among the feature maps. The network block hereby is properly deployed within U-shape symmetrical encoder-decoder style network, named TriConvUNeXt. The proposed network is validated on a public benchmark dataset with a comprehensive evaluation in both computational cost and segmentation performance against 13 baseline methods. Specifically, TriConvUNeXt achieves 1% higher than UNet and TransUNet in Dice-Coefficient while 81% and 97% lower in computational cost. The implementation of TriConvUNeXt is made publicly accessible via https://github.com/ziyangwang007/TriConvUNeXt .

Enhancing Robust Liver Cancer Diagnosis: A Contrastive Multi-Modality Learner with Lightweight Fusion and Effective Data Augmentation

This article explores the application of self-supervised contrastive learning in the medical domain, focusing on classification of multi-modality Magnetic Resonance (MR) images. To address the challenges of limited and hard-to-annotate medical data, we introduce multi-modality data augmentation (MDA) and cross-modality group convolution (CGC). In the pre-training phase, we leverage Simple Siamese networks to maximize the similarity between two augmented MR images from a patient, without a handcrafted pretext task. Our approach also combines 3D and 2D group convolution with a channel shuffle operation to efficiently incorporate different modalities of image features. Evaluation on liver MR images from a well-known hospital in Taiwan demonstrates a significant improvement over previous methods. This work contributes to advancing multi-modality contrastive learning, particularly in the context of medical imaging, offering enhanced tools for analyzing complex image data.

Advancing skin cancer diagnosis with a multi‐branch ShuffleNet architecture

AbstractIn this study, we present an innovative approach for enhancing skin cancer classification through a multi‐branch architecture inspired by ShuffleNet. Our methodology focuses on improving feature extraction and representation, emphasizing cross‐channel information exchange to achieve superior accuracy. The architecture comprises three branches: a primary feature enhancement branch, a parallel feature enhancement branch, and a global feature aggregation branch. We employ transposed convolution layers for upsampling, cross‐channel normalization, and the Swish activation function to enhance feature representations. Channel shuffle operations and group convolutions stimulate cross‐channel information exchange in both branches. The global feature aggregation branch utilizes global average pooling and depth concatenation to combine features from all branches. Subsequent Swish activation, followed by fully connected layers and softmax activation, yields class probabilities for precise skin cancer classification. This multi‐branch framework offers a promising avenue for accurate and informed medical image analysis in skin cancer detection. Integral to this research is the utilization of the ISIC2019 and ISIC 2020 datasets, encompassing diverse dermoscopic images from multiple sources. By leveraging these datasets, the methodology capitalizes on comprehensive data for precise skin cancer detection, thereby advancing medical image analysis.

Image encryption algorithm based on a new 3D chaotic system using cellular automata

Cryptography and steganography are usual methods for safe transfer of information, which encryption algorithms turn original images into unreadable noise-like images. This paper presents a novel symmetric cryptosystem designed for the transmission of RGB color images through open channels and our goal is to provide a secure cryptography algorithm against cropping and noise attacks. The encryption algorithm is based on a suitable 3D hybrid chaotic system, with high Lyapunov exponent value, leveraging the advantages of common chaos maps while addressing their inherent limitations. To further enhance security, a novel pixel shuffle operator is employed to eliminate any potential neighborhood relations between image pixels. The encryption process incorporates reversible second-order cellular automata, which are applied to the outputs of the chaotic system. Key generation is achieved through the utilization of irreversible cellular automata. The resulting key space is large enough to resist brute-force attacks and performs a high level of sensitivity. Experimental results, including analysis of histograms, entropies, and pixel correlations, confirm the effectiveness of the proposed image encryption scheme, and prove its resilient against statistical attacks and a remarkable resistance against data loss attacks.

An Image Detection Model for Aggressive Behavior of Group Sheep

Sheep aggression detection is crucial for maintaining the welfare of a large-scale sheep breeding environment. Currently, animal aggression is predominantly detected using image and video detection methods. However, there is a lack of lightweight network models available for detecting aggressive behavior among groups of sheep. Therefore, this paper proposes a model for image detection of aggression behavior in group sheep. The proposed model utilizes the GhostNet network as its feature extraction network, incorporating the PWConv and Channel Shuffle operations into the GhostConv module. These additional modules improve the exchange of information between different feature maps. An ablation experiment was conducted to compare the detection effectiveness of the two modules in different positions. For increasing the amount of information in feature maps of the GhostBottleneck module, we applied the Inverted-GhostBottleneck module, which introduces inverted residual structure based on GhostBottleneck. The improved GhostNet lightweight feature extraction network achieves 94.7% Precision and 90.7% Recall, and its model size is only 62.7% of YOLOv5. Our improved model surpasses the original model in performance. Furthermore, it addresses the limitation of the video detection model, which was unable to accurately locate aggressive sheep. In real-time, our improved model successfully detects aggressive behavior among group sheep.

Vision Transformer-Based Ensemble Learning for Hyperspectral Image Classification

Hyperspectral image (HSI) classification, due to its characteristic combination of images and spectra, has important applications in various fields through pixel-level image classification. The fusion of spatial–spectral features is a topic of great interest in the context of hyperspectral image classification, which typically requires selecting a larger spatial neighborhood window, potentially leading to overlaps between training and testing samples. Vision Transformer (ViTs), with their powerful global modeling abilities, have had a significant impact in the field of computer vision through various variants. In this study, an ensemble learning framework for HSI classification is proposed by integrating multiple variants of ViTs, achieving high-precision pixel-level classification. Firstly, the spatial shuffle operation was introduced to preprocess the training samples for HSI classification. By randomly shuffling operations using smaller spatial neighborhood windows, a greater potential spatial distribution of pixels can be described. Then, the training samples were transformed from a 3D cube to a 2D image, and a learning framework was built by integrating seven ViT variants. Finally, a two-level ensemble strategy was employed to achieve pixel-level classification based on the results of multiple ViT variants. Our experimental results demonstrate that the proposed ensemble learning framework achieves stable and significantly high classification accuracy on multiple publicly available HSI datasets. The proposed method also shows notable classification performance with varying numbers of training samples. Moreover, herein, it is proven that the spatial shuffle operation plays a crucial role in improving classification accuracy. By introducing superior individual classifiers, the proposed ensemble framework is expected to achieve even better classification performance.

RGB depth salient object detection via cross‐modal attention and boundary feature guidance

AbstractRGB depth (RGB‐D) salient object detection (SOD) is a meaningful and challenging task, which has achieved good detection performance in dealing with simple scenes using convolutional neural networks, however, it cannot effectively handle scenes with complex contours of salient objects or similarly coloured salient objects and background. A novel end‐to‐end framework is proposed for RGB‐D SOD, which comprises of four main components: the cross‐modal attention feature enhancement (CMAFE) module, the multi‐level contextual feature interaction (MLCFI) module, the boundary feature extraction (BFE) module, and the multi‐level boundary attention guidance (MLBAG) module. The CMAFE module retains the more effective salient features by employing a dual‐attention mechanism to filter noise from two modalities. In the MLCFI module, a shuffle operation is used for high‐level and low‐level channels to promote cross‐channel information communication, and rich semantic information is extracted. The BFE module converts salient features into boundary features to generate boundary maps. The MLBAG module produces saliency maps by aggregating multi‐level boundary saliency maps to guide cross‐modal features in the decode stage. Extensive experiments are conducted on six public benchmark datasets, with the results demonstrating that the proposed model significantly outperforms 23 state‐of‐the‐art RGB‐D SOD models with regards to multiple evaluation metrics.

Dynamic multi-scale topological representation for enhancing network intrusion detection

Network intrusion detection systems (NIDS) play a crucial role in maintaining network security. However, current NIDS techniques tend to neglect the topological structures of network traffic to varying degrees. This fundamental oversight leads to challenges in handling class-imbalanced and highly dynamic network traffic. In this paper, we propose a novel dynamic multi-scale topological representation (DMTR) method for improving network intrusion detection performance. Our DMTR method achieves the perception of multi-scale topology and exhibits strong robustness. It provides accurate and stable representations even in the presence of data distribution shifts and class imbalance problems. The multi-scale topology is obtained through multiple topology lenses, which reveal topological structures from different dimensional aspects. Furthermore, to address the limitations of existing detection models based on static network traffic, the DMTR method also achieves dynamic topological representation through our proposed group shuffle operation (GSO) strategy. When new traffic data arrives, the topological representation is updated by preserving a portion of the original information without reprocessing all data. Experiments on four publicly available network traffic datasets demonstrate the feasibility and effectiveness of the proposed DMTR method in handling class imbalanced and highly dynamic network traffic.

Lightweight Distortion-Aware Network for Salient Object Detection in Omnidirectional Images

Compared with 2D image salient object detection (SOD), SOD in omnidirectional images (or 360° images) usually suffers from geometric distortion. Although existing omnidirectional image SOD (ODI-SOD) methods have improved the detection accuracy obviously, their application may be cumbersome in real scenes due to their high computational cost. To avoid distortion and reduce the computational cost simultaneously in ODI-SOD, we propose a novel lightweight distortion-aware network, named LDNet, in this paper. First, to extract features with less distortion from ODIs, we integrate the distortion-aware convolution and depth-wise separable convolution (DSConv) into distortion-aware DSConv (DDSConv) and replace the regular convolutions in the last two blocks of the ResNet-18 with DDSConvs to obtain our lightweight backbone network (LD-ResNet-18). To enhance spatial information in each channel of the extracted features at each level comprehensively, then, we propose a lightweight distortion-aware channel-wise enhancement (DCE) module (only 0.05M parameters) including DDSConvs with various dilation rates, channel shuffle operation and attention mechanism, and employ a high-to-low dense connection structure to modulate the enhanced multi-level features. Besides, we design a distortion-aware self-correlation (DSC) module (only 0.02M parameters) for mining the contextual dependency of the features via a coarse-fine strategy, and the correlated features are refined by DCE modules and integrated by another dense connection structure. The final saliency map is predicted from the densely integrated features. Compared with 12 state-of-the-art methods on two public datasets, our lightweight LDNet achieves competitive or even better performance with only 2.9M parameters and 3.4G FLOPs, which balances the efficiency and performance.

Shuffle Graph Convolutional Network for Skeleton-Based Action Recognition

A shuffle graph convolutional network (Shuffle-GCN) is proposed to recognize human action by analyzing skeleton data. It uses channel split and channel shuffle operations to process multi-feature channels of skeleton data, which reduces the computational cost of graph convolution operation. Compared with the classical two-stream adaptive graph convolutional network model, the proposed method achieves a higher precision with 1/3 of the floating-point operations (FLOPs). Even more, a channel-level topology modeling method is designed to extract more motion information of human skeleton by learning the graph topology from different channels dynamically. The performance of Shuffle-GCN is tested under 56,880 action clips from the NTU RGB+D dataset with the accuracy 96.0% and the computational complexity 12.8 GFLOPs. The proposed method offers feasible solutions for developing practical applications of action recognition.

The Asymmetric five-card trick: working with variable encoding in card-based protocols

Recently, card-based protocols have been extensively explored to illustrate relatively complex cryptographic concepts, such as Secure Multiparty Computations, so that even non-specialists can understand them. These protocols rely on three elements: a given alphabet with a set number of physical cards, encoding those cards, and shuffling operations. However, the execution is becoming over-complicated, as most follow the same encoding: fixed uniform disclosed encoding, which constrains other elements and decreases their expressiveness. This paper introduces the asymmetric five-card trick, a generalization of the five-card trick (a seminal work in the field of card-based cryptography) with the added feature of allowing variable encoding. To do so, we propose a pre-process called “handshake” that allows protocols to adapt operations based on players’ encoding. The protocol handshake is included as part of the protocol’s production phase in the proposed general framework, which is negotiated before its execution. Finally, this paper also introduces the notion of time and space complexity in card-based protocols to assess their production and execution’s lower bounds.

Lightweight Strawberry Instance Segmentation on Low-Power Devices for Picking Robots

Machine vision plays a great role in localizing strawberries in a complex orchard or greenhouse for picking robots. Due to the variety of each strawberry (shape, size, and color) and occlusions of strawberries by leaves and stems, precisely locating each strawberry brings a great challenge to the vision system of picking robots. Several methods have been developed for localizing strawberries, based on the well-known Mask R-CNN network, which, however, are not efficient running on the picking robots. In this paper, we propose a simple and highly efficient framework for strawberry instance segmentation running on low-power devices for picking robots, termed StrawSeg. Instead of using the common paradigm of “detection-then-segment”, we directly segment each strawberry in a single-shot manner without relying on object detection. In our model, we design a novel feature aggregation network to merge features with different scales, which employs a pixel shuffle operation to increase the resolution and reduce the channels of features. Experiments on the open-source dataset StrawDI_Db1 demonstrate that our model can achieve a good trade-off between accuracy and inference speed on a low-power device.

SG-Det: Shuffle-GhostNet-Based Detector for Real-Time Maritime Object Detection in UAV Images

Maritime search and rescue is a crucial component of the national emergency response system, which mainly relies on unmanned aerial vehicles (UAVs) to detect objects. Most traditional object detection methods focus on boosting the detection accuracy while neglecting the detection speed of the heavy model. However, improving the detection speed is essential, which can provide timely maritime search and rescue. To address the issues, we propose a lightweight object detector named Shuffle-GhostNet-based detector (SG-Det). First, we construct a lightweight backbone named Shuffle-GhostNet, which enhances the information flow between channel groups by redesigning the correlation group convolution and introducing the channel shuffle operation. Second, we propose an improved feature pyramid model, namely BiFPN-tiny, which has a lighter structure capable of reinforcing small object features. Furthermore, we incorporate the Atrous Spatial Pyramid Pooling module (ASPP) into the network, which employs atrous convolution with different sampling rates to obtain multi-scale information. Finally, we generate three sets of bounding boxes at different scales—large, medium, and small—to detect objects of different sizes. Compared with other lightweight detectors, SG-Det achieves better tradeoffs across performance metrics and enables real-time detection with an accuracy rate of over 90% for maritime objects, showing that it can better meet the actual requirements of maritime search and rescue.