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Parametrization of generalized triangle groups and construction of substitution-box for medical image encryption 广义三角形组的参数化和医学图像加密替代盒的构建
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-14 DOI: 10.1016/j.jksuci.2024.102159
Aqsa Zafar Abbasi , Ayesha Rafiq , Lioua Kolsi
<div><div>The construction of strong encryption techniques is crucial to meet the increasing demand for secure transmission as well as storage of medical images. A substitution box (S-Box) is an important component of block ciphers and nonlinearity is an important attribute to consider while designing secure S-boxes. As a result, it is required to create new approaches for producing S-boxes with high non-linearity scores. We present a method of parametrization of the generalized triangle group <span><math><mrow><mo>〈</mo><mi>x</mi><mo>,</mo><mi>y</mi><mspace></mspace><mo>|</mo><msup><mrow><mi>x</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><msup><mrow><mi>y</mi></mrow><mrow><mn>5</mn></mrow></msup><mo>=</mo><msup><mrow><mi>w</mi></mrow><mrow><mi>k</mi></mrow></msup><mo>=</mo><mn>1</mn><mo>〉</mo></mrow></math></span> as linear groups, where <span><math><mrow><mi>w</mi><mo>=</mo><mi>x</mi><mi>y</mi><mi>x</mi><msup><mrow><mi>y</mi></mrow><mrow><mn>2</mn></mrow></msup><mi>x</mi><msup><mrow><mi>y</mi></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span> which is extended by the parametrization for triangle group <span><math><mrow><mo>〈</mo><mi>x</mi><mo>,</mo><mi>y</mi><mo>,</mo><mi>t</mi><mspace></mspace><mo>|</mo><msup><mrow><mi>x</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><msup><mrow><mi>y</mi></mrow><mrow><mn>5</mn></mrow></msup><mo>=</mo><msup><mrow><mi>t</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><msup><mrow><mrow><mo>(</mo><mi>x</mi><mi>t</mi><mo>)</mo></mrow></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><msup><mrow><mrow><mo>(</mo><mi>y</mi><mi>t</mi><mo>)</mo></mrow></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><msup><mrow><mrow><mo>(</mo><mi>x</mi><mi>y</mi><mo>)</mo></mrow></mrow><mrow><mi>k</mi></mrow></msup><mo>=</mo><mn>1</mn><mo>〉</mo></mrow></math></span>. This parametrization is then used for the construction of a highly nonlinear and secure substitution box designed for <span><math><msup><mrow><mn>2</mn></mrow><mrow><mn>8</mn></mrow></msup></math></span> elements, tailored specifically for the finite generalized triangle group case with <span><math><mrow><mi>k</mi><mo>=</mo><mn>2</mn></mrow></math></span> for <span><math><mrow><mi>θ</mi><mo>=</mo><mn>64</mn></mrow></math></span> which is parameter for all homomorphism from <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>5</mn></mrow></msub></math></span> to <span><math><mrow><mi>P</mi><mi>S</mi><mi>L</mi><mrow><mo>(</mo><mn>2</mn><mo>,</mo><mi>q</mi><mo>)</mo></mrow></mrow></math></span>, possessing an order of 1200. We rigorously evaluate and analyze various common security indicators associated with the proposed substitution box. The proposed S-box is evaluated for picture encryption using various statistical approaches. Comparative analysis and additional scrutiny reveal promising attributes, affirming its suitability, efficacy, and potential applicability in the domain of medical image encryption. Our S-box achieves the necessary conditions for secu
要满足医疗图像安全传输和存储日益增长的需求,构建强大的加密技术至关重要。置换盒(S-Box)是块密码的重要组成部分,而非线性是设计安全 S-box 时需要考虑的重要属性。因此,需要创造新的方法来生成具有高非线性分数的 S-Box。我们提出了一种将广义三角形群 〈x,y|x2=y5=wk=1〉参数化为线性群的方法,其中 w=xyxy2xy4 由三角形群 〈x,y,t|x2=y5=t2=(xt)2=(yt)2=(xy)k=1〉的参数化扩展而来。然后,我们利用这种参数化方法构建了一个为 28 个元素设计的高度非线性和安全的置换盒,该置换盒专门为有限广义三角形群的情况量身定制,θ=64 时的 k=2 是从 H5 到 PSL(2,q) 的所有同构的参数,具有 1200 阶。我们严格评估和分析了与所提替换盒相关的各种常见安全指标。我们使用各种统计方法对所提出的 S 盒进行了图片加密评估。对比分析和额外的审查揭示了其有前途的属性,肯定了它在医学图像加密领域的适用性、有效性和潜在适用性。我们的 S-box 满足了安全通信和图像加密的必要条件,这一点已通过积极的检验结果得到证实。
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We present a method of parametrization of the generalized triangle group &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;〈&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;〉&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; as linear groups, where &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; which is extended by the parametrization for triangle group &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;〈&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mo&gt;|&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;x&lt;/mi&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;〉&lt;/mo&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. This parametrization is then used for the construction of a highly nonlinear and secure substitution box designed for &lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; elements, tailored specifically for the finite generalized triangle group case with &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;k&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;θ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;64&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; which is parameter for all homomorphism from &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; to &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;mi&gt;S&lt;/mi&gt;&lt;mi&gt;L&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;q&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, possessing an order of 1200. We rigorously evaluate and analyze various common security indicators associated with the proposed substitution box. The proposed S-box is evaluated for picture encryption using various statistical approaches. Comparative analysis and additional scrutiny reveal promising attributes, affirming its suitability, efficacy, and potential applicability in the domain of medical image encryption. Our S-box achieves the necessary conditions for secu","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102159"},"PeriodicalIF":5.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Anomalous behavior detection based on optimized graph embedding representation in social networks 基于优化图嵌入表示的社交网络异常行为检测
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-13 DOI: 10.1016/j.jksuci.2024.102158
Ling Xing , Shiyu Li , Qi Zhang , Honghai Wu , Huahong Ma , Xiaohui Zhang

Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.

社交网络中的异常行为会导致隐私泄露和虚假信息传播。本文提出了一种基于优化图嵌入表示的异常行为检测方法。具体来说,首先将用户行为日志提取为社交网络用户行为时态知识图谱,在此基础上利用图嵌入表示方法将用户行为知识图谱中的网络拓扑和时态信息转化为结构嵌入向量和时态信息嵌入向量,并利用混合注意力机制将两类向量合并得到最终的实体嵌入,完成对用户行为时态知识图谱的预测和补充。我们利用图神经网络,将用户行为的时间信息作为时间约束,同时捕捉用户行为信息和语义信息。它将两部分信息转化为向量进行串联和线性变换,从而得到整个子图的综合表示向量,并联合深度学习模型对异常行为进行评估。最后,我们在 Yelp 数据集上进行了实验,验证了我们的方法在 F1 分数上实现了 9.56% 的提升。
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引用次数: 0
Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs 基于 LSM 树的键值存储在 ZNS SSD 上的高效损耗平级感知数据放置
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-08 DOI: 10.1016/j.jksuci.2024.102156
Runyu Zhang, Lening Zhou, Mingjie Li, Yunlin Tan, Chaoshu Yang

Emerging Zoned Namespace (ZNS) is a new-style Solid State Drive (SSD) that manages data in a zoned manner, which can achieve higher performance by strictly obeying the sequential write mode in each zone and alleviating the redundant overhead of garbage collections. Unfortunately, flash memory usually has a serious problem with limited program/erase cycles. Meanwhile, inappropriate data placement strategy of storage systems can lead to imbalanced wear among zones, severely reducing the lifespan of ZNS SSDs. In this paper, we propose a Wear-Leveling-Aware Data Placement (WADP) to solve this problem with negligible performance cost. First, WADP employs a wear-aware empty zone allocation algorithm to quantify the resets of zones and choose the less-worn zone for each allocation. Second, to prevent long-term zone occupation of infrequently written data (namely cold data), we propose a wear-leveling cold zone monitoring mechanism to identify cold zones dynamically. Finally, WADP adopts a real-time I/O pressure-aware data migration mechanism to adaptively migrate cold data for achieving wear-leveling among zones. We implement the proposed WADP in ZenFS and evaluate it with widely used workloads. Compared with state-of-the-art solutions, i.e., LIZA and FAR, the experimental results show that WADP can significantly reduce the standard deviation of zone resets while maintaining decent performance.

新出现的分区命名空间(ZNS)是一种新型固态硬盘(SSD),它以分区方式管理数据,通过严格遵守各区的顺序写入模式,减轻垃圾回收的冗余开销,从而实现更高的性能。遗憾的是,闪存通常存在程序/擦除周期有限的严重问题。同时,存储系统不恰当的数据放置策略会导致各区之间的磨损不平衡,严重缩短 ZNS SSD 的使用寿命。在本文中,我们提出了一种可感知磨损的数据放置(WADP)来解决这一问题,其性能成本几乎可以忽略不计。首先,WADP 采用磨损感知空区分配算法来量化区的重置,并为每次分配选择磨损较少的区。其次,为防止不经常写入的数据(即冷数据)长期占用区域,我们提出了一种损耗水平冷区监控机制,以动态识别冷区。最后,WADP 采用实时 I/O 压力感知数据迁移机制,自适应地迁移冷数据,以实现区域间的损耗均衡。我们在 ZenFS 中实现了所提出的 WADP,并用广泛使用的工作负载对其进行了评估。实验结果表明,与最先进的解决方案(即 LIZA 和 FAR)相比,WADP 可以显著降低区域重置的标准偏差,同时保持良好的性能。
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引用次数: 0
Structure recovery from single omnidirectional image with distortion-aware learning 利用失真感知学习从单幅全向图像中恢复结构
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-08 DOI: 10.1016/j.jksuci.2024.102151
Ming Meng , Yi Zhou , Dongshi Zuo , Zhaoxin Li , Zhong Zhou

Recovering structures from images with 180 or 360 FoV is pivotal in computer vision and computational photography, particularly for VR/AR/MR and autonomous robotics applications. Due to varying distortions and the complexity of indoor scenes, recovering flexible structures from a single image is challenging. We introduce OmniSRNet, a comprehensive deep learning framework that merges distortion-aware learning with bidirectional LSTM. Utilizing a curated dataset with optimized panorama and expanded fisheye images, our framework features a distortion-aware module (DAM) for extracting features and a horizontal and vertical step module (HVSM) of LSTM for contextual predictions. OmniSRNet excels in applications such as VR-based house viewing and MR-based video surveillance, achieving leading results on cuboid and non-cuboid datasets. The code and dataset can be accessed at https://github.com/mmlph/OmniSRNet/.

从 180∘ 或 360∘ FoV 的图像中恢复结构是计算机视觉和计算摄影的关键,尤其是在 VR/AR/MR 和自主机器人应用中。由于室内场景的畸变和复杂性各不相同,从单张图像中恢复灵活的结构具有挑战性。我们介绍了 OmniSRNet,这是一种综合深度学习框架,它将失真感知学习与双向 LSTM 相结合。利用包含优化全景和扩展鱼眼图像的数据集,我们的框架具有用于提取特征的失真感知模块(DAM)和用于上下文预测的 LSTM 水平和垂直阶跃模块(HVSM)。OmniSRNet 在基于 VR 的房屋查看和基于 MR 的视频监控等应用中表现出色,在立方体和非立方体数据集上取得了领先的结果。代码和数据集可通过 https://github.com/mmlph/OmniSRNet/ 访问。
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引用次数: 0
Performance analysis of cloud resource allocation scheme with virtual machine inter-group asynchronous failure 带有虚拟机组间异步故障的云资源分配方案的性能分析
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-07 DOI: 10.1016/j.jksuci.2024.102155
Yuan Zhao , Kang Chen , Hongmin Gao , Yan Li

The recent rapid expansion of cloud computing has led to the prominence of Cloud Data Center (CDC) emerging. However, user requests’ waiting time might be greatly increased for a single physical machine (PM) in the CDC. We provide a cloud resource allocation scheme with virtual machine (VM) inter-group asynchronous failure. This method improves requests’ throughput and reduces wait time of requests. In particular, two PMs with different service rates for mapping multiple VMs are deployed in order to equally distribute cloud users’ requests, and we assume that the two PMs will fail and repair at different probabilities. A finite cache is also introduced to reduce the requests’ blocking rate. We model the VMs and user requests and create a 3-dimensional Markov chain (3DMC) to gauge the requests’ performance metrics. Numerical experiments are performed to obtain multiple performance metrics graphs for the requests. By comparing our scheme with the traditional cloud resource allocation scheme that involves synchronization failure in VM, we find that our scheme has an improvement in throughput, and each scheme has advantages and disadvantages in blocking rate of requests.

近年来,云计算的迅速发展使云数据中心(CDC)崭露头角。然而,对于云数据中心中的单个物理机(PM)来说,用户请求的等待时间可能会大大增加。我们提供了一种虚拟机(VM)组间异步故障的云资源分配方案。这种方法提高了请求的吞吐量,减少了请求的等待时间。特别是,为了平均分配云用户的请求,我们部署了两个具有不同服务速率的 PM 来映射多个虚拟机,并假设这两个 PM 将以不同的概率发生故障和修复。我们还引入了有限缓存,以降低请求阻塞率。我们对虚拟机和用户请求进行建模,并创建一个三维马尔可夫链(3DMC)来衡量请求的性能指标。通过数值实验,我们获得了请求的多个性能指标图。通过将我们的方案与涉及虚拟机同步故障的传统云资源分配方案进行比较,我们发现我们的方案在吞吐量方面有所改善,而且两种方案在请求阻塞率方面各有利弊。
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引用次数: 0
LDNet: High Accuracy Fish Counting Framework using Limited training samples with Density map generation Network LDNet:利用密度图生成网络的有限训练样本实现高精度鱼类计数框架
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-07 DOI: 10.1016/j.jksuci.2024.102143
Ximing Li , Yitao Zhuang , Baihao You , Zhe Wang , Jiangsan Zhao , Yuefang Gao , Deqin Xiao

Fish counting is crucial in fish farming. Density map-based fish counting methods hold promise for fish counting in high-density scenarios; however, they suffer from ineffective ground truth density map generation. High labeling complexities and disturbance to fish growth during data collection are also challenging to mitigate. To address these issues, LDNet, a versatile network with attention implemented is introduced in this study. An imbalanced Optimal Transport (OT)-based loss function was used to effectively supervise density map generation. Additionally, an Image Manipulation-Based Data Augmentation (IMBDA) strategy was applied to simulate training data from diverse scenarios in fixed viewpoints in order to build a model that is robust to different environmental changes. Leveraging a limited number of training samples, our approach achieved notable performances with an 8.27 MAE, 9.97 RMSE, and 99.01% Accuracy on our self-curated Fish Count-824 dataset. Impressively, our method also demonstrated superior counting performances on both vehicle count datasets CARPK and PURPK+, and Penaeus_1k Penaeus Larvae dataset when only 5%–10% of the training data was used. These outcomes compellingly showcased our proposed approach with a wide applicability potential across various cases. This innovative approach can potentially contribute to aquaculture management and ecological preservation through counting fish accurately.

鱼类计数在养鱼业中至关重要。基于密度图的鱼类计数方法有望用于高密度情况下的鱼类计数;然而,这些方法存在无法有效生成地面实况密度图的问题。在数据采集过程中,标记复杂度高和对鱼类生长的干扰也是难以解决的问题。为了解决这些问题,本研究引入了一种具有注意力的多功能网络 LDNet。基于最优传输(OT)的不平衡损失函数被用来有效监督密度图的生成。此外,还采用了基于图像处理的数据增强(IMBDA)策略,在固定视角下模拟来自不同场景的训练数据,以建立一个对不同环境变化具有鲁棒性的模型。利用有限的训练样本,我们的方法在自编的鱼类计数-824 数据集上取得了显著的性能,最大误差为 8.27,均方根误差为 9.97,准确率为 99.01%。令人印象深刻的是,我们的方法还在车辆计数数据集 CARPK 和 PURPK+ 以及 Penaeus_1k Penaeus Larvae 数据集(仅使用 5%-10%的训练数据)上表现出卓越的计数性能。这些结果充分展示了我们提出的方法在各种情况下的广泛适用性。这种创新方法可以通过准确计数鱼类,为水产养殖管理和生态保护做出潜在贡献。
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引用次数: 0
Leveraging syntax-aware models and triaffine interactions for nominal compound chain extraction 利用语法感知模型和三石蜡相互作用提取名词化合物链
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-07 DOI: 10.1016/j.jksuci.2024.102153
Yinxia Lou , Xun Zhu , Ming Chen , Donghong Ji , Junxiang Zhou

Recently, Nominal Compound Chain Extraction (NCCE) has been proposed to detect related mentions in a document to improve understanding of the document’s topic. NCCE involves longer span detection and more complicated rules for relation decisions, making it more difficult than previous chain extraction tasks, such as coreference resolution. Current methods achieve certain progress on the NCCE task, but they suffer from insufficient syntax information utilization and incomplete mention relation mining, which are helpful for NCCE. To fill these gaps, we propose a syntax-guided model using a triaffine interaction to improve the performance of the NCCE task. Instead of solely relying on the text information to detect compound mentions, we also utilize the noun-phrase (NP) boundary information in constituency trees to incorporate prior boundary knowledge. In addition, we use biaffine and triaffine operations to mine the mention interactions in the local and global context of a document. To show the effectiveness of our methods, we conduct a series of experiments on a human-annotated NCCE dataset. Experimental results show that our model significantly outperforms the baseline systems. Moreover, in-depth analyses reveal the effect of utilizing syntactic information and mention interactions in the local and global contexts.

最近,有人提出了名词复合链提取(NCCE),用于检测文档中的相关提及,以提高对文档主题的理解。NCCE 涉及更长的跨度检测和更复杂的关系判定规则,因此比以往的链提取任务(如核心参照解析)更加困难。目前的方法在 NCCE 任务上取得了一定的进展,但也存在语法信息利用不足和提及关系挖掘不完整等问题,而这些问题对 NCCE 都有帮助。为了弥补这些不足,我们提出了一种语法引导模型,利用三方交互来提高 NCCE 任务的性能。我们不再单纯依赖文本信息来检测复合提及,而是还利用选区树中的名词短语(NP)边界信息来纳入先验边界知识。此外,我们还使用双峰和三峰运算来挖掘文档局部和全局上下文中的提及交互。为了证明我们的方法的有效性,我们在人工标注的 NCCE 数据集上进行了一系列实验。实验结果表明,我们的模型明显优于基线系统。此外,深入分析揭示了在局部和全局上下文中利用句法信息和提及交互的效果。
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引用次数: 0
Heterogeneous network link prediction based on network schema and cross-neighborhood attention 基于网络模式和交叉邻域关注的异构网络链接预测
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-06 DOI: 10.1016/j.jksuci.2024.102154
Pengtao Wang , Jian Shu , Linlan Liu

Heterogeneous network link prediction is a hot topic in the analysis of networks. It aims to predict missing links in the network by utilizing the rich semantic information present in the heterogeneous network, thereby enhancing the effectiveness of relevant data mining tasks. Existing heterogeneous network link prediction methods utilize meta-paths or meta-graphs to extract semantic information, heavily relying on the priori knowledge. This paper proposes a heterogeneous network link prediction based on network schema and cross-neighborhood attention method (HNLP-NSCA). The heterogeneous node features are projected into a shared latent vector space using fully connected layers. To resolve the issue of prior knowledge dependence on meta-path, the semantic information is extracted by using network schema structures uniquely in heterogeneous networks. Node features are extracted based on the relevant network schema instances, avoiding the problem of meta-path selection. The neighborhood interaction information of input node pairs is sensed via cross-neighborhood attention, strengthening the nonlinear mapping capability of the link prediction. The resulting cross-neighborhood interaction vectors are combined with the node feature vectors and fed into a multilayer perceptron for link prediction. Experimental results on four real-world datasets demonstrate that the proposed HNLP-NSCA mothed outperforms the baseline models.

异构网络链接预测是网络分析领域的一个热门话题。它旨在利用异构网络中丰富的语义信息预测网络中缺失的链接,从而提高相关数据挖掘任务的效率。现有的异构网络链接预测方法利用元路径或元图提取语义信息,严重依赖先验知识。本文提出了一种基于网络模式和交叉邻域关注法(HNLP-NSCA)的异构网络链接预测方法。利用全连接层将异构节点特征投射到共享的潜在向量空间。为解决元路径的先验知识依赖问题,在异构网络中使用网络模式结构提取语义信息。根据相关的网络模式实例提取节点特征,避免了元路径选择问题。通过交叉邻域关注感知输入节点对的邻域交互信息,加强了链接预测的非线性映射能力。由此产生的交叉邻域交互向量与节点特征向量相结合,并输入多层感知器进行链接预测。在四个实际数据集上的实验结果表明,所提出的 HNLP-NSCA 模型优于基线模型。
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引用次数: 0
Spatial relaxation transformer for image super-resolution 用于图像超分辨率的空间松弛变换器
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-06 DOI: 10.1016/j.jksuci.2024.102150
Yinghua Li , Ying Zhang , Hao Zeng , Jinglu He , Jie Guo

Transformer-based approaches have demonstrated remarkable performance in image processing tasks due to their ability to model long-range dependencies. Current mainstream Transformer-based methods typically confine self-attention computation within windows to reduce computational burden. However, this constraint may lead to grid artifacts in the reconstructed images due to insufficient cross-window information exchange, particularly in image super-resolution tasks. To address this issue, we propose the Multi-Scale Texture Complementation Block based on Spatial Relaxation Transformer (MSRT), which leverages features at multiple scales and augments information exchange through cross windows attention computation. In addition, we introduce a loss function based on the prior of texture smoothness transformation, which utilizes the continuity of textures between patches to constrain the generation of more coherent texture information in the reconstructed images. Specifically, we employ learnable compressive sensing technology to extract shallow features from images, preserving image features while reducing feature dimensions and improving computational efficiency. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method outperforms previous state-of-the-art approaches in both qualitative and quantitative evaluations.

基于变换器的方法由于能够模拟长距离依赖关系,因此在图像处理任务中表现出卓越的性能。目前基于变换器的主流方法通常将自注意计算限制在窗口内,以减轻计算负担。然而,由于跨窗口信息交换不足,这种限制可能会导致重建图像中出现网格伪影,尤其是在图像超分辨率任务中。为了解决这个问题,我们提出了基于空间松弛变换器(MSRT)的多尺度纹理补全块,它利用了多个尺度的特征,并通过跨窗注意力计算增强了信息交换。此外,我们还引入了基于纹理平滑度变换先验的损失函数,该函数利用斑块间纹理的连续性来限制在重建图像中生成更连贯的纹理信息。具体来说,我们采用可学习的压缩传感技术从图像中提取浅层特征,在保留图像特征的同时减少特征维数并提高计算效率。在多个基准数据集上进行的广泛实验表明,我们的方法在定性和定量评估方面都优于之前的先进方法。
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引用次数: 0
RSA-RRT: A path planning algorithm based on restricted sampling area RSA-RRT:基于受限采样区域的路径规划算法
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-06 DOI: 10.1016/j.jksuci.2024.102152
Lixin Zhang , Hongtao Yin , Ang Li , Longbiao Hu , Lan Duo

The rapidly-exploring random tree (RRT) algorithm has a wide range of applications in the field of path planning. However, conventional RRT algorithm suffers from low planning efficiency and long path length, making it unable to handle complex environments. In response to the above problems, this paper proposes an improved RRT algorithm based on restricted sampling area (RSA-RRT). Firstly, to address the problem of low efficiency, a restricted sampling area strategy is proposed. By dynamically restricting the sampling area, the number of invalid sampling points is reduced, thus improving planning efficiency. Then, for the path planning problem in narrow areas, a fixed-angle sampling strategy is proposed, which improves the planning efficiency in narrow areas by conducting larger step size sampling with a fixed angle. Finally, a multi-triangle optimization strategy is proposed to address the problem of longer and more tortuous paths. The effectiveness of RSA-RRT algorithm is verified through improved strategy performance verification and ablation experiments. Comparing with other algorithms in different environments, the results show that RSA-RRT algorithm can obtain shorter paths while taking less time, effectively balancing the path quality and planning speed, and it can be applied in complex real-world environments.

快速探索随机树(RRT)算法在路径规划领域有着广泛的应用。然而,传统的 RRT 算法存在规划效率低、路径长度长等问题,无法应对复杂环境。针对上述问题,本文提出了一种基于受限采样区域的改进 RRT 算法(RSA-RRT)。首先,针对效率低的问题,提出了限制采样区域策略。通过动态限制采样区域,减少无效采样点的数量,从而提高规划效率。然后,针对狭窄区域的路径规划问题,提出了固定角度采样策略,通过在固定角度下进行较大步长的采样,提高了狭窄区域的规划效率。最后,针对较长和较曲折的路径问题,提出了多三角优化策略。通过改进的策略性能验证和消融实验,验证了 RSA-RRT 算法的有效性。与其他算法在不同环境下的对比结果表明,RSA-RRT 算法可以在耗时更短的情况下获得更短的路径,有效平衡了路径质量和规划速度,可以应用于复杂的实际环境。
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引用次数: 0
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Journal of King Saud University-Computer and Information Sciences
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