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An Improved Northern Goshawk Optimization Algorithm for Feature Selection

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Journal of Bionic Engineering 2024年第4期21卷 2034-2072页

作者： Rongxiang Xie Shaobo Li Fengbin Wu State Key Laboratory of Public big data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China State Key Laboratory of Public big data Guizhou UniversityGuiyang550025China

Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a *** work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address the FS *** NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern *** order to overcome the disadvantages that NGO is prone to local optimum trap,slow convergence speed and low convergence accuracy,two strategies are introduced in the original NGO to boost the effectiveness of ***,a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population ***,a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and *** prove the effectiveness of the suggested DENGO,it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions,and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and ***,the proposed DENGO is used for FS,and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods,and therefore,DENGO is considered to be one of the most prospective FS ***'s code can be obtained at https://***/matlabcentral/fileexchange/158811-project1.

关键词： Northern goshawk optimization Learning strategy Hybrid differential strategy Numerical optimization Feature selection

A robust tensor watermarking algorithm for diffusion-tensor images

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 384-397页

作者： Chengmeng LIU Zhi LI Guomei WANG Long ZHENG State Key Laboratory of Public big data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China

Watermarking algorithms that use convolution neural networks have exhibited good robustness in studies of deep learning ***,after embedding watermark signals by convolution,the feature fusion eficiency of convolution is relatively low;this can easily lead to distortion in the embedded *** distortion occurs in medical images,especially in diffusion tensor images(DTIs),the clinical value of the DTI is *** address this issue,a robust watermarking algorithm for DTIs implemented by fusing convolution with a Transformer is proposed to ensure the robustness of the watermark and the consistency of sampling distance,which enhances the quality of the reconstructed image of the watermarked DTIs after embedding the watermark *** the watermark-embedding network,Ti-weighted(Tlw)images are used as prior *** correlation between T1w images and the original DTI is proposed to calculate the most significant features from the T1w images by using the Transformer *** maximum of the correlation is used as the most significant feature weight to improve the quality of the reconstructed *** the watermark extraction network,the most significant watermark features from the watermarked DTI are adequately learned by the Transformer to robustly extract the watermark signals from the watermark *** results show that the average peak signal-to-noise ratio of the watermarked DTI reaches 50.47 dB,the diffusion characteristics such as mean diffusivity and fractional anisotropy remain unchanged,and the main axis deflection angleαAc is close to *** proposed algorithm can effectively protect the copyright of the DTI and barely affects the clinical diagnosis.

关键词： Robust watermarking algorithm Transformer Image reconstruction Diffusion tensor images Soft attention Hard attention Tl-weighted images

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Understanding and improving fairness in cognitive diagnosis

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science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of big data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence school of computer science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

Unsupervised social network embedding via adaptive specific mappings

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Frontiers of computer science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG school of computer science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of big data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

Blockchain-Based Key Management Scheme Using Rational Secret Sharing

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computers, Materials & Continua 2024年第4期79卷 307-328页

作者： Xingfan Zhao Changgen Peng Weijie Tan Kun Niu State Key Laboratory of Public big data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Guizhou big data Academy Guizhou UniversityGuiyang550025China

Traditional blockchain key management schemes store private keys in the same location,which can easily lead to security issues such as a single point of ***,decentralized threshold key management schemes have become a research focus for blockchain private key *** security of private keys for blockchain user wallet is highly related to user identity authentication and digital asset *** threshold blockchain private key management schemes based on verifiable secret sharing have made some progress,but these schemes do not consider participants’self-interested behavior,and require trusted nodes to keep private key fragments,resulting in a narrow application scope and low deployment efficiency,which cannot meet the needs of personal wallet private key escrow and recovery in public *** design a private key management scheme based on rational secret sharing that considers the self-interest of participants in secret sharing protocols,and constrains the behavior of rational participants through reasonable mechanism design,making it more suitable in distributed scenarios such as the public *** proposed scheme achieves the escrow and recovery of personal wallet private keys without the participation of trusted nodes,and simulate its implementation on smart *** to other existing threshold wallet solutions and keymanagement schemes based on password-protected secret sharing(PPSS),the proposed scheme has a wide range of applications,verifiable private key recovery,low communication overhead,higher computational efficiency when users perform one-time multi-key escrow,no need for trusted nodes,and personal rational constraints and anti-collusion attack capabilities.

关键词： Blockchain smart contract rational secret sharing key management

Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

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computers, Materials & Continua 2024年第4期79卷 1659-1677页

作者： Zhanfeng Wang Lisha Yao school of computer science and Artificial Intelligence Chaohu UniversityHefei238000China school of big data and Artificial Intelligence Anhui Xinhua UniversityHefei230088China

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.

关键词： Expression recognition capsule neural network convolutional neural network

Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

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computers, Materials & Continua 2024年第3期78卷 3021-3039页

作者： Shoukun Xu Lujun Zhang Guangqi Jiang Yining Hua Yi Liu school of computer science and Artificial Intelligence Aliyun School of Big Data and School of SoftwareChangzhou UniversityChangzhou213164China Department of computer science University of AberdeenKing’s CollegeAberdeenAB243FXUK

This paper focuses on the task of few-shot 3D point cloud semantic *** some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic *** tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding ***,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their ***,we designed a multi-prototype enhancement module to enhance the prototype ***,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich ***,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category *** studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic ***,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

关键词： Few-shot point cloud semantic segmentation CapsNets

A Note of the Interpolating Sequence in Qp∩H~∞

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Wuhan University Journal of Natural sciences 2024年第1期29卷 1-6页

作者： ZHOU Jizhen SUN Hejie school of Mathematics and big data Anhui University of science and Technology

In this paper,{zn}n=1~∞ acts as an interpolating sequence for Qp∩H~∞.An analytic function f is constructed,and f(zn)=∑jλjfzj(zn)=λn,n=1,2,…for any {λn}∈l~∞,wheref and ■ belong to Qp∩H~∞.As a result,the st... 详细信息

关键词： Q_p space H∞ space F (p,p - 2,s) ∩H~∞ interpolating sequence

Computing Persistent Homology by Spanning Trees and Critical Simplices

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Research 2024年第2期2023卷 519-527页

作者： Dinghua Shi Zhifeng Chen Chuang Ma Guanrong Chen Department of Mathematics Cllege of ScienceShanghai UniversityShanghaiChina school of big data Fuzhou University of International Studies and TradeFuzhouChina Department of data science and big data Technology School of InternetAnhui UniversityHefeiChina Department of Electrical Engineering City University of Hong KongHong KongChina

Topological data analysis can extract effective information from higher-dimensional *** mathematical basis is persistent *** persistent homology can calculate topological features at different spatiotemporal scales of the dataset,that is,establishing the integrated taxonomic relation among points,lines,and ***,the simplicial network composed of all-order simplices in a simplicial complex is *** the sequence of nested simplicial subnetworks can be regarded as a discrete Morse function from the simplicial network to real values,a method based on the concept of critical simplices can be developed by searching all-order spanning *** this new method,not only the Morse function values with the theoretical minimum number of critical simplices can be obtained,but also the Betti numbers and composition of all-order cavities in the simplicial network can be calculated ***,this method is used to analyze some examples and compared with other methods,showing its effectiveness and feasibility.

关键词： Morse establishing topological