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Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

computers, Materials & Continua 2024年第3期78卷 4091-4107页

作者： Qinyue Wu Hui Xu Mengran Liu School of Computer Science Hubei University of TechnologyWuhan430068China

Network traffic identification is critical for maintaining network security and further meeting various demands of network ***,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data *** the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic ***,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal ***,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal ***,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate *** the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)*** simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO *** experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,***,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identificati

关键词： Network security network traffic identification data analytics feature selection dung beetle optimizer

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Weakly-supervised instance co-segmentation via tensor-based salient co-peak search

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Frontiers of computer science 2024年第2期18卷 83-92页

作者： Wuxiu QUAN Yu HU Tingting DAN Junyu LI Yue ZHANG Hongmin CAI School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510665China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China

Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point ***,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual *** tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency *** proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak *** having accurate co-peaks,one can efficiently infer responses of the targeted *** on four benchmark datasets validate the superior performance of the proposed method.

关键词： weakly-supervised co-segmentation co-peak tensormatching deep network instance segmentation

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Controllable Preparation of Branched Polyolefins with Various Microstructural Units via Chain-walking Ethylene and Pentene Polymerizations

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Chinese Journal of Polymer science 2023年第6期41卷 905-914,I0008页

作者： Hong-Ju Liu Wan-Lu Tian Xiao-Yue Wang Tong Lei Pei Li Guo-Yong Xu Chao Li Shao-Jie Zhang Fu-Zhou Wang Institutes of Physical Science and Information Technology School of Computer Science and TechnologyAnhui UniversityHefei 230601China

Branched polyolefins with controllable topology structures were generated from the chain-walking(co)polymerizations of ethylene,1-pentene(1P)and 2-pentene(2P)using Brookhart-typeα-diimine Ni(II)-based catalysts possessing different para-substituted groups,{[(4-R-2-Et-6-Me-C6H2N=C)2Nap]NiBr2,Nap:1,8-naphthdiyl;R=CHMePh,Ni1;R=Ph,Ni2;R=H,Ni3}.The X-ray diffraction analysis demonstrated that the crystalline structure of Ni1′is in centrosymmetric dimer structure mode with the bimetallic Ni center connected by two bromide *** para-sec-phenethyl moiety in the catalyst Ni1 obviously improved its catalytic performance and thermal stability in the ethylene *** Ni1/Et2AlCl system showed great catalytic activities(up to 7.73×106 g·mol-1·h-1)and achieved polyethylene(PE)with alkyl chains,including Me,Et,n-Pr,n-Bu,sec-Bu branches and longer chains(Lg).Compared with the 1-pentene polymerization,this catalyst system successfully mediated the polymerization of 2P to give highly branched polymers with approximately 195 branches/1000C possessing Me,Et,and n-Pr branches and a long methylene sequence due to the monomer *** Et branches derived from 2,3-insertion is slightly less than the sum of Me and n-Pr branches derived from 3,2-insertion,indicating that the 3,2-insertion mode is a slightly favorable pathway in the polymerization of 2P.

关键词： polymerization insertion chains

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EAT: Multi-Exposure Image Fusion with Adversarial Learning and Focal Transformer

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IEEE Transactions on Multimedia 2025年 27卷 3744-3754页

作者： Tang, Wei He, Fazhi Tongji University School of Computer Science and Technology Shanghai China Wuhan University School of Computer Science Wuhan430072 China

In this paper, different from previous traditional multi-exposure image fusion (MEF) algorithms that use handdesigned feature extraction approaches or deep learning-based algorithms that utilize convolutional neural networks for information preservation, we propose a novel multi-Exposure image fusion method via Adversarial learning and focal Transformer, named EAT. In our framework, a Focal Transformer is proposed to focus on more remarkable regions and construct long-range multi-exposure relationships, with which the fusion model can simultaneously extract local and global multi-exposure properties and therefore generate promising fusion results. To further improve the fusion performance, we introduce adversarial learning to train the proposed method in an adversarial manner with the guidance of ground truth. By doing so, the fused images exhibit better visual perception and color fidelity. Extensive experiments conducted on publicly available databases provide compelling evidence that EAT surpasses other state-of-the-art approaches on both quantitative and qualitative evaluations. Furthermore, we directly employ our trained model to address another benchmark MEF dataset. The impressive fusion performance serves as evidence of the credible generalization ability of EAT. The code of the proposed method is available at https://***/tthinking/EAT. © 1999-2012 IEEE.

关键词： Image fusion

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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science China(Information sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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MemDefense: Defending against Membership Inference Attacks in IoT-based Federated Learning via Pruning Perturbations

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IEEE Transactions on Big Data 2024年 1-13页

作者： Shen, Meng Meng, Jin Xu, Ke Yu, Shui Zhu, Liehuang School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science Tsinghua University Beijing China School of Computer Science University of Technology Sydney Sydney NSW Australia

Depending on large-scale devices, the Internet of Things (IoT) provides massive data support for resource sharing and intelligent decision, but privacy risks also increase. As a popular distributed learning framework, Federated Learning (FL) is widely used because it does not need to share raw data while only parameters to collaboratively train models. However, Federated Learning is not spared by some emerging attacks, e.g., membership inference attack. Therefore, for IoT devices with limited resources, it is challenging to design a defense scheme against the membership inference attack ensuring high model utility, strong membership privacy and acceptable time efficiency. In this paper, we propose MemDefense, a lightweight defense mechanism to prevent membership inference attack from local models and global models in IoT-based FL, while maintaining high model utility. MemDefense adds crafted pruning perturbations to local models at each round of FL by deploying two key components, i.e., parameter filter and noise generator. Specifically, the parameter filter selects the apposite model parameters which have little impact on the model test accuracy and contribute more to member inference attacks. Then, the noise generator is used to find the pruning noise that can reduce the attack accuracy while keeping high model accuracy, protecting each participant's membership privacy. We comprehensively evaluate MemDefense with different deep learning models and multiple benchmark datasets. The experimental results show that lowcost MemDefense drastically reduces the attack accuracy within limited drop of classification accuracy, meeting the requirements for model utility, membership privacy and time efficiency. IEEE

关键词： Internet of things

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When Software Security Meets Large Language Models:A Survey

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 317-334页

作者： Xiaogang Zhu Wei Zhou Qing-Long Han Wanlun Ma Sheng Wen Yang Xiang IEEE the School of Computer and Mathematical Sciences the University of Adelaide the School of Science Computing and Engineering Technologies Swinburne University of Technology

Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.

关键词： Large language models (LLMs) software analysis software security software testing

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A Multi-Resolution Feature Fusion Method for Pedestrian Re-identification

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第9期51卷 1393-1403页

作者： Qin, Haitian Xu, Yang Chen, Xupeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Pedestrian re-identification technology enables accurate identification of individuals and is widely used in modern intelligent video surveillance systems to aid law enforcement, including criminal apprehension and locating missing persons. However, variations in lighting, background, resolution, and other imaging conditions captured by different cameras create significant challenges in pedestrian feature extraction, often leading to poor recognition accuracy. To overcome these challenges, this paper presents a Multi-resolution Feature Fusion (MRFF) method for pedestrian re-identification, based on the Pedestrian Re-identification Relational Network (RNFPR). This approach incorporates the Coordinate Attention (CA) module into the DenseNet161 network to enhance feature extraction capabilities. Improving the discriminative and recognition accuracy of features requires learning and fusing pedestrian features from multiple low-resolution images. This process enhances the expressive power of feature maps, ultimately improving pedestrian recognition performance. Additionally, this method introduces a multi-resolution feature fusion module that segments and integrates multi-resolution features from image data. This enables the model to effectively combine feature information from various resolution levels, resulting in a more comprehensive feature representation. Experimental results show that the MRFF method achieves a 1.3% increase in mean Average Precision (mAP) and a 1.0% improvement in Rank-1 accuracy on the Market1501 dataset. For the DukeMTMC-reID dataset, it provides a 0.2% increase in mAP and a 0.7% enhancement in Rank-1 accuracy. Consequently, the MRFF approach results in an overall mAP increase of 0.7% on the DukeMTMC-ReID dataset, significantly improving pedestrian gender re-identification accuracy. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Security systems

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Zero-valent silver nanoparticles functionalized populus tomentosa fiber for efficient capture and immobilization of iodine vapor

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Nuclear science and Techniques 2025年第5期36卷 43-56页

作者： Yu Fang Hui Zhu Pei Chen Fang Liu Yong Yi Jian Zhou Tao Duan Jie-Hong Lei Research Center for New Energy Materials China West Normal UniversityNanchong 637002China School of Physics and Astronomy China West Normal UniversityNanchong 637002China College of Materials and Chemistry&Chemical Engineering Chengdu University of TechnologyChengdu 610059China School of Materials and Chemistry Southwest University of Science and TechnologyMianyang 621010China State Key Laboratory of Environment-friendly Energy Materials School of National Defense Science&TechnologySouthwest University of Science and TechnologyMianyang 621010China Tianfu Institute of Research and Innovation Southwest University of Science and TechnologyChengdu 610299China

With the rapid development of nuclear energy,the removal of radioactive iodine generated during spent fuel reprocessing has become increasingly *** on the unique straw-like structure of populus tomentosa fiber(PTF)and the highly active iodine vapor capture ability of zero-valent silver nanoparticles(PTF@Ag^(0)NP),an Ag^(0)NP composite functional material with highly efficient iodine vapor capture capability was synthesized from biowaste PTF through ultrasonic and hightemperature hydrothermal methods in this *** iodine capture experiment demonstrated that PTF@Ag^(0)NP exhibits rapid iodine capture efficiency,reaching dynamic equilibrium within 4 h and a maximum capture capacity of 1008.1 mg/*** functional theory calculations show that PTF@Ag^(0)NP exhibits extremely high chemical reactivity toward iodine,with a reaction binding energy of-2.88 e ***,the molecular dynamics of PTF@Ag^(0)NP indicate that there is no atomic displacement at 77?C,indicating the excellent temperature stability of the material at the operating *** capture mechanism suggests that iodine vapor primarily reacts with Ag^(0)NP to form Ag I,and that the hydroxyl groups in PTF can also effectively capture iodine vapor by adsorption *** conclusion,PTF@Ag^(0)NP is expected to be an effective candidate adsorbent material for removing radioactive iodine vapor from exhaust gases during spent fuel reprocessing.

关键词： Ag^(0)NP Populus tomentosa fiber Iodine vapor Capture and immobilization DFT calculations

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