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Smart World Congress (SWC), IEEE

作者： Shanshan Liu Xiangzhi Liu Jiazheng Man Bingyang Cao Yunfeng Dong Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The large variation in target scales within steel surface defect images often results in low detection accuracy. To address this issue, we have developed a multi-scale defect detection method named SGNet. Initially, we introduced a Self-Guided Bi-Level Routing Transformer (SG-BiFormer) as the backbone network of our model. SG-BiFormer utilizes a self-guided strategy to model global information and dynamically reallocates tokens based on the importance of image regions, increasing token quantity in significant areas to preserve fine-grained details and reducing it in less critical areas. This strategy significantly enhances the accuracy and efficiency of the model in handling multi-scale targets. Subsequently, we incorporated Grouped Separable Convolutions (GSConv) to manage local information of multi-scale features. GSConv automatically adjusts the size and shape of the convolution kernels according to different input features. This design not only improves global information processing capabilities but also ensures sensitive and precise detail capture. Lastly, we employed MPDIoU as the loss function for our model, which optimizes corner positioning to tackle the challenges of regression tasks, substantially improving the accuracy of the model in handling complex defects. Experimental evidence confirms the effectiveness of this method. Our SGNet achieved a 77.1% mAP on the NEU-DET dataset, demonstrating outstanding performance.

关键词： Visualization Accuracy Shape Detectors Transformers Routing Feature extraction Steel Kernel Defect detection

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RDGFuzz: A directed greybox fuzzing optimization method based on Rich-Branch nodes 22

RDGFuzz: A directed greybox fuzzing optimization method base...

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Proceedings of the 2022 6th International Conference on Electronic Information technology and Computer Engineering

作者： Zejun Wu Li Lu Qiong Jia Zhihao Chen State Key Laboratory of Mathematical Engineering and advanced Computing China National Local Joint Engineering Laboratory of Network Space Security Technology China Beijing Institute of Computer Technology and Application China

ISBN: (纸本)9781450397148

Directed fuzzing technology is one of the key technologies to quickly reach a specific location of software, and to conduct targeted testing or bug recurrence. However, directed fuzzing technology has some problems, such as unreasonable seed energy allocation, low code coverage and incomplete testing. To solve the above problems, this paper proposes an optimization method of directed fuzzing based on Rich-Branch nodes. In this method, the concept of Rich-Branch nodes is defined and the algorithm of extracting Rich-Branch nodes is given. The optimization method collects the coverage information of the target program in the running process, calculates the weights of covered functions and nodes in real time by combining CG and CFG of the target program, and generates a list of Rich-Branch nodes. According to the weights of Rich-Branch nodes, the seed energy allocation algorithm of AFLGo is optimized and improved. Compared with AFLGo, this optimization method improves the average code coverage of each targeted point by 56.79%, and has the same target reaching ability as AFLGo.

关键词： Rich-Branch nodes Directed fuzzing Code coverage rate Vulnerability discovery

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Covariance Matrix Adaptation Evolutionary Algorithm for Multi-task Optimization 15th

Covariance Matrix Adaptation Evolutionary Algorithm for Mult...

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15th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2020

作者： Li, Wei Lei, Zhou Yuan, Junqing Luo, Haonan Jiang, Qiaoyong Li, Jinbo School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China

ISBN: (纸本)9789811613531

Multifactorial evolution is a relatively new evolutionary multitasking paradigm which aims to solve multiple optimization problems simultaneously. In order to make a further contribution to multifactorial optimization, this paper introduced a covariance matrix adaptation evolutionary strategy to explore the generality of the multitasking paradigm. A novel encoding and decoding method is employed so that each component problem is solved by a unique population. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm has a superior performance in comparison with other state-of-the-art multifactorial optimization algorithms. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multitasking

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DBE-SwinUNet: A Novel Dual-Branch SwinUNet for Accurate Cancer Pathology Image Segmentation

DBE-SwinUNet: A Novel Dual-Branch SwinUNet for Accurate Canc...

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International Conference on Computer Supported Cooperative Work in Design

作者： Tao Hu Jingshan Pan Na Li Tangbin Tian Shengyu Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center(National Supercomputer Center in Jinan) Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In the field of medical image analysis, precise cancer pathology image segmentation is crucial for assisting clinicians in identifying lesion areas. This paper proposes an innovative dual-branch architecture, DBE-SwinUNet, to overcome the reliance on expert knowledge, inefficiencies, and the limitations of local feature extraction in traditional deep learning models. We introduced depthwise separable convolution (DSConv) to enhance global feature representation and integrated Multi-Scale Dense Skip Connections (MSDSC) to facilitate information flow between the encoder and decoder, improving model stability and segmentation accuracy. Additionally, we incorporated a convolutional neural network enhancement module (DA-DWConv) with a dual attention mechanism, which enhances local feature extraction while effectively controlling model complexity. Experimental results show that on the gastric cancer pathology image (GCPI) dataset, our method achieved a Dice similarity coefficient (DSC) of 90.15 % and a 95th percentile Hausdorff distance (HD95) of 39.25 mm. On the public Synapse dataset, the method reached 80.42% DSC and 19.28 mm HD95, significantly outperforming existing methods.

关键词： Pathology Image segmentation Attention mechanisms Accuracy Feature extraction Transformers Stability analysis Decoding Convolutional neural networks Cancer

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A Federated Learning network Intrusion Detection System for Multiple Imbalances

A Federated Learning Network Intrusion Detection System for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yifan Zeng Peijia Zheng Jian Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning network Intrusion Detection System (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNΠ)S can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple imbalanced scenarios and even surpasses centralized training in several settings.

关键词： Training Privacy Data privacy Accuracy Costs Federated learning network intrusion detection Signal processing Benchmark testing Speech processing

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Infringement Detection and Traceability Scheme Based on Ciphertext Feature Extraction

Infringement Detection and Traceability Scheme Based on Ciph...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Lianhai Wang Xinlei Wang Tianrui Liu Yingxiaochun Wang Qi Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid growth of healthcare data transactions has fostered win-win developments in healthcare technology innovation and economic benefits. However, the issue of secondary transactions makes data ownership vulnerable to infringement due to the easily replicable nature of the data. As the importance of ownership and privacy increases, more and more users protect the privacy of data through encryption methods, making it impossible for existing methods to make infringement judgments on ciphertext data. To solve this problem, we propose an infringement detection and traceability scheme based on feature extraction of ciphertext data. Firstly, the characteristics of fully homomorphic encryption are utilized to obtain data features in the ciphertext state, and the plaintext state of these features is used as the basis for infringement judgment. Additionally, during the data transaction process, the ownership registration of the sales data is verified through smart contracts to ensure the effective association between the transaction data and the seller’s address. The experimental results show that the scheme can accurately determine infringement while protecting data security and privacy.

关键词： Economics Data privacy Technological innovation Data security Smart contracts Cyberspace Medical services Data science Feature extraction Homomorphic encryption

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An Improved Cloud Particles Optimizer for Function Optimization 1

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2nd International Conference on Neural computing for Advanced Applications, NCAA 2021

作者： Li, Wei Luo, Haonan Yuan, Junqing Lei, Zhou Wang, Lei School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an710048 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

Evolutionary algorithm is a popular and effective method in optimization field. As one of the optimization algorithms, cloud particles optimizer (CPEA) has shown to be effective in solving problems with different characteristics. However, due to the powerful local search ability, premature convergence is a significant disadvantage of CPEA optimizer. To alleviate this problem, an improved cloud particles optimizer named ICPEA is proposed. In ICPEA, the fluid operation is designed to explore the evolution direction, while the solid operation is employed to improve the exploitation efficiency. Moreover, the dynamical selection strategies of control parameters are employed to cope with premature convergence issues. In order to demonstrate the effectiveness of ICPEA, CEC2014 test suites are used for simulating. The experimental results affirm that ICPEA is a competitive optimizer compared to CPEA algorithm and several state-of-the-art optimizers. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Global optimization

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Enhanced Velocity-Driven Particle Swarm Optimization for Evolving Artificial Neural network 2021

Enhanced Velocity-Driven Particle Swarm Optimization for Evo...

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7th International Conference on computing and Artificial Intelligence, ICCAI 2021

作者： Li, Wei Luo, Haonan Lei, Zhou Yuan, Junqing Dong, Wangshun Wang, Lei School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an710048 China

ISBN: (纸本)9781450389501

Neural network has been considered as an effective tool for predicting chaotic time series and approximating functions. However, traditional methods for training neural networks have some shortcomings, such as slow convergence speed, easy to fall into local optimum and so on. To obtain an efficient performance, an enhanced velocity-driven particle swarm optimization, called EVDPSO, is proposed in this paper. The proposed algorithm introduces an adaptive method for the parameter f used in the velocity-driven mechanism, which helps to achieve an effective trade-off between exploration and exploitation. To verify the effectiveness of the proposed algorithm, the algorithm is employed as a new optimize method to train artificial neural networks for nonlinear function approximation problems and chaotic time series prediction. The experimental results demonstrate the effectiveness of the proposed algorithm over other traditional optimization algorithms. © 2021 ACM.

关键词： Neural networks

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Mirror contrastive loss based sliding window transformer for subject-independent motor imagery based EEG signal recognition

arXiv

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arXiv 2024年

作者： Luo, Jing Mao, Qi Shi, Weiwei Shi, Zhenghao Wang, Xiaofan Lu, Xiaofeng Hei, Xinhong Shaanxi Key Laboratory for Network Computing and Security Technology and Human–Machine Integration Intelligent Robot Shaanxi University Engineering Research Center School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an China

While deep learning models have been extensively utilized in motor imagery based EEG signal recognition, they often operate as black boxes. Motivated by neurological findings indicating that the mental imagery of left or right-hand movement induces event-related desynchronization (ERD) in the contralateral sensorimotor area of the brain, we propose a Mirror Contrastive Loss based Sliding Window Transformer (MCL-SWT) to enhance subject-independent motor imagery-based EEG signal recognition. Specifically, our proposed mirror contrastive loss enhances sensitivity to the spatial location of ERD by contrasting the original EEG signals with their mirror counterparts—mirror EEG signals generated by interchanging the channels of the left and right hemispheres of the EEG signals. Moreover, we introduce a temporal sliding window transformer that computes self-attention scores from high temporal resolution features, thereby improving model performance with manageable computational complexity. We evaluate the performance of MCL-SWT on subject-independent motor imagery EEG signal recognition tasks, and our experimental results demonstrate that MCL-SWT achieved accuracies of 66.48% and 75.62%, surpassing the state-of-the-art (SOTA) model by 2.82% and 2.17%, respectively. Furthermore, ablation experiments confirm the effectiveness of the proposed mirror contrastive loss. A code demo of MCL-SWT is available at https://***/roniusLuo/MCL_SWT. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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