检索结果-内蒙古大学图书馆

Petri Net-Based Attack Modeling for Industrial Control System networks 3rd

学校读者我要写书评

暂无评论

Petri Net-Based Attack Modeling for Industrial Control Syste...

3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Wang, Yichuan Lv, Zhaoqi Zhang, Yaling Xiao, Yeqiu Hei, Xinhong School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819903009

Industrial control systems have been deeply involved in industrial facilities and infrastructure. With the integrated development of industry and information technology, industrial control systems are tied to the Internet and may be threatened by cyber attacks. Modeling analysis and security evaluation are significant to guarantee industrial control system security. For in-depth research of security in the industrial control network, we first provide a cyber attack strategy for programmable logic controllers with respect to critical nodes in industrial control systems. Then, we explore and validate the viability of attacks based on the proposed strategy through simulations and visualization. Finally, Petri net modeling is utilized to depict both cyber attacks and defensive techniques. The results in this paper demonstrate that the proposed attack strategy gives new ideas for us to investigate industrial control cyber assaults, while the modeling and analytic methodologies in this paper are also useful for system weaknesses analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： network security

Deep Learning-Driven Simultaneous Layout Decomposition and Mask Optimization

学校读者我要写书评

暂无评论

Deep Learning-Driven Simultaneous Layout Decomposition and M...

作者： Zhong, Wei Hu, Shuxiang Ma, Yuzhe Yang, Haoyu Ma, Xiuyuan Yu, Bei DUTRU International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116024 China

Combining multiple patterning lithography (MPL) and optical proximity correction (OPC) pushes the limit of 193-nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization (MO) process to select the best candidate for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and MO (LDMO) flow. In this article, we propose a convolutional neural network (CNN)-based prediction and integrate it into our new high-performance LDMO framework. The optimization process can be considerably improved as the decomposition quality has been inferred in the early phase. To facilitate the network training and ensure better estimation accuracy, we develop sampling strategies for both layout and decomposition. Moreover, we enhance the layout sampling approach by adopting autoencoder to distance evaluation that promises superior sampling results. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms. © 1982-2012 IEEE.

关键词： Manufacture

Continuous contrastive learning for long-tailed semi-supervised recognition 24

学校读者我要写书评

暂无评论

Continuous contrastive learning for long-tailed semi-supervi...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zi-Hao Zhou Siyuan Fang Zi-Jing Zhou Tong Wei Yuanyu Wan Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China Xiaomi Inc. China School of Software Technology Zhejiang University Ningbo China

ISBN: (纸本)9798331314385

Long-tailed semi-supervised learning poses a significant challenge in training models with limited labeled data exhibiting a long-tailed label distribution. Current state-of-the-art LTSSL approaches heavily rely on high-quality pseudo-labels for large-scale unlabeled data. However, these methods often neglect the impact of representations learned by the neural network and struggle with real-world unlabeled data, which typically follows a different distribution than labeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. Our framework derives the class-balanced contrastive loss through Gaussian kernel density estimation. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels. By progressively estimating the underlying label distribution and optimizing its alignment with model predictions, we tackle the diverse distribution of unlabeled data in real-world scenarios. Extensive experiments across multiple datasets with varying unlabeled data distributions demonstrate that CCL consistently outperforms prior state-of-the-art methods, achieving over 4% improvement on the ImageNet-127 dataset. Our source code is available at https://***/zhouzihao11/CCL.

关键词：

Competitive Swarm Optimizer Based on Individual Learning Mechanism

学校读者我要写书评

暂无评论

Competitive Swarm Optimizer Based on Individual Learning Mec...

High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： JingQi Tang Wei Li Lei Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

In view of the disadvantages of the traditional competitive swarm optimizer (CSO), such as falling into local minimization or poor convergence accuracy, this paper proposed an enhanced CSO algorithm called competitive swarm optimizer based on individual learning mechanism (ILCSO). Firstly, the individual selection rate and the learning rate are designed to dynamically select the winner and loser. The losers are updated by the precise competitive learning strategy to improve the algorithm exploitation ability. Secondly, the mutation strategy based on performance improvement is introduced, which improves the search ability of algorithm. The proposed algorithm effectively balances the local search and global exploration with the individual learning mechanism, and improves the probability of finding the global optimal solution. Finally, ILCSO is compared with six classical meta-heuristic algorithms on CEC2014 benchmark functions. The Wilcoxon rank-sum test is used to demonstrate that the ILCSO is effective. Experimental results and statistical analysis show that ILCSO has higher convergence speed and convergence accuracy.

关键词：

Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Congzhi Zhang, Linhai Wu, Jialong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on the model training stage, including approaches based on data augmentation and reweighting, yet they struggle with the complex biases inherent in LLMs. To address such limitations, the causal relationship behind the prompting methods is uncovered using a structural causal model, and a novel causal prompting method based on front-door adjustment is proposed to effectively mitigate LLMs biases. In specific, causal intervention is achieved by designing the prompts without accessing the parameters and logits of LLMs. The chain-of-thought generated by LLM is employed as the mediator variable and the causal effect between input prompts and output answers is calculated through front-door adjustment to mitigate model biases. Moreover, to accurately represent the chain-of-thoughts and estimate the causal effects, contrastive learning is used to fine-tune the encoder of chain-of-thought by aligning its space with that of the LLM. Experimental results show that the proposed causal prompting approach achieves excellent performance across seven natural language processing datasets on both open-source and closed-source LLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Large, Small or Both: A Novel Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are reluctant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the over-reliance on a specific framework is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, data augmentation based on large language models faces two disadvantages: 1) the potential issues or toxicity in the augmented data;2) the expensive costs. Therefore, in this paper, we propose a novel data augmentation framework based on both large and small language models for debiasing opinion summarization. In specific, a small size of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on confusion degree and sentiment classification. Experiments have proved that our framework can effectively alleviate emotional bias same as using only large models, but more economically. Copyright © 2024, The Authors. All rights reserved.

关键词： Data assimilation

All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Xu, Xinmeng Tu, Weiping Han, Chang Yang, Yuhong National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Luojia Laboratory China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

Monaural speech enhancement (SE) is an ill-posed problem due to the irreversible degradation process. Recent methods to achieve SE tasks rely solely on positive information, e.g., ground-truth speech and speech-relevant features. Different from the above, we observe that the negative information, such as original speech mixture and speech-irrelevant features, are valuable to guide the SE model training procedure. In this study, we propose a SE model that integrates both speech positive and negative information for improving SE performance by adopting contrastive learning, in which two innovations have consisted. (1) We design a collaboration module (CM), which contains two parts, contrastive attention for separating relevant and irrelevant features via contrastive learning and interactive attention for establishing the correlation between both speech features in a learnable and self-adaptive manner. (2) We propose a contrastive regularization (CR) built upon contrastive learning to ensure that the estimated speech is pulled closer to the clean speech and pushed far away from the noisy speech in the representation space by integrating self-supervised models. We term the proposed SE network with CM and CR as CMCR-Net. Experimental results demonstrate that our CMCR-Net achieves comparable and superior performance to recent approaches. © 2023, CC BY.

关键词： Speech enhancement

Styles Energy Consumption Analysis of Lane-Changing Maneuvers in Autonomous Vehicles: The Role of Driving Styles

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Guo, Yu Nie, Guigen Zou, Xiaowei Zhu, Dongliang Gao, Wenliang Liao, Mi GNSS Research Center Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

HIGHLIGHTS·Optimized lane-change trajectories considering safety, comfort, and time efficiency.·Developed a model for identifying different lane changing styles to meet personalized driving preferences.·Comprehensive analysis uncovers significant energy consumption patterns in electric autonomous vehicles during lane-change maneuvers.·Conducted rigorous examination of energy consumption during lane changes, identifying consumption trends in aggressive, conservative, and normal driving styles.·Correlation analysis of key factors affecting energy consumption paves the way for customizing driving styles and powertrain design to reduce energy usage. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

Multi-Instance Partial-Label Learning with Margin Adjustment

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Tang, Wei Yang, Yin-Fang Wang, Zhaofei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning