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Boosting Adversarial Training with Learnable Distribution

Computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

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OCD Diagnosis with Multiple Spatial Similarity-Aware Learning and Diffusion Structure-Aware Graph Convolutional Network

OCD Diagnosis with Multiple Spatial Similarity-Aware Learnin...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liang, Yu Zhao, Chen Wang, Tianfu Peng, Ziwen Yang, Peng Lei, Baiying Shenzhen University School of Computer Science and Software Shenzhen China Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen China South China Normal University Center for the Study of Applied Psychology Guangzhou China

ISBN: (纸本)9798350386226

Obsessive-Compulsive Disorder (OCD) is a hereditary mental illness, and unaffected first-degree relative (UFDR) is also at high risk. This study constructed a framework based on traditional machine learning and deep learning methods to identify OCD and UFDR. Specifically, we propose Multiple Spatial Similarity-Aware Learning (MSSL) models to construct Brain Functional Connectivity Networks (BFCNs) and use Diffusion Structure-Aware Graph Convolutional Network (DSAGCN) for feature learning. Two regularization terms are added to the MSSL model, one is to limit the similarity of functional connectivity of adjacent brain regions, and the other is to constrain the similarity of signals in adjacent brain regions. In this strategy, redundant information can be removed while constructing the interrelationships of various brain regions, which can reflect the interrelationships between brain *** graph diffusion is introduced to the DSAGCN model, which can not only solve the edge noise problem caused by the graph structure constructed by the custom adjacency matrix but also enrich the node features by using the position information of the nodes on the reconstructed graph *** framework has been validated on our dataset collected from local hospitals, and the experimental results show that our proposed method outperforms the state-of-the-art. © 2024 IEEE.

关键词： Deep learning

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A Comprehensive Review of Active EV Battery Cell Voltage Balancing Systems: Current Issues and Prospective Solutions 1

A Comprehensive Review of Active EV Battery Cell Voltage Bal...

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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Chetri, Chandan Huynh, Alvin Williamson, Sheldon S. Laboratory Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineering OshawaON L1G 0C5 Canada

ISBN: (纸本)9798350398069

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered. © 2022 IEEE.

关键词： Topology

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

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StakePage: Analysis of Stakeholders of an Information System Using Page Rank Algorithm 1

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Hassan, Tanveer Mohammad, Chaudhary Wali Sadiq, Mohd. Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

ISBN: (数字)9789811988257

ISBN: (纸本)9789811988240

The requirements elicitation process is the sub-process of requirements engineering in which stakeholders are involved to identify the requirements of an information system. Various methods have been developed to identify and analyze the stakeholders like goal-oriented requirements elicitation process, traditional methods, etc. Based on our review, we found that little attention is given to the analysis of the stakeholders using different measures of social networks before the starting of the requirements elicitation process. Therefore, to address this issue, this paper presents a StackPage method for the analysis of the stakeholders using page rank algorithm of social network measures. The applicability of the StackPage method is discussed with the help of the stakeholders of an institute examination system. Finally, the results are compared with two state-of-the-art methods, i.e., StakeRare and StakeSoNet methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Requirements engineering

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SMRI: A New Method for siRNA Design for COVID-19 Therapy

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Journal of Computer Science & Technology 2022年第4期37卷 991-1002页

作者： Meng-Xin Chen Xiao-Dong Zhu Hao Zhang Zhen Liu Yuan-Ning Liu College of Software Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki 851-0193Japan

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

关键词： COVID-19 SARS-CoV-2 RibonucleicAcid interference

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A Decision-driven Methodology for Designing Uncertainty-aware AI Self-Assessment

arXiv

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arXiv 2024年

作者： Canal, Gregory Leung, Vladimir Sage, Philip Heim, Eric Wang, I-Jeng The Johns Hopkins University Applied Physics Laboratory United States Software Engineering Institute Carnegie Mellon University United States

Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact scenarios of national interest. Yet, despite AI's impressive predictive capabilities in controlled settings, it still suffers from a range of practical setbacks preventing its widespread use in various critical scenarios. In particular, it is generally unclear if a given AI system's predictions can be trusted by decision-makers in downstream applications. To address the need for more transparent, robust, and trustworthy AI systems, a suite of tools has been developed to quantify the uncertainty of AI predictions and, more generally, enable AI to "self-assess" the reliability of its predictions. In this manuscript, we categorize methods for AI self-assessment along several key dimensions and provide guidelines for selecting and designing the appropriate method for a practitioner's needs. In particular, we focus on uncertainty estimation techniques that consider the impact of self-assessment on the choices made by downstream decision-makers and on the resulting costs and benefits of decision outcomes. To demonstrate the utility of our methodology for self-assessment design, we illustrate its use for two realistic national-interest scenarios. This manuscript is a practical guide for machine learning engineers and AI system users to select the ideal self-assessment techniques for each problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Multi-agent policy transfer via task relationship modeling

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Science China(Information Sciences) 2024年第8期67卷 102-114页

作者： Rongjun QIN Feng CHEN Tonghan WANG Lei YUAN Xiaoran WU Yipeng KANG Zongzhang ZHANG Chongjie ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies School of Engineering and Applied Sciences Harvard University Department of Computer Science and Technology Tsinghua University Institute for Interdisciplinary Information Sciences Tsinghua University

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks. We posit that the relationship among tasks provides key information for policy *** utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams, proposing to learn an effect-based task representation as a common latent space among tasks, and using it to build an alternatively fixed training scheme. Herein, we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks. Thus, the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source ***, the learned transferred policies help solve tasks that are difficult to learn from scratch.

关键词： multi-agent reinforcement learning cooperative transfer learning task relationship modeling multi-agent policy reuse multi-agent multi-task learning

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Sodium chloride induced nitrogen salt with cyclo-N5 anions at high pressure

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Physical Review Research 2024年第3期6卷 033213页

作者： Bi Zhang Meiling Xu Yu Xin Shuyi Lin Jian Hao Yanchao Wang Yinwei Li Laboratory of Quantum Functional Materials Design and Application School of Physics and Electronic Engineering Key Laboratory of Material Simulation Methods and Software of Ministry of Education and State Key Laboratory of Superhard Materials College of Physics Department of Applied Physics The

The energy landscape of sodium chloride-nitrogen mixtures has been comprehensively explored to examine the ability of the formation of unknown compounds under pressures of up to 100 GPa, using swarm-intelligence structure prediction methodology and first-principles calculations. We identified a thermodynamically stable NaN5ClN5 compound containing two cyclo-N5 species under pressures exceeding 53 GPa, representing milder conditions in comparison to those requisite for pure solid nitrogen. In NaN5ClN5, the high electron affinity of the cyclo-N5 motif allows it to oxidize the chlorine atoms, resulting in the formation of two cyclo-N5 anions. Additionally, the weak covalent interactions between Cl and nearby N atoms plays a key role in stabilization of structure. It has been demonstrated that simple NaN5 salt was a suitable precursor for the synthesis of NaN5ClN5 at high pressure. Ab initio molecular dynamics simulations demonstrated the recoverability of NaN5ClN5 as a metastable phase at ambient pressure-temperature conditions. Additionally, NaN5ClN5 exhibits a higher energy density of 3.86 kJ/g and a lower mass density of 1.67 g/cm3 in comparison to metal pentazolate salts, highlighting its potential as a high energy-density material.

关键词： First-principles calculations Energy materials

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基于SO(3)群几何精确Kirchhoff梁理论的细长梁动力学

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Acta Mechanica Sinica 2024年第4期40卷 212-225页

作者：安志朋王斌侯云森刘铖 MOE Key Laboratory of Dynamics and Control of Flight Vehicle School of Aerospace EngineeringBeijing Institute of TechnologyBeijing100081China CAEP Software Center for High Performance Numerical Simulation Beijing100088China Institute of Applied Physics and Computational Mathematics Beijing100088China

本研究旨在开发一种高效的细长梁单元,精确描述细长柔性结构的大变形和大旋转,在现有细长梁单元研究的基础上,提出一种基于SO(3)群活动标架的二阶几何精确Kirchhoff梁单元(GEKBF),用于细长梁的动力学研究.首先,在此标架中推导出运动学... 详细信息

本研究旨在开发一种高效的细长梁单元,精确描述细长柔性结构的大变形和大旋转,在现有细长梁单元研究的基础上,提出一种基于SO(3)群活动标架的二阶几何精确Kirchhoff梁单元(GEKBF),用于细长梁的动力学研究.首先,在此标架中推导出运动学描述和几何、本构和动态平衡方程及其线性化.然后,引入相对扭转角的二阶插值函数,以提高单元的收敛性,通过选择合适的标架来开发旋转矢量参数化.它可以显著减少由刚体旋转引起的几何非线性,并提高描述当前标架下单元旋转公式的计算效率.此外,考虑了参考标架的影响,并使用空间平行移动而非时间平行移动,简化了弹性力和雅可比矩阵的推导.使用李群广义α-方法求解李群上的半离散运动方程.最后,通过数值实例验证了GEKBF的准确性,包括静力学和动力学.

关键词：平行移动标架弹性力旋转矢量细长梁柔性结构半离散动态平衡

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