检索结果-内蒙古大学图书馆

International Conference on Computer Research and Development (ICCRD)

作者： Zhuang Miao Yang Li Jiabao Wang Jixiao Wang Rui Zhang Command & Control Engineering College Army Engineering University of PLA Nanjing China

ISBN: (数字)9781665422604

ISBN: (纸本)9781665446518

Recent years have witnessed wide application of infrared and visible image fusion. However, most existing deep fusion methods focused primarily on improving the accuracy without taking much consideration of efficiency. In this paper, our goal is to build a better, faster and stronger image fusion method, which can reduce the computation complexity significantly while keep the fusion quality unchanged. To this end, we systematically analyzed the image fusion accuracy for different depth of image features and designed a lightweight backbone network with spatial frequency for infrared and visible image fusion. Unlikely previous methods based on traditional convolutional neural networks, our method can greatly preserve the detail information during image fusion. We analyze the spatial frequency strategy of our prototype and show that it can maintain more edges and textures information during fusion. Furthermore, our method has fewer parameters and lower computation in comparison of state-of-the-art fusion methods. Experiments conducted on benchmarks demonstrate that our method can achieve compelling fusion results over 97.0% decline of parameter size, running 5 times faster than state-of-the-art fusion methods.

关键词： Degradation Deep learning Time-frequency analysis Image edge detection Prototypes Image fusion Research and development

来源：评论

学校读者我要写书评

暂无评论

Inverse Reinforcement Learning Integrated Reinforcement Learning for Single Intersection Traffic Signal control

Inverse Reinforcement Learning Integrated Reinforcement Lear...

引用

International Conference on Industrial Artificial Intelligence (IAI)

作者： Shiyi Gu Tingting Zhang Ya Zhang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China College of Command and Control Engineering Army Engineering University Nanjing China

Traffic Signal control (TSC) optimization is vital for efficient traffic flow in urban intersections. Recent research has explored the application of Reinforcement Learning (RL) agents to tackle TSC challenges. However, the selection of an appropriate reward function in RL agent training still relies on subjective judgment and experience. To address this issue, an Inverse Reinforcement Learning (IRL) integrated RL algorithm is proposed. In the IRL learning section, expert trajectory data are collected and analyzed by Relative Entropy IRL (REIRL). The latent reward of expert policy is reconstructed and utilized in RL agent training process. In the RL control section, a Double Dueling Deep Q Network is applied under a cycle control framework. As verified in simulations, the introduction of expert experience improves the performance of the RL agent to the expert level and concurrently enables robustness to expert policy noises.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Network Protocol Recognition Based on Convolutional Neural Network

引用

China Communications 2020年第4期17卷 125-139页

作者： Wenbo Feng Zheng Hong Lifa Wu Menglin Fu Yihao Li Peihong Lin Institute of Command and Control Engineering Army Engineering University of PLANanjing210007China School of Computer Science and Technology Nanjing University of Posts and TelecommunicationsNanjing210023China

How to correctly acquire the appropriate features is a primary problem in network protocol recognition *** to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy,a network protocol recognition method based on Convolutional Neural Network(CNN)is *** method utilizes deep learning technique,and it processes network flows ***,normalization is performed on the intercepted network flows and they are mapped into two-dimensional matrix which will be used as the input of ***,an improved classification model named Ptr CNN is built,which can automatically extract the appropriate features of network ***,the classification model is trained to recognize the network *** proposed approach is compared with several machine learning *** results show that the tailored CNN can not only improve protocol recognition accuracy but also ensure the fast convergence of classification model and reduce the classification time.

关键词： convolutional neural network protocol recognition network flow classification model

来源：评论

学校读者我要写书评

暂无评论

KGRGRL: A User’s Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Zhang, Lei Pan, Yu Liu, Yi Zheng, Qibin Pan, Zhisong Command and Control Engineering College Army Engineering University of PLA Nanjing210007 China 31436 troop of PLA Shenyang110005 China Academy of Military Science Haidian Beijing100091 China

In general, multiple domain cyberspace security assessments can be implemented by reasoning user’s permissions. However, while existing methods include some information from the physical and social domains, they do not provide a comprehensive representation of cyberspace. Existing reasoning methods are also based on expert-given rules, resulting in inefficiency and a low degree of intelligence. To address this challenge, we create a Knowledge Graph (KG) of multiple domain cyberspace in order to provide a standard semantic description of the multiple domain cyberspace. Following that, we proposed a user’s permissions reasoning method based on reinforcement learning. All permissions in cyberspace are represented as nodes, and an agent is trained to find all permissions that user can have according to user’s initial permissions and cyberspace KG. We set 10 reward setting rules based on the features of cyberspace KG in the reinforcement learning of reward information setting, so that the agent can better locate user’s all permissions and avoid blindly finding user’s permissions. The results of the experiments showed that the proposed method can successfully reason about user’s permissions and increase the intelligence level of the user’s permissions reasoning method. At the same time, the F1 value of the proposed method is 6% greater than that of the Translating Embedding (TransE) method. Copyright © 2022, The Authors. All rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning

arXiv

引用

arXiv 2022年

作者： Chen, Jinyin Hu, Shulong Zheng, Haibin Xing, Changyou Zhang, Guomin Institute of Cyberspace Security Zhejiang University of Technology Hangzhou China College of Information Engineering Zhejiang University of Technology Hangzhou China College of Command and Control Engineering Army Engineering University of PLA Nanjing China

Penetration testing (PT) is an efficient network testing and vulnerability mining tool by simulating a hacker’s attack for valuable information applied in some areas, e.g., the host’s operating and database systems. Compared with manual PT, intelligent PT has become a dominating mainstream due to less time-consuming and lower labor costs. Among the already proposed intelligent PT methods, reinforcement learning (RL) based PT has achieved state-of-the-art (SOTA) performance. Unfortunately, RL-based PT is still challenged in real exploitation scenarios because the agent’s action space is usually high-dimensional discrete, thus leading to algorithm convergence difficulty. Besides, most PT methods still rely on the decisions of security experts. Addressing the challenges, for the first time, we introduce expert knowledge to guide the agent to make better decisions in RL-based PT, and propose a Generative Adversarial Imitation Learning based generic intelligent Penetration Testing framework, denoted as GAIL-PT, to solve the problems of higher labor costs due to the involvement of security experts and high-dimensional discrete action space. Specifically, first, we manually collect the state-action pairs to construct an expert knowledge base when the pre-trained RL / DRL model executes successful penetration testings. Second, we input the expert knowledge and the state-action pairs generated online by the different RL / DRL models into the discriminator of GAIL for training. At last, we apply the output reward of the discriminator to guide the agent to perform the action with a higher penetration success rate to improve PT’s performance. Extensive experiments conducted on the real target host and simulated network scenarios show that GAIL-PT achieves SOTA penetration performance against DeepExploit in exploiting actual target Metasploitable2 and Q-learning in optimizing penetration path, not only in small-scale with or without honey-pot network environments, but also in th

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Whole Database Retrieval Method of General Relational Database Based on Lucene

Whole Database Retrieval Method of General Relational Databa...

引用

2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

作者： Jin, Dawei Chen, Gang Hao, Wenning Bin, Liu School of Command and Control Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9781728170053

ISBN: (纸本)9781728170046

Aiming at the needs of full-database retrieval of multi-table massive data, a general-purpose, lightweight, non-invasive full-database retrieval method was proposed and implemented. The JDBC technology was used to complete the content extraction of the general database, and the mapping relationships of common database data types such as Mysql, Sqlserver, and Oracle in the Type class were summarized. Based on Lucene, The full-text indexing of the database content is implemented. From the result of experiment, it could be demonstrated that the proposed method has good performance and stability and is well appropriate for construction of index system for common relational database. © 2020 IEEE.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

The presence design of online teaching based on CoI model 2

The presence design of online teaching based on CoI model

引用

2nd International Conference on Computer Science and Educational Informatization, CSEI 2020

作者： Zhang, Wenyu Chen, Weiwei Wang, Rui Zhao, Honghua Yuan, En Wang, Zhen Army Engineering University of PLA Department of Command and Control Engineering Nanjing China

ISBN: (纸本)9781728170077

The lack of social attributes in online teaching makes learners feel lonely during the learning process, which affects the learning effect and learning experience. Based on the CoI model, teaching design of cognitive presence, social presence and teaching presence in online teaching is studied in this research. And the empirical research is carried out in the course of Principle of Computer Composition. The research results show that the presence design of online teaching can improve learners' self-management, promote the construction of deep-level cognition, and improve learners' learning effect and learning autonomy. © 2020 IEEE.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Teaching Reform and Research of Data Structure Course Based on BOPPPS Model and Rain Classroom

Teaching Reform and Research of Data Structure Course Based ...

引用

2021国际计算机前沿大会

作者： Yongfen Wu Zhigang Li Yong Li Yin Liu School of Command and Control Engineering Army Engineering University of PLA

Data structure is the core course for computer science majors. How to improve their ‘computational thinking' ability is crucial and challenging in this course. To optimize the teaching effect, a classroom teaching model is *** model combines Yu Classroom online teaching tools, Chinese university MOOC teaching resources and BOPPPS model. The empirical experiment was implemented and the results show that the comprehensive application of this teaching model helps to cultivate students' high-level cognitive ability of ‘analysis,evaluation and creation'. It is proved the validity of this teaching model improve students'.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Practice and Thinking of “Data Structure and Programming” Based on Blended Teaching

Practice and Thinking of “Data Structure and Programming” ...

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Tang Yanqin Chen Weiwei Wu Yongfen Yuan En Shi Lei Zhang Wenyu Command & Control Engineering College Army Engineering University of PLA Nanjing Jiangsu China

ISBN: (纸本)9781728194202

In order to improve the programming ability of students, teachers are actively seeking various new methods for research and practice. Based on the “Data Structure and Program Design” course, we have carried out the exploration and practice of blended teaching, formulated instructional design based on OBE theory, constructed the teaching mode of online preview before class, offline class + online test in class, and online test after class. Practice shows that these measures improve the students' ability to analyze, express and solve problems.

关键词： Education Information technology Programming profession

来源：评论

学校读者我要写书评

暂无评论

A Convergence Analysis of Nesterov's Accelerated Gradient Method in Training Deep Linear Neural Networks

arXiv

引用

arXiv 2022年

作者： Liu, Xin Tao, Wei Pan, Zhisong Command & Control Engineering College Army Engineering University of PLA Nanjing210007 China Center for Strategic Assessment and Consulting Academy of Military Science Beijing100091 China

Momentum methods, including heavy-ball (HB) and Nesterov's accelerated gradient (NAG), are widely used in training neural networks for their fast convergence. However, there is a lack of theoretical guarantees for their convergence and acceleration since the optimization landscape of the neural network is non-convex. Nowadays, some works make progress towards understanding the convergence of momentum methods in an over-parameterized regime, where the number of the parameters exceeds that of the training instances. Nonetheless, current results mainly focus on the two-layer neural network, which are far from explaining the remarkable success of the momentum methods in training deep neural networks. Motivated by this, we investigate the convergence of NAG with constant learning rate and momentum parameter in training two architectures of deep linear networks: deep fully-connected linear neural networks and deep linear ResNets. Based on the over-parameterization regime, we first analyze the residual dynamics induced by the training trajectory of NAG for a deep fully-connected linear neural network under the random Gaussian initialization. Our results show that NAG can converge to the global minimum at a (1 − O(1/√κ))t rate, where t is the iteration number and κ > 1 is a constant depending on the condition number of the feature matrix. Compared to the (1 − O(1/κ))t rate of GD, NAG achieves an acceleration over GD. To the best of our knowledge, this is the first theoretical guarantee for the convergence of NAG to the global minimum in training deep neural networks. Furthermore, we extend our analysis to deep linear ResNets and derive a similar convergence result. Copyright © 2022, The Authors. All rights reserved.

关键词： Gradient methods

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：