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An Automated Penetration Semantic Knowledge Mining Algorithm Based on Bayesian Inference

Computers, Materials & Continua 2021年第3期66卷 2573-2585页

作者： Yichao Zang Tairan Hu Tianyang Zhou Wanjiang Deng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000China National Engineering Technology Research Center of the Digital Switching System Zhengzhou450000China NUS Business School National University of SingaporeSingapore119077Singapore

Mining penetration testing semantic knowledge hidden in vast amounts of raw penetration testing data is of vital importance for automated penetration *** rule mining,a data mining technique,has been studied and explored for a long ***,few studies have focused on knowledge discovery in the penetration testing *** experimental result reveals that the long-tail distribution of penetration testing data nullifies the effectiveness of associative rule mining algorithms that are based on frequent *** address this problem,a Bayesian inference based penetration semantic knowledge mining algorithm is ***,a directed bipartite graph model,a kind of Bayesian network,is constructed to formalize penetration testing ***,we adopt the maximum likelihood estimate method to optimize the model parameters and decompose a large Bayesian network into smaller networks based on conditional independence of variables for improved solution ***,irrelevant variable elimination is adopted to extract penetration semantic knowledge from the conditional probability distribution of the *** experimental results show that the proposed method can discover penetration semantic knowledge from raw penetration testing data effectively and efficiently.

关键词： Penetration semantic knowledge automated penetration testing Bayesian inference cyber security

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Function Projective Synchronization of Chaotic PMSMs with Certain and Uncertain System Parameters

Function Projective Synchronization of Chaotic PMSMs with Ce...

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2021 China Automation Congress, CAC 2021

作者： Wang, Mufeng Liu, Ke Geng, Yangyang Che, Xin Wang, Jingpei College of Control Science & Engineering Zhejiang University Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665426473

In recent years, driven by huge engineering demands, industrial Cyber-Physical System (iCPS) has developed rapidly. As an important energy conversion device in iCPS, stable operation of permanent magnet synchronous motor (PMSM) is very important to ensure the availability of the whole iCPS. In this paper, based on Lyapunov stability theory, the function projective synchronization (FPS) of chaotic PMSM systems with certain and uncertain system parameters are investigated, respectively. The proposed control method is proved by using explicit numerical theory and then applied in numerical simulations. From the results of simulations, effectiveness of the control method is obviously. Our study can solve the synchronization and motors coordination problem in industry manufacture. © 2021 IEEE

关键词： Cyber Physical System

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Code Clone Detection Based on Code Semantic Enhancement Model

Code Clone Detection Based on Code Semantic Enhancement Mode...

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Intelligent Communication and Computer engineering (ICICCE), International Conference on

作者： Zhang Chunyan Shen Ziang Liang Chen State Key of Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Department of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

Code clones are code fragments that are similar in composition or semantics of code statements, which can significantly improve software development efficiency and reduce costs by improving the quality of reusable code. However, existing code clone detection methods ignore the source code structure information and sequence information seriously. In this paper, we launched our research on the harder-to-break Type IV code clone detection, and propose a new method, namely Aug_Clone, to complete code clone detection task. Firstly, we utilize our published code semantic enhancement model to obtain the code semantic vector. Secondly, we perform code clone detection by calculating the similarity between these semantic vectors. At last, we evaluate our method on a large public data set, and experimental results show improvement over the state-of-the-art benchmarks, which demonstrates the effectiveness and efficiency of our method.

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A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction 42

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

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42nd Chinese Control Conference, CCC 2023

作者： Yan, Weidong Xu, Zhaoliang Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Advanced Institute of Information Technology Peking University Hangzhou310000 China

ISBN: (纸本)9789887581543

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Brain computer interface

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Preparation and Simulation of Quantum states of Matrices 6

Preparation and Simulation of Quantum States of Matrices

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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Liu, Zhengyu Liu, Xiaonan Xie, Haoshan Zhao, Chenyan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450000 China National Supercomputing Center in Zhengzhou Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450000 China

ISBN: (纸本)9781450397148

A basic problem of quantum computing is how to effectively express classical data in quantum systems. This problem is called state preparation problem, and the process of preparing quantum states is called coding. In this paper, the classical data of matrix is transformed into quantum states in the form of amplitude coding and simulated. This paper first introduces the detailed process of matrix transformation into the linear combination of quantum ground state, then designs a quantum circuit model to realize amplitude coding according to the idea of binary tree model and quantum random walk algorithm, and finally simulates the three matrices on IBM quantum cloud platform, and analyzes and introduces many situations. The experimental results verify the feasibility of converting the matrix into quantum states by amplitude coding, analyze the causes of errors in the experimental process, and put forward the corresponding solutions. This paper introduces in detail the whole process of matrix conversion to quantum states and the construction of quantum circuits for simulation. Compared with the existing experiments, the quantum circuits constructed in this paper are shorter in length, less in number of quantum gates, and more versatile. © 2022 Association for computing Machinery.

关键词： Matrix algebra

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Path planning of UAV base station based on deep reinforcement learning 12

Path planning of UAV base station based on deep reinforcemen...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Yang, Siming Shan, Zheng Cao, Jiang Gao, Yuan Guo, Yang Wang, Ping Wang, Xiaonan Wang, Jing Zhang, Tingting Guo, Jiayu State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China Academy of Military Science of the PLA Beijing100091 China Network Technology Research Center of the PLA Beijing100090 China

UAV base station platform has become the current research hotspot of assisting ground base station for wireless *** present, the most important issue is how to make path planning to provide the stable communication guarantee for multiple mobile users. In this article, we model the air-to-ground channel to describe the path loss between the UAV platform and the user and build a simulation environment for training based on the OpenAI-GYM architecture. In addition, this paper proposes a reinforcement learning algorithm based on intrinsic rewards, which uses the mean square error of the state prediction results to quantify the novelty of the state. Algorithms enable agents to efficiently carry out strategy iterations. Experiments results showed that our algorithm has a higher score and takes less time. © 2022 The Authors. Published by Elsevier B.V.

关键词： Deep learning Deep reinforcement learning UAV path planning Wireless communication coverage

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Distributed Deep Learning System for Efficient Face Recognition in Surveillance System 3

Distributed Deep Learning System for Efficient Face Recognit...

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3rd International Conference on advanced Information Science and System, AISS 2021

作者： Liu, Jinjin Chen, Zhifeng Li, Xiaonan Wei, Tongxin School of Computer Science Zhongyuan University of Technology China and State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science Zhongyuan University of Technology China School of Cyberspace Security Zhengzhou University China

ISBN: (纸本)9781450385862

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process. © 2021 ACM.

关键词： Distillation

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A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition 42

A Multi-Head Convolution Network with Attention Consistency ...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Wenkang Sun, Mingyi Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Peking University Advanced Institute of Information Technology Hangzhou310000 China

ISBN: (纸本)9789887581543

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Face recognition

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A Review of Field Text Analysis

A Review of Field Text Analysis

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2021 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2021

作者： Li, Zhuolun Kang, Fei Yu, Pu Shu, Hui Zhengzhou University School of Software Zhengzhou China Meac State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665424981

Field text analysis is the process of using natural language processing technology to understand the Text and extract information from unstructured text data. In recent years, due to the increasing maturity of machine learning technology, higher requirements have been put forward for field text analysis. This review first introduces the process of Text preprocessing and the development of text representation. Secondly, it summarizes the four typical techniques of text analysis, including named entity recognition, relation extraction, extractive text summarization, and text classification. Finally, for each typical technology, it introduces the commonly used methods and part of the researcher's exploration of this technology. © 2021 IEEE.

关键词： Text processing

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