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Advances in Electrical engineering and Computer Applications( AEECA), 2020 IEEE International Conference on

作者： Guanglai Nie Zheng Zhang Yufeng Zhao State key Laboratory of Mathematical Engineering and Advanced Computing MEAC Zhengzhou China

ISBN: (数字)9781728165219

ISBN: (纸本)9781728165226

In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm.

关键词： Web servers Symbiosis Security Scheduling algorithms Cyberspace Redundancy Heuristic algorithms

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New Virus Infection Technology and Its Detection

New Virus Infection Technology and Its Detection

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Zhixu Yangchun Yuntian Zhao Ju Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Computer virus detection technology is an important basic security technology in the information age. The current detection technology has a high success rate for the detection of known viruses and known virus infection technologies, but the development of detection technology often lags behind the development of computer virus infection technology. Under Windows system, there are many kinds of file viruses, which change rapidly, and pose a continuous security threat to users. The research of new file virus infection technology can provide help for the development of virus detection technology. In this paper, a new virus infection technology based on dynamic binary analysis is proposed to execute file virus infection. Using the new virus infection technology, the infected executable file can be detected in the experimental environment. At the same time, this paper discusses the detection method of new virus infection technology. We hope to provide help for the development of virus detection technology from the perspective of virus design.

关键词： Computer viruses Instruments Resists Information age Security Viruses (medical) Software engineering

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Recurrent neural network based binary code vulnerability detection 2

Recurrent neural network based binary code vulnerability det...

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2nd International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2019

作者： Zheng, Jianyun Pang, Jianmin Zhang, Xiaochuan Zhou, Xin Li, MingLiang Wang, Jun State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781450372619

Vulnerability detection, especially vulnerability automatic detection, have long been an area of active research. Considering that RNN can model dependencies automatically, we propose a recurrent neural network (RNN) based method for binary code vulnerability detection. We collected correspond to 4 types of vulnerabilities, including CWE-134, CWE-191, CWE-401 and *** find which RNN is better in vulnerability detection, we evaluated 6 different RNNs, including SRNN, BSRNN, LSTM, BLSTM, GRU and BGRU. The results demonstrated that BSRNN is better than others in vulnerability detection. © 2019 ACM.

关键词： Network security

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Twitter User Geolocation based on Heterogeneous Network and Label Propagation 19

Twitter User Geolocation based on Heterogeneous Network and ...

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Proceedings of the 2019 8th International Conference on computing and Pattern Recognition

作者： Hechan Tian Yaqiong Qiao Yimin Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan China

ISBN: (纸本)9781450376570

Twitter user geolocation is applied into various applications, such as local messages recommendation and event location recognition. Existing methods do not usually utilize the potential positional correlation between heterogeneous information, resulting in low user geolocation accuracy. Therefore, this paper proposes a user geolocation method based on heterogeneous network and label propagation. First, a heterogeneous network is built based on heterogeneous information mined from user tweets. Then, by analyzing the factors that impact the efficiency and accuracy of label propagation, redundant nodes and edges in the initialized heterogeneous network are removed. Finally, the locations of unlabeled users are geolocated through label propagation in the simplified heterogeneous network. Experiments on the publicly available Twitter benchmark dataset show that proposed method accurately locate 57% of users within 161 km of their locations, a significant improvement over several existing methods.

关键词： Data mining label propagation heterogeneous network geolocation

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A Deep Learning Approach for a Source Code Detection Model Using Self-Attention

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Complexity 2020年 2020卷

作者： Meng, Yao Liu, Long State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001 China

With the development of deep learning, many approaches based on neural networks are proposed for code clone. In this paper, we propose a novel source code detection model At-biLSTM based on a bidirectional LSTM network with a self-attention layer. At-biLSTM is composed of a representation model and a discriminative model. The representation model firstly transforms the source code into an abstract syntactic tree and splits it into a sequence of statement trees; then, it encodes each of the statement trees with a deep-first traversal algorithm. Finally, the representation model encodes the sequence of statement vectors via a bidirectional LSTM network, which is a classical deep learning framework, with a self-attention layer and outputs a vector representing the given source code. The discriminative model identifies the code clone depending on the vectors generated by the presentation model. Our proposed model retains both the syntactics and semantics of the source code in the process of encoding, and the self-attention algorithm makes the classifier concentrate on the effect of key statements and improves the classification performance. The contrast experiments on the benchmarks OJClone and BigCloneBench indicate that At-LSTM is effective and outperforms the state-of-art approaches in source code clone detection. © 2020 Yao Meng and Long Liu.

关键词： Semantics

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Implementation of Multi-compartment Neuron Model with Synaptic Plasticity Using FPGA

Implementation of Multi-compartment Neuron Model with Synapt...

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International Conference on Communication Technology (ICCT)

作者： Yiwen Ding Wenlong Fu Lihong Cao Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665432078

Computational neuroscience is interdisciplinary and plays an essential role in facilitating the development of cognitive neuroscience and Artificial General Intelligence (AGI). To explore the computational efficiency of Spiking Neural Network (SNN) on a computational hardware platform, we deploy SNN on the Xilinx Zynq7 Zedboard Field Programmable Gate Array (FPGA). Specifically, we use multi-compartmental Hodgkin-Huxley (HH) neuron model to construct neural circuits via C++ code and adopt a biologically plausible calcium concentration-based plasticity model as the learning rule of SNN. Our experimental results show that the FPGA platform can support parallel computation of the multi-compartment HH model with synaptic plasticity. Compared to other methods, FPGA-based computing has a significant speed advantage, suggesting that FPGA can be a good choice for implementing brain-inspired computation.

关键词： Computational modeling Biological system modeling Neurons Neural circuits Logic gates Brain modeling Hardware

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Twitter user geolocation based on heterogeneous network and label propagation 8

Twitter user geolocation based on heterogeneous network and ...

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8th International Conference on computing and Pattern Recognition, ICCPR 2019

作者： Tian, Hechan Qiao, Yaqiong Liu, Yimin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan*** China

ISBN: (纸本)9781450376570

关键词： Heterogeneous networks

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Research on 3D Human Body Reconstruction Algorithm of Monocular Image

Research on 3D Human Body Reconstruction Algorithm of Monocu...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Zijian Liang Huan Liang Wenlong Fu Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781665426312

The three-dimensional human body feature parameters extracted by the neural network method are coupled in the high-dimensional feature space. The fully connected network processes the three-dimensional human pose and shape feature information synchronously, which cannot handle multifactor features well, which makes the model perform poorly. To solve this problem, on the basis of the benchmark network, a decoupling model is added, which processes the three-dimensional human pose and shape information separately, and finally integrates the features through the bi-linear transformation method. Through experimental verification, the algorithm effectively reduces the reconstruction error, and the improved algorithm performs better.

关键词： Couplings Solid modeling Three-dimensional displays Shape Biological system modeling Neural networks Reconstruction algorithms

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Truth discovery on multi-dimensional properties of data sources 19

Truth discovery on multi-dimensional properties of data sour...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Zheng, Yan Yin, Meijuan Luo, Junyong He, Gongzhen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China

ISBN: (纸本)9781450371582

In the era of information explosion, data fusion has captured increasing attention from researchers as it plays an important part in data application. However, resolving the inconsistency of information generated by various data sources, i.e., truth discovery, has posed great challenges to data fusion. Although existing truth discovery methods mainly focus on source quality, they conduct the truth derivation iteratively only based on the accuracy of sources rather than the recall, which leads to the bad precision. Considering the assumption, sources are independent, cannot satisfy the reality of diverse correlations between them any more. A Gaussian Truth Finder with Correlations (GTFC) algorithm has been proposed in this paper. GTFC iteratively derives the truth, accuracy and recall of sources. The empirical results demonstrate that GTFC can significantly outperform the state-of-the-art algorithms. © 2019 Association for computing Machinery.

关键词： Data fusion

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

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

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Chinese Control Conference (CCC)

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

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.

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