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arXiv 2024年

作者： Li, Qian Guo, Shu Chen, Yingjia Ji, Cheng Sheng, Jiawei Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The State Key Laboratory of Software Development Environment Beihang University Beijing100083 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100083 China School of Mathematics Edinburgh University Edinburgh United Kingdom Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China

Few-shot knowledge graph completion (FKGC) aims to query the unseen facts of a relation given its few-shot reference entity pairs. The side effect of noises due to the uncertainty of entities and triples may limit the few-shot learning, but existing FKGC works neglect such uncertainty, which leads them more susceptible to limited reference samples with noises. In this paper, we propose a novel uncertainty-aware few-shot KG completion framework (UFKGC) to model uncertainty for a better understanding of the limited data by learning representations under Gaussian distribution. Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution. Further, to better integrate the neighbors with uncertainty characteristics for entity features, we design an uncertainty-aware relational graph neural network (UR-GNN) to conduct convolution operations between the Gaussian distributions. Then, multiple random samplings are conducted for reference triples within the Gaussian distribution to generate smooth reference representations during the optimization. The final completion score for each query instance is measured by the designed uncertainty optimization to make our approach more robust to the noises in few-shot scenarios. Experimental results show that our approach achieves excellent performance on two benchmark datasets compared to its competitors. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Pre-ictal causal connectivity reveals the epileptic network characteristics for deep brain stimulation

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Neuroscience 2025年 579卷 329-343页

作者： Yifan Wang Tao Yu Yulan Ma Weigang Cui Xueyuan Wang Liankun Ren Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Beijing Institute of Functional Neurosurgery Department of Functional Neurosurgery Xuanwu Hospital Capital Medical University Beijing 100053 China. School of Engineering Medicine Beihang University Beijing 100191 China. Department of Neurology Xuanwu Hospital Capital Medical University Beijing 100053 China. Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Department of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: liyang@***.

Deep brain stimulation of anterior nucleus of the thalamus (ANT-DBS) is an effective clinical treatment for drug-resistant focal epilepsy. However, the complex epileptic network characteristics underlying ANT-stimulation effectiveness remain unknown, owing to currently unclear connectivity between ANT and seizure-related cortex in pre-ictal periods. Here, we developed a novel individualized pre-ictal ANT-cortical tripartite connectivity network (PANT-CTCNet), aiming to reveal epileptic network characteristics using intracranial stereo-electroencephalography (sEEG) recordings in five patients with focal epilepsy for 90 trials. Each trial represented a pre-ictal or post-stimulus sEEG duration, which was used to construct the epileptic network. By employing conditional Granger causality, we constructed individualized ANT-cortical connectivity networks and found a common epileptic network centred on ANT closely connected with seizure-related cortex in pre-ictal periods. After ANT stimulation for clinical validation, strengthened pre-ictal connectivity between ANT and epileptogenic zones led to significant decline in the causal intensity of epileptic networks. The PANT-CTCNet can give a quantitative reference for clinical preoperative evaluation of patient suitability for ANT-DBS treatment. These findings regarding epileptic network characteristics provide theoretical basis in the selection of optimal surgical candidates for personalized ANT-DBS.

关键词： Anterior nucleus of the thalamus Epilepsy Epileptic network characteristics Neuromodulation Stereo-electroencephalography

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MG-DVD: A real-time framework for malware variant detection based on dynamic heterogeneous graph learning

arXiv

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arXiv 2021年

作者： Liu, Chen Li, Bo Zhao, Jun Su, Ming Liu, Xu-Dong School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

Detecting the newly emerging malware variants in real time is crucial for mitigating cyber risks and proactively blocking intrusions. In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time. Particularly, MG-DVD first models the fine-grained execution event streams of malware variants into dynamic heterogeneous graphs and investigates real-world meta-graphs between malware objects, which can effectively characterize more discriminative malicious evolutionary patterns between malware and their variants. Then, MG-DVD presents two dynamic walk-based heterogeneous graph learning methods to learn more comprehensive representations of malware variants, which significantly reduces the cost of the entire graph retraining. As a result, MG-DVD is equipped with the ability to detect malware variants in real time, and it presents better interpretability by introducing meaningful meta-graphs. Comprehensive experiments on large-scale samples prove that our proposed MG-DVD outperforms state-of-the-art methods in detecting malware variants in terms of effectiveness and efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Malware

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

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Post-Quantum Blockchain over Lattice

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Computers, Materials & Continua 2020年第5期63卷 845-859页

作者： Xiao Zhang Faguo Wu Wang Yao Wenhua Wang Zhiming Zheng School of Mathematics and Systems Science Beihang Universityand Key Laboratory of MathematicsInformatics and Behavioral SemanticsMinistry of EducationBeijing100191China Peng Cheng Laboratory Shenzhen518055China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100191China Aviation Industry Development Research Center of China BeijingChina

Blockchain is an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies,and has recently attracted intensive attention from governments,financial institutions,high-tech enterprises,and the capital *** cryptographic security relies on asymmetric cryptography,such as ECC,***,with the surprising development of quantum technology,asymmetric cryptography schemes mentioned above would become ***,lattice-based cryptography scheme was proposed to be secure against attacks in the quantum *** 2018,with the aid of Bonsai Trees technology,Yin et al.[Yin,Wen,Li et al.(2018)]proposed a lattice-based authentication method which can extend a lattice space to multiple lattice spaces accompanied by the corresponding *** their scheme has theoretical significance,it is unpractical in actual situation due to extremely large key size and signature *** this paper,aiming at tackling the critical issue of transaction size,we propose a post quantum blockchain over *** using SampleMat and signature without trapdoor,we can reduce the key size and signature size of our transaction authentication approach by a significant *** of using a whole set of vectors as a basis,we can use only one vector and rotate it enough times to form a *** on the hardness assumption of Short Integer Solution(SIS),we demonstrate that the proposed anti-quantum transaction authentication scheme over lattice provides existential unforgeability against adaptive chosen-message attacks in the random *** compared to the Yin et al.[Yin,Wen,Li et al.(2018)]scheme,our scheme has better performance in terms of energy consumption,signature size and signing key *** the underlying lattice problem is intractable even for quantum computers,our scheme would work well in the quantum age.

关键词： Blockchain post quantum lattice random oracle

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Two-stage Generative Adversarial Recovery Network for MR brain Images Containing Tumors 20

Two-stage Generative Adversarial Recovery Network for MR Bra...

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Proceedings of the 2020 9th International Conference on Bioinformatics and Biomedical Science

作者： MENG KONG HAIFENG ZHAO SHAOJIE ZHANG ZHENYU TANG School of Computer Science and Technology Anhui University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (纸本)9781450388658

brain image registration (BIR) plays an important role in neuroscience. However, for the registration of brain image containing tumors, the existence of tumor could cause great influence to BIR. One possible solution for getting rid of such influence is to recover the tumor brain image to “normal” appearance brain image (no tumor). Most of existing methods for tumor brain image recovery are based on low-rank, which is time consuming and low recovery quality. In this paper, we propose a novel deep-learning based method for tumor brain image recovery. Specifically, a two-stage generative adversarial network comprising a region recovery stage and an image recovery stage is presented. For the input tumor brain image, the region recovery stage first generates a recovered brain region image containing three different regions (i.e., the gray matter, the white matter and the cerebrospinal fluid). The recovered brain region image is used in the image recovery stage as priori information to get the final “normal” appearance brain image. Both stages are trained under the generative adversarial framework. The experimental results demonstrate that the registration accuracy of tumor brain images can be significantly enhanced by our network as compared to the state-of-the-art image recovery methods.

关键词： Tumor brain image recovery Deep learning Two stage recovery GAN brain image registration

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Two-stage Generative Adversarial Recovery Network for MR brain Images Containing Tumors 9

Two-stage Generative Adversarial Recovery Network for MR Bra...

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9th International Conference on Bioinformatics and Biomedical Science, ICBBS 2020

作者： Kong, Meng Zhao, Haifeng Zhang, Shaojie Tang, Zhenyu School of Computer Science and Technology Anhui University HefeiAnhui230601 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781450388658

brain image registration (BIR) plays an important role in neuroscience. However, for the registration of brain image containing tumors, the existence of tumor could cause great influence to BIR. One possible solution for getting rid of such influence is to recover the tumor brain image to "normal"appearance brain image (no tumor). Most of existing methods for tumor brain image recovery are based on low-rank, which is time consuming and low recovery quality. In this paper, wepropose a novel deep-learning based method for tumor brain image recovery. Specifically, a two-stage generative adversarial network comprising a region recovery stage and an image recovery stage is presented. For the input tumor brain image, the region recovery stage first generates a recovered brain region image containing three different regions (i.e., the gray matter, the white matter and the cerebrospinal fluid). The recovered brain region image is used in the image recovery stage as priori information to get the final "normal"appearance brain image. Both stages are trained under the generative adversarial framework. The experimental results demonstrate that the registration accuracy of tumor brain images can be significantly enhanced by our network as compared to the state-of-the-art image recovery methods. © 2020 ACM.

关键词： Tumors

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Dynamic network embedding via incremental skip-gram with negative sampling

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Science China(Information Sciences) 2020年第10期63卷 89-107页

作者： Hao PENG Jianxin LI Hao YAN Qiran GONG Senzhang WANG Lin LIU Lihong WANG Xiang REN Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Southern California

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O（1/n;）, and the second order moment of the objective difference can be stabilized in order of O（1）. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to

关键词： dynamic network embedding bound and convergence analysis multi-label classification link prediction

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DOANet: Point Cloud Registration with Deep Overlap Attention

DOANet: Point Cloud Registration with Deep Overlap Attention

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Chinese Automation Congress (CAC)

作者： Haining Guan Qing Gao Baochang Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (纸本)9781665426480

Pairwise point cloud registration is a fundamental task in 3D computer vision and various approaches have been proposed to handle this problem in recent years. Despite the fast evolution of registration algorithms, little attention has been drawn to align point cloud pairs with a low-overlap ratio (≤30%). In this paper, we propose a new neural network architecture named the DOANet, an end-to-end trainable fully convolutional neural network, to deal with the problem of registration with low overlap. In particular, we leverage sparse convolutions to extract robust and discriminative dense features, and further utilize attention mechanisms to explicitly predict the overlap region for point cloud fragments. The receptive field of our local point patches is constrained within the overlap region generated on the fly, which prevents the local point features from being contaminated by geometric information outside the overlap region. Experiments indicate the effectiveness and advantages of our method on both 3DLoMatch (all pairs under 30% overlap-ratio) and 3DMatch and illustrate obvious improvement over the existing results.

关键词： Point cloud compression Computer vision Three-dimensional displays Automation Neural networks Computer architecture Benchmark testing

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CausalTester: Measuring the Consistency of Replicated Services via Causality Semantics

CausalTester: Measuring the Consistency of Replicated Servic...

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Asian Test Symposium (ATS)

作者： Yu Tang Le Zhao Wei Yuan Xu Wang Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China Chengdu Aircraft Design & Research Institute Chengdu China

ISBN: (纸本)9781665440523

Cloud and big data systems often replicate data and prefer weak consistency such as eventual consistency for better scalability and availability. Such weak consistency may produce unexpected and harmful system behaviors, for example, stale reads and conflicting writes. In order to measure the consistency levels and help developers understand the harmful degree, we propose a testing framework called CausalTester to evaluate the causality semantics of replicated systems, including 12 real test cases collected from Twitter, Flickr, Amazon, the corresponding benchmark services, and the automatic detection of causality violation with crash injection. We implement the testing framework and measure the consistency of three widely-used distributed databases. The experimental results show that it is effective to detect the consistency violations for the weak consistency and helpful to find consistency-related bugs if the strong consistency is violated.

关键词： Social networking (online) Image databases Scalability Semantics Multimedia Web sites Computer bugs Blogs

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