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World Congress on computing and Communication Technologies (WCCCT)

作者： Hu Tairan Gao Wenlong Zhou Tianyang Zang Yichao National Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

With the growth of network scale, present researches on the attack path discovery often encounter the problem of search space explosion that results in fails. To tackle the problem of attack path discovery for large-scale networks, this paper proposes a bi-population genetic algorithm for the attack path discovery in large-scale networks. Firstly, this paper represents the connective relationship between hosts as vectors and uses the Balanced Iterative Reducing and Clustering Using Hierarchies (Birch) algorithm to realize network decomposition. Then, the attack path discovery problem is encoded based on the decomposition result, and the bi-population mechanism is introduced to find the attack path. The experiment result shows that our algorithm performs better than Metric-FF and SGA in optimization and efficiency.

关键词： Clustering algorithms Search problems Iterative algorithms Explosions Communications technology Optimization Genetic algorithms

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Research on 3D Human Body Reconstruction Algorithm of Monocular Image 5

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

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5th Asian Conference on Artificial Intelligence Technology, ACAIT 2021

作者： Liang, Zijian Liang, Huan Fu, Wenlong 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)9781665426305

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. © 2021 IEEE.

关键词： Three dimensional computer graphics

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Integrating KNN and Gradient Boosting Decision Tree for Recommendation

Integrating KNN and Gradient Boosting Decision Tree for Reco...

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IEEE advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Qiming Du Nan Li Shudan Yang Daozhu Sun Wenfu Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

As an efficient information filtering tool, the recommendation system can help users obtain valuable information quickly and efficiently while effectively alleviating the information load problem. However, the classic collaborative filtering algorithm has data sparsity problems in practical applications, which affects the accuracy of recommendation. To solve this problem, this paper took movie recommendation as the research object and proposed a hybrid recommendation algorithm named K-GBDT which combined KNN and gradient boosting decision tree. Firstly, it used KNN to obtain the similar information of the target user and item for preliminary prediction. At the same time, it mined users' basic information features and potential features of users and items from the original data. Then, it tried to adopt XGBoost, LightGBM and CatBoost algorithm which were proposed base the idea of gradient boosting decision tree to build regression model. Finally, the target user's rating for the movie was predicted with the trained model, and the recommendation list was generated based on the predicted results. Compared with some classic recommendation algorithms such as NMF, Slope One, Co-Clustering and etc. Based on MovieLens datasets, results show that the mean absolute error and root mean square error of this algorithm are both low, and its recommendation accuracy is higher.

关键词： Training Distributed databases Prediction algorithms Boosting Feature extraction Motion pictures Data models

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An overview on the designs of distributed observers in LTI multi-agent systems

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Science China(Technological Sciences) 2021年第11期64卷 2337-2346页

作者： PEI YingQing GU HaiBo LIU KeXin LU JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

With the rapid development of information technologies and cloud computing, sensor networks play an increasingly important role in our society. Over the past few decades, distributed observer theory has attracted unprecedented attention due to its wide potential applications in different areas. Meanwhile, various approaches and algorithms have been proposed and investigated. The design of distributed observers is one of the frontier topics of system and control research, which has the significant theoretical values and broad application prospects. This paper attempts to review the representative models and the corresponding approaches for distributed observer design in linear time-invariant(LTI) systems. Firstly, the research backgrounds and main advances of distributed observer designs are briefly reviewed. Then, recent results of distributed observer designs for discrete-time and continuous-time LTI multi-agent systems(MASs) are introduced in detail, respectively. Finally, the prospects and the future work directions of the design of distributed observers are put forward. The main purpose of this paper is to promote the emerging topic on the designs of distributed observers, with focuses on the interdisciplinary interest from technological sciences.

关键词： multi-agent system linear time-invarient system distributed observer consensus

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Research on Security Protocol Analysis Tool SmartVerif

Research on Security Protocol Analysis Tool SmartVerif

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2021 International Conference on Computer Technology, Information engineering and Electron Materials, CTIEEM 2021

作者： Lu, Siqi Han, Qingdi Miao, Xuyang Liu, Yubo State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450000 China Henan Key Laboratory of Network Cryptography Technology Henan Zhengzhou450000 China Department of Information Science and Engineering Ocean University of China Shandong Qingdao266100 China

Security protocols have been designed to protect the security of the network. However, many security protocols cannot guarantee absolute security in real applications. Therefore, security tests of the network protocol become particularly important. In this paper, firstly, we introduce SmartVerif, which is the first formal analysis tool to automatically verify the security of protocols through dynamic strategies. And then, we use SmartVerif to verify the pseudo-randomness of the encapsulated key of the Two-Pass AKE protocol, which was proposed by Liu's in ASIACRYPT in 2020. Finally, we summary our work and show some limitations of SmartVerif. At the same time, we also point out the direction for future improvement of SmartVerif. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Network security

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Progressive Pretext Task Learning for Human Trajectory Prediction

arXiv

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arXiv 2024年

作者： Lin, Xiaotong Liang, Tianming Lai, Jianhuang Hu, Jian-Fang School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Human trajectory prediction is a practical task of predicting the future positions of pedestrians on the road, which typically covers all temporal ranges from short-term to long-term within a trajectory. However, existing works attempt to address the entire trajectory prediction with a singular, uniform training paradigm, neglecting the distinction between short-term and long-term dynamics in human trajectories. To overcome this limitation, we introduce a novel Progressive Pretext Task learning (PPT) framework, which progressively enhances the model's capacity of capturing short-term dynamics and long-term dependencies for the final entire trajectory prediction. Specifically, we elaborately design three stages of training tasks in the PPT framework. In the first stage, the model learns to comprehend the short-term dynamics through a stepwise next-position prediction task. In the second stage, the model is further enhanced to understand long-term dependencies through a destination prediction task. In the final stage, the model aims to address the entire future trajectory task by taking full advantage of the knowledge from previous stages. To alleviate the knowledge forgetting, we further apply a cross-task knowledge distillation. Additionally, we design a Transformer-based trajectory predictor, which is able to achieve highly efficient two-step reasoning by integrating a destination-driven prediction strategy and a group of learnable prompt embeddings. Extensive experiments on popular benchmarks have demonstrated that our proposed approach achieves state-of-the-art performance with high efficiency. Code is available at https://***/iSEE-laboratory/PPT. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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Differentiable Gated Autoencoders for Unsupervised Feature Selection

SSRN

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SSRN 2024年

作者： Chen, Zebin Bian, Jintang Qiao, Bo Xie, Xiaohua School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Unsupervised feature selection (UFS) aims to identify a subset of the most informative features from high-dimensional data without labels. However, most existing UFS methods cannot adequately capture the intricate nonlinear relationships present in high-dimensional data, limiting their ability to obtain meaningful features. In this letter, we propose a novel UFS model that leverages the autoencoder with a differentiable gating function to address this limitation. Our model optimizes a bi-level loss function to automatically select a subset of informative and stable features by capturing the nonlinear interactions between features. Furthermore, we leverage the continuous relaxation of the Bernoulli distribution to parameterize stochastic gates, thereby enabling the learning of a relaxed model through a low-variance gradient estimator. The proposed model does not necessitate prior knowledge or domain-specific tuning, making it a versatile and widely applicable method. Extensive experiments on 13 benchmark datasets demonstrate the effectiveness and superiority of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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Dendritic integration inspired artificial neural networks capture data correlation 24

Dendritic integration inspired artificial neural networks ca...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chongming Liu Jingyang Ma Songting Li Douglas Zhou School of Mathematical Sciences and Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematical Sciences and Institute of Natural Sciences and Ministry of Education Key Laboratory of Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai China School of Mathematical Sciences and Institute of Natural Sciences and Ministry of Education Key Laboratory of Scientific and Engineering Computing and Shanghai Frontier Science Center of Modern Analysis Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331314385

Incorporating biological neuronal properties into Artificial Neural Networks (ANNs) to enhance computational capabilities is under active investigation in the field of deep learning. Inspired by recent findings indicating that dendrites adhere to quadratic integration rule for synaptic inputs, this study explores the computational benefits of quadratic neurons. We theoretically demonstrate that quadratic neurons inherently capture correlation within structured data, a feature that grants them superior generalization abilities over traditional neurons. This is substantiated by few-shot learning experiments. Furthermore, we integrate the quadratic rule into Convolutional Neural Networks (CNNs) using a biologically plausible approach, resulting in innovative architectures—Dendritic integration inspired CNNs (Dit-CNNs). Our Dit-CNNs compete favorably with state-of-the-art models across multiple classification benchmarks, e.g., ImageNet-1K, while retaining the simplicity and efficiency of traditional CNNs. All source code are available at https://***/liuchongming1999/Dendritic-integration-inspired-CNN-NeurIPS-2024.

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Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding

Siamese Learning with Joint Alignment and Regression for Wea...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chaolei Tan Jianhuang Lai Wei-Shi Zheng Jian-Fang Hu School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Video Paragraph Grounding (VPG) is an emerging task in video-language understanding, which aims at localizing multiple sentences with semantic relations and temporal or-der from an untrimmed video. However, existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. In this work, we introduce and explore Weakly-Supervised Video Paragraph Grounding (WSVPG) to elim-inate the need of temporal annotations. Different from pre-vious weakly-supervised grounding frameworks based on multiple instance learning or reconstruction learning for two-stage candidate ranking, we propose a novel siamese learning framework that jointly learns the cross-modal feature alignment and temporal coordinate regression without timestamp labels to achieve concise one-stage localization for WSVPG. Specifically, we devise a Siamese Grounding TRansformer (SiamGTR) consisting of two weight-sharing branches for learning complementary supervision. An Aug-mentation Branch is utilized for directly regressing the tem-poral boundaries of a complete paragraph within a pseudo video, and an Inference Branch is designed to capture the order-guided feature correspondence for localizing multi-ple sentences in a normal video. We demonstrate by exten-sive experiments that our paradigm has superior practica-bility and flexibility to achieve efficient weakly-supervised or semi-supervised learning, outperforming state-of-the-art methods trained with the same or stronger supervision.

关键词： Location awareness Computer vision Grounding Annotations Semantics Semisupervised learning Transformers

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Programming Logic Modeling and Cross-Program Defect Detection Method for Object-Oriented Code

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Computers, Materials & Continua 2020年第7期64卷 273-295页

作者： Yan Liu Wenyuan Fang Qiang Wei Yuan Zhao Liang Wang State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering UniversityZhengzhou450001China The School of Computing Newcastle UniversityNewcastle upon TyneNE45TGUK

Code defects can lead to software vulnerability and even produce vulnerability *** research shows that the code detection technology with text analysis can judge whether object-oriented code files are defective to some ***,these detection techniques are mainly based on text features and have weak detection capabilities across *** with the uncertainty of the code and text caused by the developer’s personalization,the programming language has a stricter logical specification,which reflects the rules and requirements of the language itself and the developer’s potential way of *** article replaces text analysis with programming logic modeling,breaks through the limitation of code text analysis solely relying on the probability of sentence/word occurrence in the code,and proposes an object-oriented language programming logic construction method based on method constraint relationships,selecting features through hypothesis testing ideas,and construct support vector machine classifier to detect class files with defects and reduce the impact of personalized programming on detection *** the experiment,some representative Android applications were selected to test and compare the proposed *** terms of the accuracy of code defect detection,through cross validation,the proposed method and the existing leading methods all reach an average of more than 90%.In the aspect of cross program detection,the method proposed in this paper is superior to the other two leading methods in accuracy,recall and F1 value.

关键词： Method constraint relationship programming logic code defect hypothesis test

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