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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Yuanxun Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

In this work, we consider the cross-scene person trajectory anomaly detection problem, which detects the anomalous trajectories across multiple nonoverlapping scenes. This problem is highly significant for public security, but it is still underexplored. Since the trajectory is not continuous across nonoverlapping camera views, we take use of person re-identification (re-ID) to associate the same pedestrian in different scenes while mitigating its inaccuracy by a directional probabilistic graph. To better distinguishing normal samples from anomalies, We formulate a maximized margin graph autoencoder (MMGAE) model, and the reconstruction error of the MMGAE is regarded as an anomaly indicator for the sample. To verify the effectiveness of our approach, we collected and labeled a new dataset. we also explore the impact of the re-ID performance on the anomaly detection problem and the effect of an inaccurately constructed graph on the MMGAE. © 2021 IEEE

关键词： Trajectories

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Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection 8

Real-to-Sim Grasp: Rethinking the Gap between Simulation and...

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8th Conference on Robot Learning, CoRL 2024

作者： Cai, Jia-Feng Chen, Zibo Wu, Xiao-Ming Jiang, Jian-Jian Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way1. However, this way explicitly or implicitly forces the simulated data to adapt to the noisy real data when training grasp detectors, where the positional drift and structural distortion within the camera noise will harm the grasp learning. In this work, we propose a Real-to-Sim framework for 6-DoF Grasp detection, named R2SGrasp, with the key insight of bridging this gap in a real-to-sim way2, which directly bypasses the camera noise in grasp detector training through an inference-time real-to-sim adaption. To achieve this real-to-sim adaptation, our R2SGrasp designs the Real-to-Sim Data Repairer (R2SRepairer) to mitigate the camera noise of real depth maps in data-level, and the Real-to-Sim Feature Enhancer (R2SEnhancer) to enhance real features with precise simulated geometric primitives in feature-level. To endow our framework with the generalization ability, we construct a large-scale simulated dataset cost-efficiently to train our grasp detector, which includes 64,000 RGB-D images with 14.4 million grasp annotations. Sufficient experiments show that R2SGrasp is powerful and our real-to-sim perspective is effective. The real-world experiments further show great generalization ability of R2SGrasp. Project page is available on https://***/R2SGrasp. © 2024 Proceedings of Machine Learning Research.

关键词： Image annotation

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Clause-level Relationship-aware Math Word Problems Solver

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Machine intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Class-specific Prompts in Vision Transformer for Continual Learning of New Diseases

Class-specific Prompts in Vision Transformer for Continual L...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Defeng Ye, Zejun Zheng, Wei-Shi Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Peng Cheng Laboratory Department of Network Intelligence Shenzhen China Moe Key Laboratory of Machine Intelligence and Advanced Computing China

ISBN: (纸本)9798350337488

Current intelligent diagnosis systems are often trained to diagnose a small number of diseases and lack the ability of continually learning new disease knowledge. To have such continual learning ability, the deployed intelligent system needs to be continually updated based on training data of only new diseases, without accessing old data of previously learned diseases due to privacy concerns and challenges in data sharing across medical centers. In this study, a novel and effective prompt learning strategy is proposed to help a pretrained and fixed Vision Transformer (ViT) continually learn new diseases. In particular, a set of unique prompts for each disease are effectively learned such that discriminative disease features can be well extracted from the fixed ViT under the instructions of prompts during feature extraction, even though the ViT feature extractor is pretrained in the natural image domain. Extensive empirical evaluations on two medical image datasets and one natural image dataset demonstrate the superior performance of the proposed method. The source code is available at https://***/zhaoedf/CSPrompt. © 2023 IEEE.

关键词： Continual learning Disease diagnosis Prompt learning

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DeepFake Videos Detection Based on Texture Features

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Computers, Materials & Continua 2021年第7期68卷 1375-1388页

作者： Bozhi Xu Jiarui Liu Jifan Liang Wei Lu Yue Zhang School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhou510006China Department of Computer Science University of Massachusetts LowellLowell01854MAUSA

In recent years,with the rapid development of deep learning technologies,some neural network models have been applied to generate fake ***,a deep learning based forgery technology,can tamper with the face easily and generate fake videos that are difficult to be distinguished by human *** spread of face manipulation videos is very easy to bring fake ***,it is important to develop effective detection methods to verify the authenticity of the *** to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in the forgery process,the texture details of the fake face are ***,in this paper,a new method is proposed to detect DeepFake ***,the texture features are constructed,which are based on the gradient domain,standard deviation,gray level co-occurrence matrix and wavelet transform of the face ***,the features are processed by the feature selection method to form a discriminant feature vector,which is finally employed to SVM for classification at the frame *** experimental results on the mainstream DeepFake datasets demonstrate that the proposed method can achieve ideal performance,proving the effectiveness of the proposed method for DeepFake videos detection.

关键词： DeepFake video tampering tampering detection texture feature

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Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data 38

Revealing Distribution Discrepancy by Sampling Transfer in U...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Zhilin Cao, Longbing Fan, Xuhui Zheng, Wei-Shi School of Computing Macquarie University Australia School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

There are increasing cases where the class labels of test samples are unavailable, creating a significant need and challenge in measuring the discrepancy between training and test distributions. This distribution discrepancy complicates the assessment of whether the hypothesis selected by an algorithm on training samples remains applicable to test samples. We present a novel approach called Importance Divergence (I-Div) to address the challenge of test label unavailability, enabling distribution discrepancy evaluation using only training samples. I-Div transfers the sampling patterns from the test distribution to the training distribution by estimating density and likelihood ratios. Specifically, the density ratio, informed by the selected hypothesis, is obtained by minimizing the Kullback-Leibler divergence between the actual and estimated input distributions. Simultaneously, the likelihood ratio is adjusted according to the density ratio by reducing the generalization error of the distribution discrepancy as transformed through the two ratios. Experimentally, I-Div accurately quantifies the distribution discrepancy, as evidenced by a wide range of complex data scenarios and tasks. © 2024 Neural information processing systems foundation. All rights reserved.

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LCOC: A Low-cost Optimizing Compiler for Large Scale Quantum Programs Towards Realistic Hardware

LCOC: A Low-cost Optimizing Compiler for Large Scale Quantum...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Yu Zhu Jinchen Xu Qing Mu Hongru Yang Xiaodong Ding Qiming Du Zheng Shan Laboratory for Advanced Computing and Intelligence Engineering China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Quantum compilers play an essential role in the translation of quantum algorithms on quantum hardware. As the size of quantum programs increases, the problem of extreme compilation overhead becomes apparent. To address this problem, compilation frameworks with high-level intermediate representations such as QCOR have been introduced. However, these frameworks still face significant overhead from qubit mapping and optimizing sequence redundancy, resulting in challenges when compiling circuits to realistic hardware. In our paper, a low-cost optimizing compiler (LCOC) is proposed, which addresses the overhead in qubit mapping and enhanced the optimization capabilities. A qubit mapping optimizer based on the LLVM Polly is introduced in LCOC, which implements an efficient mapping of quantum programs containing specific loop types. While achieving low-cost mapping, it considerably reduces the depth of the circuit. We also implement an optimization sequence generator that leverages the modularity of LLVM and iterative compilation to provide LCOC with a more flexible and efficient optimization sequence. We conduct experiments with 100+ qubit circuits. Experiments show that LCOC is up to 1540× faster compared to Qiskit and 74× faster compared to QCOR in compile time. The average speedup relative to Qiskit and QCOR are 194× and 8.64×, respectively. In addition, it can effectively reduce the circuit depth by 50.32% compared to Qiskit and 83.5% compared to QCOR on average.

关键词： Translation Quantum algorithm Optimizing compilers Qubit Circuits Redundancy Hardware Generators Iterative methods Optimization

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VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model for C/C++ Code 6

VulD-CodeBERT: CodeBERT-Based Vulnerability Detection Model ...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Xiong, Zhe Dong, Weiyu School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350353174

Recently, deep learning techniques have received widespread attention in code vulnerability and vulnerability detection. However, there are some problems with current vulnerability detection techniques, firstly, because of the non-homogeneity of vulnerability categories and quantities, there are omission false positives and overfitting problems for vulnerabilities with a small amount of data. Second, the existing detection methods have limited ability to characterize the code, there is insufficient depth of characterization, does not contain all of its vulnerability characteristics or is interspersed with useless information. Finally, the current research does not use the powerful feature mining capability of pre-trained models and does not employ model fusion to increase the effectiveness of model vulnerability detection. To solve the above problems, this paper proposes a new deep learning based pre-trained model for code vulnerability detection tool VulD-CodeBERT. By fine-tuning CodeBERT, a code adaptation extension for pre-trained models developed by Microsoft, the CodeBERT model can solve the problem of not being able to learn the vulnerability patterns due to insufficient data, thus resulting in missed false alarms. The sequence output from the CodeBERT model are added to a BiLSTM network with an attention mechanism to mine the local key features of the vulnerabilities combined with the global features of the pooler output from the CodeBERT model to deeply mine the dependencies between the vulnerability statements and derive the probability of the existence of the vulnerability. For vulnerability types that are difficult to uncover captured features or the number of samples is not enough, a polynomial plain Bayesian classifier is used for detection, and finally the two models are combined to deeply mine the vulnerability features, thus solving the problem of difficult and small samples, and reducing the leakage rate and false alarm rate. Experimental results sh

关键词： C++ (programming language)

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SPAFuzz: Web Security Fuzz Testing Tool Based on Fuzz Testing and Genetic Algorithm 4

SPAFuzz: Web Security Fuzz Testing Tool Based on Fuzz Testin...

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4th International Conference on Consumer Electronics and Computer engineering, ICCECE 2024

作者： Lin, Jiang Dong, Weiyu School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9798350384062

Fuzzing is one of the most effective methods currently available for detecting web vulnerabilities. However, traditional fuzzing suffers from issues such as low coverage and limited diversity in test cases. To address these issues, we propose a way to generate test cases based on dynamic feature libraries and attack models by analysing the characteristics of SQL and XSS. Additionally, we introduce a genetic algorithm to guide the population's evolution by monitoring the state changes of the web application. Furthermore, we improved the selection phase of the genetic algorithm to optimally deform multiple iterations for use cases with low fitness values in order to preserve the population diversity to expand the attack capability of the test cases and based on this the SPAFuzz fuzzy testing tool was implemented. Experimental results show that SPAFuzz is able to detect more network vulnerabilities than Sqlmap and Powerfuzzer. © 2024 IEEE.

关键词： Genetic algorithms

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An Opposition-Based Learning Adaptive Chaotic Particle Swarm Optimization Algorithm

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Journal of Bionic engineering 2024年第6期21卷 3076-3097页

作者： Chongyang Jiao Kunjie Yu Qinglei Zhou State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering UniversityZhengzhou450001China Henan Information Engineering School Zhengzhou Vocational College of Industrial SafetyZhengzhou450000China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450001China

To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was *** chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the *** inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local *** LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence *** addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.

关键词： PSO Opposition-based learning Chaotic motion Inertia weight Intelligent algorithm

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