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5th International Conference on Artificial intelligence and Computer engineering, ICAICE 2024

作者： Zhang, Chuancai Qu, Dan Du, Liming Yang, Kaiyuan School of Information Systems Engineering University of Information Engineering Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China School of Cyberspace Security Zhengzhou University Zhengzhou China

ISBN: (纸本)9798331528911

In Unsupervised Neural Machine Translation (UNMT) tasks, the lack of extensive parallel corpora makes it challenging for the model to directly optimize the correspondence between the source and target languages. UNMT models primarily rely on learning from monolingual data, making it difficult to fully capture cross-linguistic semantic alignment and consistency during training. Therefore, effectively enhancing semantic alignment between the source and target languages under unsupervised conditions has become a key challenge in the field of UNMT. To address this issue, this study proposes a semantic alignment and consistency enhancement mechanism based on contrastive learning, aiming to improve the model's ability to capture cross-lingual semantic differences by constructing an optimization objective focused on semantic consistency. The method utilizes a pretrained cross-lingual model to generate semantic embeddings for both the source and target languages, then designs a contrastive learning-based loss function that minimizes the semantic distance between correct translations and source sentences while increasing the semantic distance between incorrect translations and source sentences, thereby enhancing the model's performance in semantic consistency. The experiments validate the effectiveness and broad applicability of the semantic consistency optimization method based on contrastive learning, demonstrating great potential and advantages, especially in multilingual tasks of UNMT. The model achieved significant improvements in BLEU scores across various language pairs. © 2024 IEEE.

关键词： Contrastive Learning

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IMPROVING WEAKLY SUPERVISED OBJECT LOCALIZATION BY UNCERTAINTY ESTIMATION OF PSEUDO SUPERVISION

IMPROVING WEAKLY SUPERVISED OBJECT LOCALIZATION BY UNCERTAIN...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Chen, Xi Ma, Andy J. Guo, Nanxi Chen, Jiajia School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

Pseudo bounding box supervision is a promising approach for weakly supervised object localization (WSOL) with only image-level labels. However, the generated pseudo bounding boxes may be inaccurate or even completely non-overlapped with the objects of interest. In this paper, we propose to estimate the uncertainty of pseudo bounding boxes such that the negative impact caused by inaccurate estimation of pseudo supervision could be alleviated for better WSOL. The refined bounding boxes and corresponding variance uncertainties are learned by training a neural network regressor to penalize the erroneous estimations. To the best of our knowledge, this is the first work to incorporate uncertainty information of pseudo bounding boxes for WSOL. Experimental results show that our method not only outperforms previous state-of-the-art methods in CUB-200-2011 and ILSVRC datasets but also gives more precise bounding box prediction when the IoU threshold is higher. © 2021 IEEE

关键词： Uncertainty analysis

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A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1778-1791页

作者： Ning Tan Peng Yu Zhiyan Zhong Fenglei Ni IEEE the School of Computer Science and Engineering&Key Laboratory of Machine Intelligence and Advanced Computing Ministry of EducationSun Yat-sen UniversityGuangzhou 510006China the School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China the State Key Laboratory of Robotics and Systems Harbin Institute of Technology(HIT)Harbin 150080China

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks *** a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated ***,robust model-free control of robotic arms in the presence of noise interference remains a problem worth *** this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant ***,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic *** finite-time convergence and robustness of the proposed control scheme are proven by theoretical ***,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

关键词： Dual zeroing neural networks(ZNN) finite-time convergence model-free robot control robustness analysis

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Research on Sleep Staging Method Based on Multi-Scale Convolution and Self-Attention Mechanism 2

Research on Sleep Staging Method Based on Multi-Scale Convol...

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2nd International Conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Pan, Dongdong Li, Yingying School of Computer and Artificial Intelligence Zhengzhou University China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University China

ISBN: (纸本)9781510666733

Sleep is a very important physiological process for humans, and sleep staging is an important basis for evaluating sleep quality and diagnosing sleep-related diseases. Manual sleep staging is time-consuming and laborious, and the staging results are easily affected by subjective factors. Therefore, the research on automatic sleep staging models has extremely high research value and clinical application value. This paper proposes a sleep staging method based on multi-scale one-dimensional convolutional neural network and self-attention mechanism. The attention mechanism mines the channel correlation and temporal correlation of different channels respectively. This paper selects the sleep data of 153 normal subjects in the public dataset Sleep-EDF for model training and testing. The average staging accuracy rate was 84.5%, and the consistency test value was 0.781, which was better than the consistency test value of 0.75 for the inter-physician score. The experimental results show that the algorithm model in this paper has a high staging effect, can make full use of sleep data, and has important research significance for assisting clinicians in rapid sleep staging. © 2023 SPIE.

关键词： Convolution

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Survey on the Improvement and Application of HHL Algorithm

Survey on the Improvement and Application of HHL Algorithm

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2022 International Conference on Computer Modelling and Big Data Analytics, CMBDA 2022

作者： Liu, Xiaonan Xie, Haoshan Liu, Zhengyu Zhao, Chenyan State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University ZhengZhou450000 China School of Computer and Artificial Intelligence ZhengZhou University ZhengZhou450000 China

Quantum computing is a new computing mode that follows the laws of quantum mechanics to control quantum information units for computation. In terms of computational efficiency, due to the existence of quantum mechanical superposition, some known quantum algorithms can process problems faster than traditional general-purpose computers. HHL algorithm is an algorithm for solving linear system problems. Compared with classical algorithms in solving linear equations, it has an exponential acceleration effect in certain cases and as a sub-module, it is widely used in some machine learning algorithms to form quantum machines learning algorithms. However, there are some limiting factors in the use of this algorithm, which affect the overall effect of the algorithm. How to improve it to make the algorithm perform better has become an important issue in the field of quantum computing. This paper summarizes the optimization and improvement of HHL algorithm since it was proposed, and the application of HHL algorithm in machine learning, and discusses some possible future improvements of some subroutines in HHL algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Linear systems

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Inequality-Type Zhang Equivalency of Great-Than-or-Equal-to (GTOET) Sign in Matrix-Vector,Matrix,and Integral Situations Applied to Obstacle Avoidance 41

Inequality-Type Zhang Equivalency of Great-Than-or-Equal-to ...

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第41届中国控制会议

作者： Yunong Zhang Jianzhen Xiao School of Computer Science and Engineering Sun Yat-sen University Research Institute of Sun Yat-sen University in Shenzhen Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the concept of Zhang equivalency(ZE) and equality-type ZE are *** the basis of equalitytype ZE,we introduce the inequality-type ZE of great-than-or-equal-to(GTOET) sign and thus propose the order-1,order-2,and order-3 general formulas of the inequality-type ZE of GTOET ***,we investigate the inequality-type ZE of GTOET sign in three different and appealing situations,including matrix-vector situation,matrix situation,and integral ***,the corresponding formulas are *** distance between a certain object and an obstacle can be considered as the point-to-point distance,and thus the obstacle avoidance scheme is described as an *** by that thought,we apply the inequality-type ZE of GTOET sign to the matrix-form obstacle avoidance.

关键词： Zhang equivalency Great-than-or-equal-to(GTOET) sign Inequalities Obstacle avoidance

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Software Diversification Protection Methods for Binary Programs

Software Diversification Protection Methods for Binary Progr...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Yingchao Chen Junchao Wang Xin Zhou Jianmin Pang Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Software diversification is an effective software protection method against reverse engineering and code reuse attacks, which can provide heterogeneous redundant execution bodies for mimetic defense mechanisms. Most existing software diversification methods require access to the source code, which can provide defenders with more valuable information for devising effective defense strategies. However, due to commercial copyright and the purpose of preventing software piracy, developers often keep their software closed-source, making it difficult to access the source code. Therefore, a method called R2BF (ReCooking and Randomizing Binary File) is proposed, which combines decompilation and diversification compilation techniques to address the difficulty of obtaining the source code. This method involves diversifying the source code through compilation to achieve software diversification for binary executable files. Security and performance testing of diversified C programs has shown that diversified binary executable files can mitigate vulnerabilities resulting from code reuse, validating that this method can enhance the security of binary programs and is feasible for software protection. The diversified binary executable files are nearly identical to the original, non-diversified files in terms of file size and execution time, and may even exhibit acceleration and optimization effects in certain scenarios.

关键词： Software testing Codes Source coding Software protection Software algorithms Reverse engineering Signal processing algorithms Software Security Optimization

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Learning High-Performance Spiking Neural Networks with Multi-Compartment Spiking Neurons 12th

Learning High-Performance Spiking Neural Networks with Mult...

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12th International Conference on Image and Graphics, ICIG 2023

作者： Li, Xinjie Tang, Jianxiong Lai, Jianhuang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783031463075

In recent years, spiking neural networks (SNNs) have gained significant attention due to bio-inspired working mechanism. The VGG-like and ResNet-like architectures are widely used for SNNs modeling. However, the spiking features of such architectures are generated by the layer-wise Integrate-and-Fire (IF) dynamics. The firing of deep neurons is independent of the neurons in the shallow layers. In this paper, we propose Multi-Compartment Spiking Neural Network (MC-SNN) that integrates the MPs of the shallow neurons to fire the deep neurons. Specifically, the MC-SNN is modeled by a Multi-Compartment Leaky Integrate-and-Fire (MC-LIF) neuron and an Adaptive Gating Unit (AGU). The MC-LIF neuron models the IF dynamics using the MPs from both deep and shallow layers, and the AGU adaptively scales the MPs of the MC-LIF neuron. These increase the information interaction between spiking neurons and improve the performance of SNNs. Besides, we design the Binarized Synaptic Encoder (BSE) to reduce the computation cost for the input of SNNs. Experimental results show that the MC-SNN performs well on the neuromorphic datasets, reaching 79.52% and 81.24% on CIFAR10-DVS and N-Caltech101, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurons

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

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Computational Visual Media 2021年第2期7卷 241-252页

作者： Jin Zhou Qing Zhang Jian-Hao Fan Wei Sun Wei-Shi Zheng School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China. School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education(Sun Yat-sen University)Guangzhou 510006China Peng Cheng Laboratory Shenzhen 518000China

Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same *** address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal *** achieve this in a coarse to fine manner,by joint regression and learning from pairwise ***,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image *** then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based ***,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge *** demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art ***,we show that the learned personalized image aesthetic benefits a wide variety of applications.

关键词： s personalized image aesthetic assessment deep convolutional neural networks pairwise ranking regression

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