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DepthGAN: GAN-based depth generation from semantic layouts

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Computational Visual Media 2024年第3期10卷 505-522页

作者： Yidi Li Jun Xiao Yiqun Wang Zhengda Lu School of artificial intelligence University of Chinese Academy of SciencesBeijingChina college of computer science Chongqing UniversityChongqingChina

Existing GAN-based generative methodsare typically used for semantic image synthesis. Wepose the question of whether GAN-based architecturescan generate plausible depth maps and find thatexisting methods have difficulty in generating depthmaps which reasonably represent 3D scene structuredue to the lack of global geometric ***, we propose DepthGAN, a novel method ofgenerating a depth map using a semantic layout asinput to aid construction, and manipulation of wellstructured 3D scene point clouds. Specifically, wefirst build a feature generation model with a cascadeof semantically-aware transformer blocks to obtaindepth features with global structural *** our semantically aware transformer block, wepropose a mixed attention module and a semanticallyaware layer normalization module to better exploitsemantic consistency for depth features ***, we present a novel semantically weighteddepth synthesis module, which generates adaptivedepth intervals for the current scene. We generate thefinal depth map by using a weighted combination ofsemantically aware depth weights for different depthranges. In this manner, we obtain a more accuratedepth map. Extensive experiments on indoor andoutdoor datasets demonstrate that DepthGAN achievessuperior results both quantitatively and visually for thedepth generation task.

关键词： depth map generation generative model transformer scene generation

Short-time maritime target detection based on polarization scattering characteristics

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Journal of Systems Engineering and Electronics 2024年第1期35卷 55-64页

作者： CHEN Shichao LUO Feng TIAN Min LYU Wanghan college of computer and Information Engineering(college of artificial intelligence) Nanjing Tech UniversityNanjing 211816China Hangzhou Institute of technology Xidian UniversityHangzhou 311200China

In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is ***,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data *** to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter ***,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering ***,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.

关键词： sea clutter small target radar detection Cameron decomposition characteristics analysis

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SpikingMiniLM: energy-efficient spiking transformer for natural language understanding

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science China(Information sciences) 2024年第10期67卷 115-128页

作者： Jiayu ZHANG Jiangrong SHEN Zeke WANG Qinghai GUO Rui YAN Gang PAN Huajin TANG college of computer science and technology Zhejiang University The State Key Lab of Brain-Machine intelligence Zhejiang University Collaborative Innovation Center of artificial intelligence Zhejiang University Advanced Computing and Storage Laboratory Huawei Technologies Co. Ltd. college of computer science and technology Zhejiang University of Technology MOE Frontier science Center for Brain science and Brain-Machine Integration Zhejiang University

In the era of large-scale pretrained models, artificial neural networks（ANNs） have excelled in natural language understanding（NLU） tasks. However, their success often necessitates substantial computational resources and energy consumption. To address this, we explore the potential of spiking neural networks（SNNs） in NLU——a promising avenue with demonstrated advantages, including reduced power consumption and improved efficiency due to their event-driven characteristics. We propose the SpikingMiniLM,a novel spiking Transformer model tailored for natural language understanding. We first introduce a multi-step encoding method to convert text embeddings into spike trains. Subsequently, we redesign the attention mechanism and residual connections to make our model operate on the pure spike-based paradigm without any normalization technique. To facilitate stable and fast convergence, we propose a general parameter initialization method grounded in the stable firing rate principle. Furthermore, we apply an ANN-to-SNN knowledge distillation to overcome the challenges of pretraining SNNs. Our approach achieves a macro-average score of 75.5 on the dev sets of the GLUE benchmark, retaining 98% of the performance exhibited by the teacher model MiniLMv2. Our smaller model also achieves similar performance to BERTMINIwith fewer parameters and much lower energy consumption, underscoring its competitiveness and resource efficiency in NLU tasks.

关键词： spiking neural networks natural language understanding spiking Transformer spike-based attention multi-step encoding ANN-to-SNN distillation

Internet of Things Enabled DDoS Attack Detection Using Pigeon Inspired Optimization Algorithm with Deep Learning Approach

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computers, Materials & Continua 2024年第9期80卷 4047-4064页

作者： Turki Ali Alghamdi Saud S.Alotaibi Department of computer science and artificial intelligence College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Internet of Things(IoTs)provides better solutions in various fields,namely healthcare,smart transportation,home,*** Denial of Service(DoS)outbreaks in IoT platforms is significant in certifying the accessibility and integrity of IoT *** learning(DL)models outperform in detecting complex,non-linear relationships,allowing them to effectually severe slight deviations fromnormal IoT activities that may designate a DoS *** uninterrupted observation and real-time detection actions of DL participate in accurate and rapid detection,permitting proactive reduction events to be executed,hence securing the IoT network’s safety and ***,this study presents pigeon-inspired optimization with a DL-based attack detection and classification(PIODL-ADC)approach in an IoT *** PIODL-ADC approach implements a hyperparameter-tuned DL method for Distributed Denial-of-Service(DDoS)attack detection in an IoT ***,the PIODL-ADC model utilizes Z-score normalization to scale input data into a *** handling the convolutional and adaptive behaviors of IoT,the PIODL-ADCmodel employs the pigeon-inspired optimization(PIO)method for feature selection to detect the related features,considerably enhancing the recognition’s ***,the Elman Recurrent Neural Network(ERNN)model is utilized to recognize and classify DDoS ***,reptile search algorithm(RSA)based hyperparameter tuning is employed to improve the precision and robustness of the ERNN method.A series of investigational validations is made to ensure the accomplishment of the PIODL-ADC *** experimental outcome exhibited that the PIODL-ADC method shows greater accomplishment when related to existing models,with a maximum accuracy of 99.81%.

关键词： Internet of things denial of service deep learning reptile search algorithm feature selection

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

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computer Modeling in Engineering & sciences 2024年第6期139卷 3209-3231页

作者： Weiya Shi Shaowen Zhang Shiqiang Zhang college of artificial intelligence and Big Data Henan University of TechnologyZhengzhou450001China college of Information science and Engineering Henan University of TechnologyZhengzhou450001China

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural *** these efforts,the detection of small objects in remote sensing remains a formidable *** deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep ***,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection ***,the sensitivity of small objects to the bounding box perturbation further increases the detection *** this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote *** address feature loss in deep layers,we have devised a cross-layer attention fusion *** noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid ***,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)*** efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available *** experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

关键词： Small object detection attention mechanism cross-layer fusion discrete cosine transform

RepBoTNet-CESA:An Alzheimer’s Disease computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

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computers, Materials & Continua 2024年第5期79卷 2879-2905页

作者： Xiabin Zhang Zhongyi Hu Lei Xiao Hui Huang college of computer science and artificial intelligence Wenzhou UniversityWenzhou325035China Key Laboratory of intelligence Image Processing and Analysis Wenzhou UniversityWenzhou325035China

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global *** contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel ***,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is *** tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features *** combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification ***,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer ***,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC *** demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic ***,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet ***,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

关键词： Alzheimer CNN structural reparameterization multi head self attention computer aided diagnosis

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

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Defence technology（防务技术） 2024年第4期34卷 19-28页

作者： Xiang-long Liang Zhi-kai Yao Yao-wen Ge Jian-yong Yao School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing210094China college of Automation&college of artificial intelligence Nanjing University of Post and TelecommunicationNanjing210023China

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange *** considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and *** these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become *** by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical *** approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control *** from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control *** developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output ***,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

关键词： Adaptive control Reinforcement learning Uncertain mechanical systems Asymptotic tracking

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ContextAug:model-domain failing test augmentation with contextual information

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Frontiers of computer science 2024年第2期18卷 43-60页

作者： Zhuo ZHANG Jianxin XUE Deheng YANG Xiaoguang MAO School of Information technology and Engineering Guangzhou College of CommerceGuangzhou 511363China School of computer and Information Engineering Institute for Artificial IntelligenceShanghai Polytechnic UniversityShanghai 201209China college of computer National University of Defense TechnologyChangsha 410073China

In the process of software development,the ability to localize faults is crucial for improving the efficiency of *** speaking,detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development *** have tried to utilize various methods to locate the faulty ***,failing test cases usually account for a small portion of the test suite,which inevitably leads to the class-imbalance phenomenon and hampers the effectiveness of fault ***,in this work,we propose a new fault localization approach named *** obtaining dynamic execution through test cases,ContextAug traces these executions to build an information model;subsequently,it constructs a failure context with propagation dependencies to intersect with new model-domain failing test samples synthesized by the minimum variability of the minority feature *** contrast to traditional test generation directly from the input domain,ContextAug seeks a new perspective to synthesize failing test samples from the model domain,which is much easier to augment test *** conducting empirical research on real large-sized programs with 13 state-of-the-art fault localization approaches,ContextAug could significantly improve fault localization effectiveness with up to 54.53%.Thus,ContextAug is verified as able to improve fault localization effectiveness.

关键词： context fault localization test cases

Retrieval-and-alignment based large-scale indoor point cloud semantic segmentation

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science China(Information sciences) 2024年第4期67卷 164-180页

作者： Zongyi XU Xiaoshui HUANG Bo YUAN Yangfu WANG Qianni ZHANG Weisheng LI Xinbo GAO School of computer science and technology Chongqing University of Posts and Telecommunications Shanghai artificial intelligence Laboratory School of Electronic Engineering and computer science Queen Mary University of London

Current methods for point cloud semantic segmentation depend on the extraction of descriptive features. However, unlike images, point clouds are irregular and often lack texture information, making it demanding to extract discriminative features. In addition, noise, outliers, and uneven point distribution are commonly present in point clouds, which further complicates the segmentation task. To address these problems, a novel architecture is proposed for direct and accurate large-scale point cloud segmentation based on point cloud retrieval and alignment. The proposed approach involves using a feature-based point cloud retrieval method for searching for reference point clouds with annotations from a dataset. In the following segmentation stage, an overlap-based point cloud registration method has been developed to align the target and reference point clouds. For accurate and robust alignment, an overlap region estimation module is trained to locate the optimal overlap region between two pieces of point clouds in a coarse-to-fine manner. In the detected overlap region, the global and local features of the points are extracted and combined for featuremetric registration to obtain accurate transformation parameters between the target and reference point clouds. After alignment, the annotated segmentation of the reference is transferred to the target point clouds to obtain accurate segmentation results. Extensive experiments are conducted to show that the developed method outperforms the state-of-the-art approaches in terms of both accuracy and robustness against noise and outliers.

关键词： point cloud semantic segmentation large-scale indoor point clouds point cloud alignment overlap estimation label transfer