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16th International Conference on Intelligent Computing, ICIC 2020

作者： Li, Heng Hu, Yan Li, Sanqian Lin, Wenjun Liu, Peng Higashita, Risa Liu, Jiang School of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Tomey Corporation Nagoya451-0051 Japan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9783030608019

Nowadays, with the rapid spread of Corona Virus Disease 2019 (COVID-19), this epidemic has become a threatening risk for global public health. Medical workers and researchers all over the world are struggling against the novel coronavirus in the front line. Because the computed tomography (CT) images from infected patients exposure characteristic abnormalities, automatic CT analyzers based on AI-based algorithms are extensively employed as effective weapons to aid clinicians. However, unbalanced data and lack of annotations obstruct AI-based algorithms applying in aided diagnosis because of their low performance. Therefore, in order to solve the above problems, a general-purpose solution is proposed to synthesize COVID-19 CT scans from non-COVID-19 data for providing high-quality negative-positive paired CT scans. Particularly, we introduce an elastic registration algorithm of CT images to manufacture paired training data. Then, a conditional Generative Adversarial Networks (GANs) based image-to-image translation model is implemented to synthesize COVID-19 CT scans from non-COVID-19 data. The effectiveness of our proposed algorithm used in COVID-19 aided diagnosis is verified in the experiments, and the identification and detection capacities of the classification models have been enhanced with the generated CT scans. Specifically, the precise lesion location is achieved by the generated data with a weakly supervised algorithm of class activation mapping (CAM). The model and code of this paper are publicly available at https://***/lihengbit/Synthesis-of-COVID-19-CT-Scan. © 2020, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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Adaptive Neural Control of Robot Manipulator with Prescribed Performance

Adaptive Neural Control of Robot Manipulator with Prescribed...

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第35届中国控制会议

作者： Min Wang Anle Yang School of Automation Science and Engineering Guangzhou Key Laboratory of Brain Computer Interaction and ApplicationsSouth China University of Technology

This paper focuses on adaptive neural control of robot manipulator with unknown system dynamics under the limitation of prescribed performance. A performance function is introduced to express the prescribed constraints of tracking ***, a performance transformation method is proposed to solve the problem of the prescribed performance. The unknown dynamics of robot are approximated by radial basis function(RBF) neural networks(NN). An adaptive neural control law is proposed based on Lyapunov stability. The proposed control method guarantees that all the signals in closed-loop system are uniformly ultimately bounded(UUB), simultaneously the tracking errors converge to small preassigned regions of zero with satisfying prescribed performance. Simulation studies are shown to demonstrate the effectiveness of the proposed control method.

关键词： Adaptive Neural Control Prescribed Performance Performance Transformation Robot Manipulator

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Combination of NMF and IVA for improving the quality of EOG signals 5

Combination of NMF and IVA for improving the quality of EOG ...

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5th IEEE International Conference on Signal and Image Processing, ICSIP 2020

作者： Sun, Wenhui Xue, Bing Xue, Jingyi Zhang, Shengli Lv, Zhao Anhui University School of Computer Science and Technology Hefei230601 China Suzhou Xiaoma E-Commerce Co. Ltd Suzhou234000 China Hangzhou Dianzi University Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence Hangzhou310018 China

ISBN: (纸本)9781728168968

Eye movements play an important role in the implementation of human computer interface (HCI) system, which could be recorded by the electrooculography (EOG) technique. However, the recorded EOG signals are always contaminated by various latent artifacts. Aiming at obtaining high-quality EOG signals, a blind source separation (BSS) method combined with non-negative matrix factorization (NMF) and independent vector analysis (IVA), is used in the separation problem of EOG signals. The effectiveness of the addition of NMF is validated on the classification tasks, and the corresponding average classification accuracy is higher than that of extended Infomax independent component analysis (ICA) and IVA, with the improvement of 4.42% and 3.26%, respectively. This result indicates that the combination of NMF and IVA can achieve better separation performance, in which the NMF could provide more information close to the source signals in the iteration step. © 2020 IEEE.

关键词： Blind source separation

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Oracle character recognition by nearest neighbor classification with deep metric learning 15

Oracle character recognition by nearest neighbor classificat...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Zhang, Yi-Kang Zhang, Heng Liu, Yong-Ge Yang, Qing Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing China School of Computer & Information Engineering Anyang Normal University Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan China

ISBN: (纸本)9781728128610

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU. © 2019 IEEE.

关键词： Character recognition

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Tracking the 3D position and orientation of flying swarms with learned kinematic pattern using LSTM network

Tracking the 3D position and orientation of flying swarms wi...

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2017 IEEE International Conference on Multimedia and Expo, ICME 2017

作者： Wang, Shuo Hong Su, Hai Feng Cheng, Xi En Liu, Ye Quo, Aike Chen, Yan Qiu School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Institute of Neuroscience State Key Laboratory of Neuroscience CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences CAS Shanghai China Jingdezhen Ceramic Institute Jindezhen China College of Automation Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781509060672

Accurately and reliably tracking the 3D position and orientation of individuals in large flying swarms is valuable not only for scientific researches but also practical applications. However, large quantity, frequent occlusions, similar appearance, tiny body size and abrupt motion make it remain an open problem. The 3D flying swarm tracking method proposed in this paper tracks both position and orientation of each individual in the swarm using the particle filter framework. Particles are scattered more pertinently by the dynamic model based on the learned kinematic pattern of a single target with a Long Short-Term Memory (LSTM) network. In addition, the observation model combines the Weighted Occupancy Ratio (WOR) and Temporal Appearance Coherency (TAC) cues in each view to improve the accuracy and robustness of the reconstructed body orientation. Experiments on both simulation and real-world data sets demonstrate the effectiveness and superiority of the proposed method. © 2017 IEEE.

关键词： Long short-term memory

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Resource-constrained federated edge learning with heterogeneous data: Formulation and analysis

arXiv

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arXiv 2021年

作者： Liu, Yi Zhu, Yuanshao Yu, James J.Q. The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Efficient collaboration between collaborative machine learning and wireless communication technology, forming a Federated Edge Learning (FEEL), has spawned a series of next-generation intelligent applications. However, due to the openness of network connections, the FEEL framework generally involves hundreds of remote devices (or clients), resulting in expensive communication costs, which is not friendly to resource-constrained FEEL. To address this issue, we propose a distributed approximate Newton-type algorithm with fast convergence speed to alleviate the problem of FEEL resource (in terms of communication resources) constraints. Specifically, the proposed algorithm is improved based on distributed L-BFGS algorithm and allows each client to approximate the high-cost Hessian matrix by computing the low-cost Fisher matrix in a distributed manner to find a "better" descent direction, thereby speeding up convergence. Second, we prove that the proposed algorithm has linear convergence in strongly convex and non-convex cases and analyze its computational and communication complexity. Similarly, due to the heterogeneity of the connected remote devices, FEEL faces the challenge of heterogeneous data and non-IID (Independent and Identically Distributed) data. To this end, we design a simple but elegant training scheme, namely FedOVA, to solve the heterogeneous statistical challenge brought by heterogeneous data. In this way, FedOVA first decomposes a multi-class classification problem into more straightforward binary classification problems and then combines their respective outputs using ensemble learning. In particular, the scheme can be well integrated with our communication efficient algorithm to serve FEEL. Numerical results verify the effectiveness and superiority of the proposed algorithm. © 2021, CC0.

关键词： Learning systems

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3D Building Generation in Minecraft via Large Language Models

arXiv

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arXiv 2024年

作者： Hu, Shiying Huang, Zengrong Hu, Chengpeng Liu, Jialin Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Recently, procedural content generation has exhibited considerable advancements in the domain of 2D game level generation such as Super Mario Bros. and Sokoban through large language models (LLMs). To further validate the capabilities of LLMs, this paper explores how LLMs contribute to the generation of 3D buildings in a sandbox game, Minecraft. We propose a Text to Building in Minecraft (T2BM) model, which involves refining prompts, decoding interlayer representation and repairing. Facade, indoor scene and functional blocks like doors are supported in the generation. Experiments are conducted to evaluate the completeness and satisfaction of buildings generated via LLMs. It shows that LLMs hold significant potential for 3D building generation. Given appropriate prompts, LLMs can generate correct buildings in Minecraft with complete structures and incorporate specific building blocks such as windows and beds, meeting the specified requirements of human users. Copyright © 2024, The Authors. All rights reserved.

关键词： Buildings

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NeuroSparse: An Unsupervised Framework for Inferring brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Shuqi Zhang, Lijuan Lan, Qing Ma, Qiujie Huang, Huan Miao, Jiaqing Mou, Shiyu Luo, Cheng Yao, Dezhong Tan, Ying Southwest Minzu University Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China Moe Key Lab for Neuroinformation University of Electronic Science and Technology of China Clinical Hospital Chengdu Brain Science Institute Chengdu China University of Malaya Faculty of Engineering Department of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350386226

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse. © 2024 IEEE.

关键词： Unsupervised learning

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3D Building Generation in Minecraft via Large Language Models

3D Building Generation in Minecraft via Large Language Model...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Shiying Hu Zengrong Huang Chengpeng Hu Jialin Liu Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

关键词： Three-dimensional displays Procedural generation Large language models Buildings Refining Windows Decoding

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Driving Fatigue Detection with Fusion of EEG and Forehead EOG

Driving Fatigue Detection with Fusion of EEG and Forehead EO...

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International Joint Conference on Neural Networks

作者： Xue-Qin Huo Wei-Long Zheng Bao-Liang Lu Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center

ISBN: (纸本)9781509006212

In this paper, we fuse EEG and forehead EOG to detect drivers' fatigue level by using discriminative graph regularized extreme learning machine (GELM). Twenty-one healthy subjects including twelve men and nine women participate in our driving simulation experiments. Two fusion strategies are adopted: feature level fusion (FLF) and decision level fusion (DLF). PERCLOS (the percentage of eye closure) is calculated by using the eye movement data recorded by eye tracking glasses as the indicator of drivers' fatigue level. The prediction correlation coefficient and root mean square error (RMSE) between the estimated fatigue level and the real fatigue level are both used to evaluate the performance of single modality and fusion modality. A comparative study on modality performance is conducted between GELM and support vector machine (SVM). The experimental results show that fusion modality can improve the performance of driving fatigue detection with a higher prediction correlation coefficient and a lower RMSE value in comparison with solely using EEG or forehead EOG. And FLF achieves better performance than DLF. GELM is more suitable for driving fatigue detection than SVM. Moreover, feature level fusion with GELM achieves the best performance with the prediction correlation coefficient of 0.8080 and the RMSE value of 0.0712 on average.

关键词： Fatigue Electrooculography Feature extraction Electroencephalography Forehead brain modeling Correlation

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