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Highly efficient recognition of similar objects based on ionic robotic tactile sensors

Science Bulletin 2024年第13期69卷 2089-2098页

作者： Yongkang Kong Guanyin Cheng Mengqin Zhang Yongting Zhao Wujun Meng Xin Tian Bihao Sun Fuping Yang Dapeng Wei Chongqing Key Laboratory of Generic Technology and System of Service Robots Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China

Tactile sensing provides robots the ability of object recognition,fine operation,natural interaction,***,in the actual scenario,robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy,resulting from a lack of high-performance sensors and intelligent recognition *** this paper,a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated,exhibiting excellent signal-to-noise ratio(48 dB),low detection limit(1 Pa),high sensitivity(92.96 kPa^(-1)),fast response time(55 ms),and outstanding stability over 15,000 compression-release ***,a Pressure-Slip Dual-Branch Convolutional Neural Network(PSNet)architecture was proposed to separately extract hardness and texture features and perform feature *** tactile experiments on different kinds of leaves,a recognition rate of 97.16%was achieved,and surpassed that of human hands recognition(72.5%).These researches showed the great potential in a broad application in bionic robots,intelligent prostheses,and precise human–computer interaction.

关键词： Tactile recognition Conformal ionic gel coating Flexible sensors Dual-branch convolutional neural network

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Non-IID federated learning via random exchange of local feature maps for textile IIoT secure computing

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Science China(Information Sciences) 2022年第7期65卷 16-31页

作者： Bo PENG Mingmin CHI Chao LIU School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Zhongshan Pool Net Technology Co.Ltd. Zhongshan Fudan Joint Innovation Center Zhengzhou Zhongke Institute of Integrated Circuit and System Application

With the fast development of artificial intelligence(AI) and industrial Internet of Things(IIoT)technologies, it is challenging to deal with the problems of data privacy protection and secure *** recent years, federated learning(FL) is proposed to attack the challenges of learning shared models collaboratively while protecting security based on the data from cross-domain clients. However, data in the real environment is usually not independent and identically distributed(Non-IID) due to the differences in business, working environments, and data acquisition, and thus classic federated methods suffer from significant performance degradation. In this paper, a novel federated framework is proposed for secure textile fiber identification(Fed TFI) via cross-domain texture representation based on high-definition fabric images. In addition to sharing the gradient of Fed TFI, the local patch of feature maps between crossdomain clients is randomly exchanged to build a richer image texture feature distribution while protecting data security simultaneously for secure computing. Furthermore, a texture embedding layer is designed to provide a joint representation through similarity measure between triplet samples in low-dimensional *** verify the effectiveness of the proposed framework, two textile image datasets, i.e., one public and the other we collected, are utilized to construct four Non-IID scenarios, including label skew, feature skew, and two combined skew scenarios. The experimental results confirm the effectiveness of our model to obtain better detection accuracies than benchmarks in four Non-IID scenarios by keeping data privacy for secure computing in fabric IIoT.

关键词： federated learning secure computing industrial Internet of Things (IIoT) machine vision texture encoding image classification Non-IID

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Continual Learning with a Memory of Non-similar Samples 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Min, Qilang He, Juanjuan Yang, Liuyan Fu, Yue College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

The replay method is an effective strategy to address the problem of catastrophic forgetting, a key challenge for continuous learning. However, the sample sets obtained by replay-based methods generally suffer from local data information deficiencies. This problem leads to an imbalance in the plasticity-stability of the model on older tasks. This paper proposes a novel method, called non-similar sample storage (NSS). Non-similar refers to the Euclidean distance for feature vectors of different samples being far. NSS extracts the feature vectors of the samples and then calculates the similarity of the feature vectors for each sample after the current task training. Samples that contribute less to the model classification effect among similar samples are iteratively deleted, and the subset of low-similarity samples is retained. Moreover, NSS reserves 30% of the storage space for saving samples near the center of the sample set. Low-similarity samples stored by NSS get larger losses during the replay process, leading to lower training effectiveness of the current task. This paper introduces a knowledge distillation strategy to solve this problem. A variable parameter was used to balance the classification loss of the new task with the distillation loss of the old task (NSS-D). Experimental results in CIFAR10 and imbalanced CIFAR10 show that NSS maximizes the data’s global information and can retain the model’s ability to recognize old tasks better. Compared with classical algorithms, NSS-D performs better on CIFAR100 (48.8%) and ImageNet-200 (36.7%). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Distillation

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Video matting based on local-global features fusion 4

Video matting based on local-global features fusion

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Dong, Niuniu Liang, Yihui Zou, Kun Li, Wensheng Feng, Fujian School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Video matting aims at accurately separating foreground from videos. Recent video matting researches pursue to eliminate auxiliary inputs. However, due to the limited ability of extracting global correlation features, these methods suffer from performance degradation when dealing with complex scenes or natural background videos. To address this challenge, we propose a video matting method called Video Matting Based on Local-Global Features Fusion (VMBLGFF) which can extract both comprehensive global correlation features and local subtle features. VMBLGFF contains two closely connected networks: a transformer network that utilizes window and global attention mechanisms to obtain global correlation features within and cross windows, and a fusion network that integrates local subtle features into the global correlation features to supplement the local detail information which may be overlooked by the attention mechanisms. VMBLGFF alleviates the issue of limiting global correlation features and has been benchmarked on both synthetic and real datasets, and the results demonstrate that VMBLGFF improves the quality of video matting and exhibits good generalization performance. © 2024 SPIE.

关键词： computer vision

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Trimap generation with background for natural image matting 3

Trimap generation with background for natural image matting

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3rd International Conference on Optics and Machine Vision, ICOMV 2024

作者： Fu, Qian Liang, Yihui Kun, Zou Feng, Fujian Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510680319

Image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background Image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TP-BG leverages a ViT backbone for feature extraction and employs the Image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics. © 2024 SPIE.

关键词： Costs

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Dual Consistency Learning for Semi-Supervised Airway Segmentation 2

Dual Consistency Learning for Semi-Supervised Airway Segment...

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2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Li, Mingshuang Yuan, Yunyi Wang, Qiong Qian, Yinling Zhu, Lei Southern University of Science and Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen China Guangzhou China

ISBN: (纸本)9798350386639

Airway segmentation serves as an essential foundational process for both the diagnosis of lung conditions and the navigation of surgical interventions. Although numerous attempts have been proposed to address airway segmentation, the application of semi-supervised learning in this task has not yet been extensively investigated. In this study, we introduce an innovative semi-supervised learning framework that incorporates dual consistency learning during the re-training. Dual consistency learning exploits image-level and feature-level perturbations simultaneously to fully and efficiently extract additional information from unlabeled data. Specifically, image-level consistency learning employs a novel frequency domain data augmentation scheme to enforce topological feature capturing, and feature-level consistency learning could explore a broader perturbation space, thus realizing discriminative representation extraction from feature space. We carry out comprehensive experiments across various public datasets to substantiate the efficacy of our proposed methodology. The results illustrate that our framework attains remarkable performance levels, surpassing the current state-of-the-art approaches. © 2024 IEEE.

关键词： Self-supervised learning

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On the Security of Smart Home systems:A Survey

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Journal of computer Science & technology 2023年第2期38卷 228-247页

作者：袁斌万俊吴宇晗邹德清金海 School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Distributed System Security Huazhong University of Science and Technology Wuhan 430074China Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan 430074China National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Lab Huazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.

关键词： IoT(Internet of Things) smart home IoT security smart home security

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Data quantity governance for machine learning in materials science

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National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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AISLE: Self-supervised Representation Learning for the Investigation of Advanced Persistent Threat 7

AISLE: Self-supervised Representation Learning for the Inves...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Jia, Zian Wang, Xiaosu Xiong, Yun Zhang, Yao Zhao, JinJing Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China National Key Laboratory of Science and Technology on Information System Security China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Advanced Persistent Threats (APT) are difficult to detect and defend due to their high variability and concealment. Current APT detection and investigation approaches suffer from two major problems. First, most recent models heavily rely on heuristic rules derived from a large amount of expensive prior knowledge, which in turn prevents the models from generalizing to new types of APT attacks. Second, the development of existing fully supervised models is limited by the scarcity of APT attack data. In this paper, we first construct the provenance graph by introducing logical entities to reduce the dependency on prior knowledge. Then we propose a novel self-supervised representation-learning based model, AISLE, for the investigation of the APT attack, which consists of a graph structure encoder (GSE) and an entity interaction pattern encoder (IPE). The GSE employs a graph attention auto-encoder to effectively compress the structural information of the graph;and the IPE employs a LSTM auto-encoder to extract the interaction patterns of entities from their interaction history. The two entity embeddings calculated respectively by the two auto-encoders are concatenated to form the final representation of the entity, which is evaluated by the attack investigation module to identify attack entities. Our model is evaluated on ten real-world APT attacks in a realistic virtual environment. The results show that our model can achieve superior generalization ability and consistently outperform the latest state-of-the-art APT attack investigation approaches. © 2022 IEEE.

关键词： Graph neural networks

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Teleoperation Based on Hybrid Mapping Method of Human-Robot Heterogeneous Workspace 18th

Teleoperation Based on Hybrid Mapping Method of Human-Robo...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： Ye, Shuangxiu Zhang, Ning Zhao, Yongjia State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China Jiangxi Research Institute Beihang University Jiangxi330000 China

ISBN: (纸本)9789811962028

In this paper, we designed a robot teleoperation system of human-robot heterogeneous workspace which is based on a hybrid mapping method, a combination of terminal pose mapping and joint angle mapping. By using this hybrid mapping method, the problem of choosing the solution that best fits the expectation of the inverse kinematics of the robot is solved, which is difficult to be realized by the simple terminal position mapping. This method uses the RGB-D camera to identify the operator’s joint position information which has little physical constraints on the human body. We also realize the operator’s gesture recognition based on motion-sensing technology which can control the gripper via opening or closing the palm. Some experiments are designed to verify the fine effect of hybrid mapping method and the feasibility of teleoperation system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mapping

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