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Augmented reality-based visual-haptic modeling for thoracoscopic surgery training systems

virtual reality & Intelligent Hardware 2021年第4期3卷 274-286页

作者： Yonghang TAI Junsheng SHI Junjun PAN Aimin HAO Victor CHANG Yunnan Key Lab Optoelectronic Information Technology Yunnan Normal UniversityKunming 650500China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China School of Computing Engineering and Digital TechnologiesTeesside UniversityMiddlesbrough TS13BXUK

Background Compared with traditional thoracotomy,video-assisted thoracoscopic surgery(VATS)has less minor trauma,faster recovery,higher patient compliance,but higher requirements for *** surgery training simulation systems are important and have been widely used in Europe and *** reality(AR)in surgical training simulation systems significantly improve the training effect of virtual surgical training,although AR technology is still in its initial *** reality has gained increased attention in technology-driven modern medicine but has yet to be used in everyday *** This study proposed an immersive AR lobectomy within a thoracoscope surgery training system,using visual and haptic modeling to study the potential benefits of this critical *** content included immersive AR visual rendering,based on the cluster-based extended position-based dynamics algorithm of soft tissue physical ***,we designed an AR haptic rendering systems,whose model architecture consisted of multi-touch interaction points,including kinesthetic and pressure-sensitive ***,based on the above theoretical research,we developed an AR interactive VATS surgical training *** Twenty-four volunteers were recruited from the First People's Hospital of Yunnan Province to evaluate the VATS training ***,content,and construct validation methods were used to assess the tactile sense,visual sense,scene authenticity,and simulator *** The results of our construction validation demonstrate that the simulator is useful in improving novice and surgical skills that can be retained after a certain period of *** video-assisted thoracoscopic system based on AR developed in this study is effective and can be used as a training device to assist in the development of thoracoscopic skills for novices.

关键词： Augmented reality VATS Surgery training XPBD

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A Survey on Federated Learning Poisoning Attacks and Defenses

arXiv

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arXiv 2023年

作者： Liang, Junchuan Wang, Rong Feng, Chaosheng Chang, Chin-Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized data collaboratively without explicitly aggregating the data. Due to its ability to break data silos, federated learning has received increasing attention in many fields, including finance, healthcare, and education. However, the invisibility of clients’ training data and the local training process result in some security issues. Recently, many works have been proposed to research the security attacks and defenses in federated learning, but there has been no special survey on poisoning attacks on federated learning and the corresponding defenses. In this paper, we investigate the most advanced schemes of federated learning poisoning attacks and defenses and point out the future directions in these areas. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

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StegoFL: Using Steganography and Federated Learning to Transmit Malware

StegoFL: Using Steganography and Federated Learning to Trans...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Rong Wang Junchuan Liang Haiting Jiang Chaosheng Feng Chin-Chen Chang College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Federated learning (FL) has gained increasing popularity in today’s privacy-focused world due to its ability to break data silos. Although federated learning can keep all the training data private at each site, it results in clients not being effectively monitored, which may pose security risks to the learning process. Previous studies mainly focus on backdoor and poisoning attacks against federated learning. In this paper, we propose a new stegomalware attack in FL, where an attacker, disguised as a benign client, hides malware into his local model and distributes the malware to other clients through the FL process. Conducting such an attack faces two challenges. First, embedding data into a local model may degrade its model performance, making it easier for a server in FL to detect this anomaly. Second, the local model with embedded malware is recalculated after model aggregation, which unavoidably alters the malware’s carrier and hinders the success of malware extraction. To address these challenges, we propose a method called StegoFL to incorporate steganography into federated learning to transmit malware. Specifically, we split the malware to be transmitted into segments and randomly select a few model parameters as carriers. Each malware segment is concealed within these carriers over several training rounds. We also propose a simple value-mapping method to extract the embedded data by comparing the aggregated carrier values with a threshold. Experimental results demonstrate that StegoFL can circumvent server-side detection mechanisms, i.e., accuracy tests and parametric distribution comparisons. In addition, it increases transmission capacity by at least 50 times compared to state-of-the-art covert communication methods in federated learning.

关键词： Training Steganography Federated learning Training data Malware Data models Security Servers Monitoring Faces

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An extended sequential quadratic method with extrapolation

arXiv

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arXiv 2023年

作者： Zhang, Yongle Pong, Ting Kei Xu, Shiqi Department of Mathematics Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu China Department of Applied Mathematics the Hong Kong Polytechnic University Hong Kong Hong Kong Department of Mathematics Sichuan Normal University Chengdu China

We revisit and adapt the extended sequential quadratic method (ESQM) in [3] for solving a class of difference-of-convex optimization problems whose constraints are defined as the intersection of level sets of Lipschitz differentiable functions and a simple compact convex set. Particularly, for this class of problems, we develop a variant of ESQM, called ESQM with extrapolation (ESQMe), which incorporates Nesterov's extrapolation techniques for empirical acceleration. Under standard constraint qualifications, we show that the sequence generated by ESQMe clusters at a critical point if the extrapolation parameters are uniformly bounded above by a certain threshold. Convergence of the whole sequence and the convergence rate are established by assuming Kurdyka- Lojasiewicz (KL) property of a suitable potential function and imposing additional differentiability assumptions on the objective and constraint functions. In addition, when the objective and constraint functions are all convex, we show that linear convergence can be established if a certain exact penalty function is known to be a KL function with exponent 1/2;we also discuss how the KL exponent of such an exact penalty function can be deduced from that of the original extended objective (i.e., sum of the objective and the indicator function of the constraint set). Finally, we perform numerical experiments to demonstrate the empirical acceleration of ESQMe over a basic version of ESQM, and illustrate its effectiveness by comparing with the natural competing algorithm SCPls from [35]. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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Unraveling the Neural Dynamics of Mathematical Interference in English Reading:A Novel Approach with Deep Learning and Fnirs Data

SSRN

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SSRN 2024年

作者： Liang, Zhijie Wang, Ling Sun, Bo Wang, Daifa Yang, Juan School of Computer Science Sichuan Normal University Chengdu610066 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610066 China College of Foreign Languages Sichuan Normal University Chengdu610066 China School of Biological Science and Medical Engineering Beihang University Beijing100083 China

English has emerged as the predominant global language, making it a central focus in educational field seeking to integrate English with other disciplines for improved learning efficiency. Despite numerous studies demonstrating a connection between English learning performance and mathematical logic skills, current explanations and theoretical frameworks predominantly rely on behavioral analysis. This highlights the need for neuroscientific evidence to elucidate the neural mechanisms supporting this correlation, especially in terms of internal neural processes. To bridge this gap, the present study employs functional near-infrared spectroscopy (fNIRS) to construct a novel dataset on mathematical interference in English acquisition. Utilizing this dataset, a novel deep learning model named AC-LSTM is proposed, amalgamating Transformer and LSTM architectures to identify residual mathematical cognition during the English learning process. The AC-LSTM model achieves an exceptional accuracy rate of 99.8%, surpassing other machine learning and deep learning models. Moreover, a multi-class classification experiment is conducted to discern algebra, geometry, and quantitative reasoning interference, with the AC-LSTM model achieving the highest accuracy of 75.9% in this classification task. Furthermore, crucial brain channels for interference detection are pinpointed through grid search, and alterations in vital brain regions (R-Broca and L-Broca) are unveiled via association rule analysis. By integrating fNIRS, deep learning, and data mining techniques, this study delves into cognitive interference in English learning, providing valuable insights for educational neuroscience and data mining research. © 2024, The Authors. All rights reserved.

关键词： Data mining

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Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation

arXiv

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arXiv 2025年

作者： Zheng, Hong Chen, Yucheng Mu, Nan Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Department of Cardiology West China Hospital Sichuan University Chengdu610041 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610066 China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu610066 China

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Efficient Monaural Speech Enhancement using Spectrum Attention Fusion

arXiv

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arXiv 2023年

作者： Long, Jinyu Gū, Jetic Bai, Binhao Yang, Zhibo Wei, Ping Li, Junli School of Computing Science Sichuan Normal University Chengdu610066 China School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu610068 China Sichuan Key Laboratory of Translational Medicine of Traditional Chinese Medicine Chengdu610041 China

Speech enhancement is a demanding task in automated speech processing pipelines, focusing on separating clean speech from noisy channels. Transformer based models have recently bested RNN and CNN models in speech enhancement, however at the same time they are much more computationally expensive and require much more high quality training data, which is always hard to come by. In this paper, we present an improvement for speech enhancement models that maintains the expressiveness of self-attention while significantly reducing model complexity, which we have termed Spectrum Attention Fusion. We carefully construct a convolutional module to replace several self-attention layers in a speech Transformer, allowing the model to more efficiently fuse spectral features. Our proposed model is able to achieve comparable or better results against SOTA models but with significantly smaller parameters (0.58M) on the Voice Bank + DEMAND dataset. © 2023, CC BY.

关键词： Speech enhancement

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An Optimization Method of Primer Design Based on Attention-BiLSTM

An Optimization Method of Primer Design Based on Attention-B...

引用

Robotics, Artificial Intelligence and Intelligent Control (RAIIC), International Conference on

作者： Binhao Bai Jinyu Long Zhibo Yang Junli Li Ping Wei College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Sichuan Key Laboratory of Translational Medicine of Traditional Chinese Medicine Sichuan Academy of Traditional Chinese Medicine Sichuan Center of Translational Medicine Chengdu China

In this paper, we propose a method to predict the success of primer amplification based on the relationship existing between the sequence of primer and template, which can optimize the primer design and select the primer with better amplification from the candidate primer set. The double-stranded structure between primer and template nucleotide sequences is represented here by a number of words, each consisting of five characters that form sentences, as the dataset for the experiment, which is learned using an attention-based mechanism of bidirectional long short-term memory neural network model (Attention-BiLSTM), and then predicts primer amplification. The model predicted the results of polymerase chain reaction (PCR) involving specific primers and specific DNA templates with 82% accuracy, an improvement of about 2% over the performance of the LSTM with more stable value. These results show that the model can be used to effectively predict the results of PCR. This is the first paper to optimize primer design by screening the candidate primer set with a neural network model.

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Analysis of an k-out-of-n:G system with redundant dependency and repair equipment procurement

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Journal of Control and Decision 2021年第4期8卷 403-414页

作者： Wenqing Wu Kai Zuo Yinghui Tang Yuanyuan Zhang School of Science Southwest University of Science and TechnologyMianyangPeople’s Republic of China School of Mathematical Sciences Sichuan Normal UniversityChengduPeople’s Republic of China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal UniversityChengduPeople’s Republic of China School of Mathematics Chengdu Normal UniversityChengdu 611130People’s Republic of China

This paper studies an k-out-of-n:G system with redundant dependency and repair equipment procurement lead time where the operating times and repair times of components follow exponential distributions and phase-type distributions,*** one component breaks down,it is repaired by a repair *** repair equipment may fail during the repair period and the following repair is not‘as good as new’.After a number of repairs,it is replaced by a new *** new spare repair equipment for replacement is only available by an order,and the procurement lead time for delivering follows a phase-type ***,in the multi-component system,the redundant dependency is taken into *** the matrix-analytical method,the system availability,the rate of occurrence of failures of the system,the expected number of broken components,the availability and the rate of occurrence of failures of the repair equipment are ***,numerical examples are given to show these theoretical results.

关键词： k-out-of-n:G system redundant dependency procurement lead time matrix-analytical method reliability measures

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Model Ownership Protection for Healthcare Consumer Electronics in Federated Edge Learning

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IEEE Transactions on Consumer Electronics 2024年

作者： Wang, Rong Liang, Junchuan Feng, Chaosheng Chakraborty, Chinmay Alqahtani, Fayez Yu, Keping Sichuan Normal University College of Computer Science Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu610068 China Birla Institute of Technology Mesra India King Saud University College of Computer and Information Sciences Software Engineering Department Riyadh12372 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan

With the rising demand for intelligent services and privacy protection in consumer artificial intelligence (AI), federated edge learning has emerged as a beacon for privacy-preserving distributed machine learning. This non-centralized approach offers distinct data privacy advantages, making it especially suitable for applications with strict privacy policies, such as consumer-centric healthcare systems. However, consumer electronics/devices in federated edge learning might unlawfully violate model ownership by duplicating or distributing models. Another concern is that a central server, if compromised, can become a conduit for model leakage, whether for profit motives or due to external intrusions, thus exacerbating the model ownership violation. To our knowledge, few works have focused on model ownership protection for federated edge learning in the consumer-centric healthcare domain. In this paper, we introduce a federated model ownership protection (FedMOP) approach. FedMOP is a model ownership safeguard and a formidable fortress built upon two core innovations. First, FedMOP can swiftly embed and replace watermarks during local training, allowing for the accurate identification and subsequent flagging of deceitful consumer devices. Second, FedMOP ensures that the server only accesses encrypted versions of the federated model, keeping the model a secret. Extensive evaluations demonstrate the robustness and effectiveness of FedMOP. We hope that FedMOP can light the way for surmounting similar ethical challenges in the broader realm of consumer-edge AI. © 1975-2011 IEEE.

关键词： ethical AI Federated edge learning healthcare consumer electronicss model ownership protection

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