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arXiv 2024年

作者： Zhang, Yuelin Ding, Qingpeng Lei, Long Shan, Jiwei Xie, Wenxuan Zhang, Tianyi Yan, Wanquan Tang, Raymond Shing-Yan Cheng, Shing Shin Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan China Department of Medicine and Therapeutics Institute of Digestive Disease The Chinese University of Hong Kong Hong Kong Department of Mechanical and Automation Engineering T Stone Robotics Institute Shun Hing Institute of Advanced Engineering Multi-Scale Medical Robotics Center Institute of Medical Intelligence and XR The Chinese University of Hong Kong Hong Kong

Ultrasound (US)-guided needle insertion is widely employed in percutaneous interventions. However, providing feedback on the needle tip position via US image presents challenges due to noise, artifacts, and the thin imaging plane of US, which degrades needle features and leads to intermittent tip visibility. In this paper, a Mamba-based US needle tracker MambaXCTrack utilizing structured state space models crosscorrelation (SSMX-Corr) and implicit motion prompt is proposed, which is the first application of Mamba in US needle tracking. The SSMX-Corr enhances cross-correlation by longrange modeling and global searching of distant semantic features between template and search maps, benefiting the tracking under noise and artifacts by implicitly learning potential distant semantic cues. By combining with cross-map interleaved scan (CIS), local pixel-wise interaction with positional inductive bias can also be introduced to SSMX-Corr. The implicit lowlevel motion descriptor is proposed as a non-visual prompt to enhance tracking robustness, addressing the intermittent tip visibility problem. Extensive experiments on a dataset with motorized needle insertion in both phantom and tissue samples demonstrate that the proposed tracker outperforms other stateof- the-art trackers while ablation studies further highlight the effectiveness of each proposed tracking module. © 2024, CC BY.

关键词： Needles

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Self-supervised depth estimation to regularise semantic segmentation in knee arthroscopy 23rd

Self-supervised depth estimation to regularise semantic segm...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Liu, Fengbei Jonmohamadi, Yaqub Maicas, Gabriel Pandey, Ajay K. Carneiro, Gustavo Australian Institute for Machine Learning School of Computer Science University of Adelaide Adelaide Australia School of Electrical Engineering and Robotics Science and Engineering Faculty Queensland University of Technology Brisbane Australia

ISBN: (纸本)9783030597092

Intra-operative automatic semantic segmentation of knee joint structures can assist surgeons during knee arthroscopy in terms of situational awareness. However, due to poor imaging conditions (e.g., low texture, overexposure, etc.), automatic semantic segmentation is a challenging scenario, which justifies the scarce literature on this topic. In this paper, we propose a novel self-supervised monocular depth estimation to regularise the training of the semantic segmentation in knee arthroscopy. To further regularise the depth estimation, we propose the use of clean training images captured by the stereo arthroscope of routine objects (presenting none of the poor imaging conditions and with rich texture information) to pre-train the model. We fine-tune such model to produce both the semantic segmentation and self-supervised monocular depth using stereo arthroscopic images taken from inside the knee. Using a data set containing 3868 arthroscopic images captured during cadaveric knee arthroscopy with semantic segmentation annotations, 2000 stereo image pairs of cadaveric knee arthroscopy, and 2150 stereo image pairs of routine objects, we show that our semantic segmentation regularised by self-supervised depth estimation produces a more accurate segmentation than a state-of-the-art semantic segmentation approach modeled exclusively with semantic segmentation annotation. © Springer Nature Switzerland AG 2020.

关键词： Semantic Segmentation

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An Empirical Study of Security Practices for Microservices Systems

SSRN

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SSRN 2022年

作者： Nasab, Ali Rezaei Shahin, Mojtaba Raviz, Seyed Ali Hoseyni Liang, Peng Mashmool, Amir Lenarduzzi, Valentina School of Computer Science Wuhan University Wuhan430072 China School of Computing Technologies RMIT University Melbourne3000 Australia Department of Computer Science Bioengineering Robotics and System Engineering University of Genova Genova16126 Italy Faculty of Information Technology and Electrical Engineering University of Oulu OuluFI-90014 Finland

Despite the numerous benefits of microservices systems, security has been a critical issue in such systems. Several factors explain this difficulty, including a knowledge gap among microservices practitioners on properly securing a microservices system. To (partially) bridge this gap, we conducted an empirical study to manually analyze 861 security points collected from 10 GitHub open-source microservices systems and Stack Overflow posts concerning security in microservices systems, leading to a catalog of 28 microservices security practices. We then ran a survey with 63 microservices practitioners to evaluate the usefulness of these 28 practices. Our findings demonstrate that the survey respondents affirmed the usefulness of the 28 practices. We believe that the catalog of microservices security practices can serve as a valuable resource for microservices practitioners to more effectively address security issues in microservices systems. It can also inform the research community of the required or less explored areas to develop microservices-specific security practices and tools. © 2022, The Authors. All rights reserved.

关键词： Surveys

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A Clustering-Based Support Vector Classifier for Dynamic Time-Linkage Optimization

A Clustering-Based Support Vector Classifier for Dynamic Tim...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Meng Gao Xiao-Fang Liu Zhi-Hui Zhan Jun Zhang Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China Hanyang University Ansan South Korea Nankai University Tianjin China

Dynamic time-linkage optimization problems (DTPs) bring challenges to existing evolutionary algorithms due to the influence of a current decision in the future. Existing methods usually model the rewards of a current decision in the future for prediction. However, these methods often present low prediction accuracy due to the lack of sufficient training data. In addition, they often require a long computational time. To address these issues, the problem of predicting rewards is converted into a simpler binary classification problem, which evaluates whether a current solution can bring positive or negative influence in the future. This paper proposes a clustering-based support vector classifier for solution evaluation. In the proposed method, the density of the time-linkage property is detected first. Historical data are divided using k-means clustering so as to train a support vector classifier for solution evaluation. Good solutions are selected to generate a final decision solution using a crossover operator. Integrating the clustering-based support vector classifier into particle swarm optimization, a new method named CSVC-PSO is put forward. Multiple instances are constructed using a recent DTP test suite with different types of time-linkage patterns and density. Experimental results demonstrate that the proposed CSVC-PSO outperforms state-of-the-art algorithms on most instances using a shorter time.

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A Dynamic Multi-Objective Optimization Algorithm Based on Detection of Environmental Change Intensity and Ensemble Learning

SSRN

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SSRN 2023年

作者： Wang, Zhenwu Liang, Shangchao Guo, Yinan Han, Mengjie Liu, Fanghan Department of Computer Science and Technology China University of Mining and Technology Beijing100083 China Department of Intelligent Control and Robotics China University of Mining and Technology Beijing100083 China School of Information and Engineering Dalarna University Falun791 88 Sweden

Dynamic multi-objective optimization problems (DMOPs) need to consider all optimal solutions compromised among multiple conflicting objectives, where the objective functions, the constraints or the paramters may change with time. The characteristic of changing over time brings big challenges to solve DMOPs because the algorithms should not only be able to track the optimal solutions, but also quickly detect and respond to changes. In order to enhance the capability of detection and response to environment changes, we propose a dynamic multi-objective optimization (DMOO) algorithm based on the detection of environmental change intensity and ensemble learning (DMOO-DECI&EL). An effective detection method is employed to probe the intensity of environmental change, which then is used to guide the response strategies under the framework of ensemble learning. Experimental results on 14 benchmark funcitons show that the proposed MOCSO-DECI&EL algorithm outperforms other four state-of-the-art dynamic prediction strategies significantly on three classic evaluation crieria. © 2023, The Authors. All rights reserved.

关键词： Optimal systems

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RS-ModCubes: Self-reconfigurable, Scalable, Modular Cubic Robots for Underwater Multitasking

arXiv

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arXiv 2024年

作者： Zheng, Jiaxi Dai, Guangmin He, Botao Mu, Zhaoyang Meng, Zhaochen Zhang, Tianyi Zhi, Weiming Fan, Dixia Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Zhejiang University Zhejiang Hangzhou310027 China Key Laboratory of Coastal Environment and Resources of Zhejiang Province School of Engineering Westlake University Zhejiang Hangzhou310030 China Department of Computer Science University of Maryland College ParkMD20742 United States Institute of Advanced Technology Westlake Institute for Advanced Study Zhejiang Hangzhou310024 China

This paper introduces a reconfigurable underwater robot system, RS-ModCubes, which allows scalable multi-robot configuration. An RS-ModCubes system consists of multiple ModCube modules, that can travel underwater with 6 DoFs and assemble with each other into a larger structure with onboard electromagnets. This system is designed to support diverse underwater applications through modularity and reconfigurability, eliminating the need to customize mechanical designs for a specific task. We present a modeling framework tailored for such reconfigurable robot systems, with hydrodynamics integrated based on Monte Carlo approximation. A model-based feedforward Proportional Derivative (PD) controller serves as the baseline for control. Inspired by dexterous manipulation, we evaluated the robot’s maximum task wrench space and power efficiency, compared against four commercial underwater robots. RS-ModCubes is validated via both real-world experiments and simulations, including individual and multi-module trajectory tracking and hovering docking. We open-source the design and code to facilitate future research: https://***/***/. © 2024, CC BY.

关键词： Machine design

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Offline Equilibrium Finding

arXiv

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arXiv 2022年

作者： Li, Shuxin Wang, Xinrun Zhang, Youzhi Černý, Jakub Li, Pengdeng Chan, Hau An, Bo School of Computer Science and Engineering Nanyang Technological University Singapore Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China University of Nebraska-Lincoln United States

Offline reinforcement learning (offline RL) is an emerging field that has recently begun gaining attention across various application domains due to its ability to learn strategies from earlier collected datasets. Offline RL proved very successful, paving a path to solving previously intractable real-world problems, and we aim to generalize this paradigm to a multiplayer-game setting. To this end, we introduce a problem of offline equilibrium finding (OEF) and construct multiple types of datasets across a wide range of games using several established methods. To solve the OEF problem, we design a model-based framework that can directly apply any online equilibrium finding algorithm to the OEF setting while making minimal changes. The three most prominent contemporary online equilibrium finding algorithms are adapted to the context of OEF, creating three model-based variants: OEF-PSRO and OEF-CFR, which generalize the widely-used algorithms PSRO and Deep CFR to compute Nash equilibria (NEs), and OEF-JPSRO, which generalizes the JPSRO to calculate (Coarse) Correlated equilibria ((C)CEs). We also combine the behavior cloning policy with the model-based policy to further improve the performance and provide a theoretical guarantee of the solution quality. Extensive experimental results demonstrate the superiority of our approach over offline RL algorithms and the importance of using model-based methods for OEF problems. We hope our work will contribute to advancing research in large-scale equilibrium finding. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches

Preferences for Using Canva Apps as a Presentation Media Cre...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Fairuz Iqbal Maulana Dian Lestari Agung Purnomo Rahman Arifuddin Vandha Pradwiyasma Widartha Puput Dani Prasetyo Adi Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga Surabaya 60132 Indonesia Surabaya Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia Information System of Industrial Engineering Faculty Telkom University Bandung Indonesia Research Center for Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia

ISBN: (数字)9798350378573

ISBN: (纸本)9798350378580

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)" and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)" used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations.

关键词： Seminars Analytical models Technology acceptance model Accuracy Numerical analysis Media Mathematical models

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Enhanced Prognostics and Health Management in Automated Driving Systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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