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Neural Rendering and Flow-assisted Unsupervised Multi-view Stereo for Real-time Monocular Tracking and Scene Perception

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IEEE Transactions on automation Science and engineering 2025年

作者： Tong, Kevin W. Cai, Yandong Jie, Yu-Wen Duan, Ya Hou, Yuhong Wu, Edmond Q. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China Shanxi University School of Automation and Software Engineering Shanxi 237016 China Flight Test Technology and Engineering Center of the Chinese Flight Test Establishment Xian710089 China China Civil Aviation Shanghai Aircraft Airworthiness Certification Center Shanghai200335 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

The existing camera tracking and perception methods mainly rely on sparse SLAM, which limits the dense perception ability of the scene and affects the reliability of auxiliary decision-making. Different from this, this work proposes a real-time tracking and unsupervised dense sensing framework. Firstly, the dense depth value of the scene is predicted by unsupervised multi-view stereo to remove the dependence on labeled data. Then, the quality of synthetic pseudo-reference image is quantified according to the predicted depth map and used as a weighted guidance to train the unsupervised model, thus reducing the ambiguity of feature matching in areas such as specular reflection. Moreover, the sparse optical flow of the keyframes is solved by real-time and robust ORB feature matching operator, which assists the high-precision training of unsupervised depth inference model. To increase the prediction accuracy of occluded area, a novel rendering consistency loss via neural radiance fields is designed to constrain the geometric characteristics of object surface. Finally, dense direct image alignment is performed from a global model to improve the tracking robustness, which is incrementally constructed from dense depth prediction. Extensive experiments on synthetic datasets and real datasets validate the effectiveness and practicability of the proposed work, which is an effective supplement to the existing SLAM work. © 2025 IEEE.

关键词： Stereo image processing

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Fair rank aggregation 22

Fair rank aggregation

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Diptarka Chakraborty Syamantak Das Arindam Khan Aditya Subramanian School of Computing National University of Singapore Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi Department of Computer Science and Automation Indian Institute of Science Bengaluru

ISBN: (纸本)9781713871088

Ranking algorithms find extensive usage in diverse areas such as web search, employment, college admission, voting, etc. The related rank aggregation problem deals with combining multiple rankings into a single aggregate ranking. However, algorithms for both these problems might be biased against some individuals or groups due to implicit prejudice or marginalization in the historical data. We study ranking and rank aggregation problems from a fairness or diversity perspective, where the candidates (to be ranked) may belong to different groups and each group should have a fair representation in the final ranking. We allow the designer to set the parameters that define fair representation. These parameters specify the allowed range of the number of candidates from a particular group in the top-k positions of the ranking. Given any ranking, we provide a fast and exact algorithm for finding the closest fair ranking for the Kendall tau metric under strong fairness, i.e., when the final ranking is fair for all values of k. We also provide an exact algorithm for finding the closest fair ranking for the Ulam metric under strong fairness when there are only O(1) number of groups. Our algorithms are simple, fast, and might be extendable to other relevant metrics. We also give a novel meta-algorithm for the general rank aggregation problem under the fairness framework. Surprisingly, this meta-algorithm works for any generalized mean objective (including center and median problems) and any fairness criteria. As a byproduct, we obtain 3- approximation algorithms for both center and median problems, under both Kendall tau and Ulam metrics. Furthermore, using sophisticated techniques we obtain a (3 - ε)-approximation algorithm, for a constant ε > 0, for the Ulam metric under strong fairness.

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Supporting Confidential Workloads in SPIRE

Supporting Confidential Workloads in SPIRE

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Eduardo Falcão Matteus Silva Ariel Luz Andrey Brito Department of Computer Engineering and Automation Federal University of Rio Grande do Norte Natal Brazil Department of Systems and Computing Federal University of Campina Grande Campina Grande Brazil

ISBN: (纸本)9781665463683

The migration of traditional deployment to clouds has driven the need for a more robust security model, the Zero-Trust model. The application of zero-trust principles addresses known security issues such as lateral movement attacks but adds extra identity management complexity. In addition, to cover a broader range of attacks, one must think of strategies to protect data, code, and credentials in such applications. Confidential computing aims to fulfill this goal. Nevertheless, confidential computing is even more complex to implement than Zero-Trust. In this work, we combine the Zero-Trust model with confidential computing by leveraging the SPIFFE standard through its reference implementation (SPIRE), and Intel SGX through the SCONE framework, to seamlessly supply software identities to confidential microservices. Furthermore, we also protected the whole identity-provisioning stack with Intel SGX and assessed the performance overhead. We believe this combination not only improves the security of SPIFFE deployments but also leverages SPIFFE to facilitate the integration between confidential computing components and native applications.

关键词： Computers Cloud computing Codes Computational modeling Microservice architectures Software Complexity theory

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Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems

arXiv

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arXiv 2024年

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Auburn University AuburnAL United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

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Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review

arXiv

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arXiv 2024年

作者： Banos, Oresti Comas-González, Zhoe Medina, Javier Polo-Rodríguez, Aurora Gil, David Peral, Jesús Amador, Sandra Villalonga, Claudia Department of Computer Engineering Automation and Robotics University of Granada Granada Spain Department of Computer Science and Electronics Universidad de la Costa Barranquilla Colombia Department of Computer Science University of Jaén Jaén Spain Department of Computer Technology and Computation University of Alicante Alicante Spain Department of Sotware and Computing Systems University of Alicante Alicante Spain

Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather

关键词： Emotion Recognition

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Brain-inspired artificial intelligence research: A review

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Science China(Technological Sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Robust adaptive control of hypersonic flight vehicle with asymmetric AOA constraint

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Science China(Information Sciences) 2020年第11期63卷 195-207页

作者： Yuyan GUO Bin XU Weixin HAN Shuai LI Yueping WANG Yu ZHANG School of Automation Northwestern Polytechnical University Department of Computing The Hong Kong Polytechnic University Flight Automatic Control Research Institute State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University

This paper investigates the state-constrained controller design of a hypersonic flight vehicle(HFV) based on an asymmetric barrier Lyapunov function(ABLF). The robust adaptive back-stepping controller with integral terms is applied for the HFV longitudinal dynamics. Considering the asymmetric angle of attack(AOA) constraint caused by the unique structure and scramjet, the controller is modified by constructing an ABLF, where the asymmetric constraint on AOA tracking error is introduced. Combined with the constraint on virtual control, the AOA is restricted to a predefined asymmetric interval. The system stability and the AOA constraint are guaranteed via Lyapunov analysis. Simulation results verify that the AOA can be kept in the given asymmetric interval while the altitude reference signal is tracked.

关键词： hypersonic flight vehicle HFV integral back-stepping control adaptive control asymmetric barrier Lyapunov function ABLF angle of attack (AOA) constraint

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Real-time Semantic Segmentation of Solar Photovoltaic Arrays for Autonomous UAV Flights 43

Real-time Semantic Segmentation of Solar Photovoltaic Arrays...

引用

43rd Chinese Control Conference, CCC 2024

作者： Li, Nanyan Chen, Hao Sun, Zhigang Gao, Jing Yi, Dewei Liu, Cunjia Su, Jinya Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China State Grid Jiangsu Integrated Energy Service CO. Ltd Nanjing210096 China Jiangsu Sudian Industry Management CO. Ltd Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9789887581581

This paper considers the real-time semantic segmentation of solar PhotoVoltaic (PV) arrays using down-facing cameras onboard Unmanned Aerial Vehicles (UAVs) to support autonomous navigation for ultra-low altitude industrial inspection. Various semantic segmentation algorithms, including SegNet, UNet, Deeplabv3+, and their respective variants, are compared to identify the most suitable one that strikes a balance between segmentation and real-time performance. Subsequently, each video frame is processed by the selected segmentation network to create a solar array mask. This mask is then optimally fitted to diverse solar array shapes to determine angular and horizontal deviations between the image center and the solar array center-line. The algorithms are validated on a self-constructed dataset of 594 solar PV array UAV images, encompassing varied environmental conditions and solar farm layouts. The top-performing network, lightweight vgg 16bn-Unet, achieves 99.4% overall accuracy and 98.2% Intersection over Union (IoU), while the most balanced network, mobilev3-Deeplabv3+, attains 98.9% accuracy and 96.5% IoU, with a 27.59 fps (using an NVIDIA GeForce MX150 GPU) suitable for real-time UAV flights. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Semantic Segmentation

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Brain Dialogue Interface (BDI): A User-Friendly fMRI Model for Interactive Brain Decoding

arXiv

引用

arXiv 2024年

作者： Huang, Heng Zhao, Lin Wu, Zihao Yu, Xiaowei Zhang, Jing Hu, Xintao Zhu, Dajiang Liu, Tianming The College of Mathematical Medicine Zhejiang Normal University Jinhua China The School of Computing The University of Georgia Athens30602 United States The Department of Computer Science and Engineering The University of Texas at Arlington ArlingtonTX United States The School of Automation Northwestern Polytechnical University Xi’an China

Brain decoding techniques are essential for understanding the neurocognitive system. Although numerous methods have been introduced in this field, accurately aligning complex external stimuli with brain activities remains a formidable challenge. To alleviate alignment difficulties, many studies have simplified their models by employing single-task paradigms and establishing direct links between brain/world through classification strategies. Despite improvements in decoding accuracy, this strategy frequently encounters issues with generality when adapting these models to various task paradigms. To address this issue, this study introduces a user-friendly decoding model that enables dynamic communication with the brain, as opposed to the static decoding approaches utilized by traditional studies. The model functions as a brain simulator, allowing for interactive engagement with the brain and enabling the decoding of a subject’s experiences through dialogue-like queries. Uniquely, our model is trained in a completely unsupervised and task-free manner. Our experiments demonstrate the feasibility and versatility of our proposed method. Notably, our model demonstrates exceptional capabilities in signal compression, successfully representing the entire brain signal of approximately 185,751 voxels with just 32 signals. Furthermore, we show how our model can integrate seamlessly with multimodal models, thus enhancing the potential for controlling brain decoding through textual or image inputs. Copyright © 2024, The Authors. All rights reserved.

关键词： Brain

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

引用

arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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