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arXiv 2024年

作者： Fu, Jiyuan Chen, Zhaoyu Jiang, Kaixun Guo, Haijing Gao, Shuyong Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University China Engineering Research Center of Robotics Ministry of Education Academy for Engineering & Technology Fudan University China

Vision foundation models are increasingly employed in autonomous driving systems due to their advanced capabilities. However, these models are susceptible to adversarial attacks, posing significant risks to the reliability and safety of autonomous vehicles. Adversaries can exploit these vulnerabilities to manipulate the vehicle’s perception of its surroundings, leading to erroneous decisions and potentially catastrophic consequences. To address this challenge, we propose a novel Precision-Guided Adversarial Attack (PG-Attack) framework that combines two techniques: Precision Mask Perturbation Attack (PMP-Attack) and Deceptive Text Patch Attack (DTP-Attack). PMP-Attack precisely targets the attack region to minimize the overall perturbation while maximizing its impact on the target object’s representation in the model’s feature space. DTP-Attack introduces deceptive text patches that disrupt the model’s understanding of the scene, further enhancing the attack’s effectiveness. Our experiments demonstrate that PG-Attack successfully deceives a variety of advanced multi-modal large models, including GPT-4V, Qwen-VL, and imp-V1. Additionally, we won First-Place in the CVPR 2024 Workshop Challenge: Black-box Adversarial Attacks on Vision Foundation Models‡ and codes are available at https://***/fuhaha824/PG-Attack. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Offline Content-based Recommendation System for Wikimedia Commons Contents 16

Offline Content-based Recommendation System for Wikimedia Co...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Maitaigahasse, Ahidjo Ebongue Fendji, Jean Louis Kedieng Atemkeng, Marcellin Department Mathematics and Computer Science Faculty of Science University of Ngaoundere Ngaoundere454 Cameroon Centre for Research Experimentation and Production School of Chemical Engineering and Mineral Industries University of Ngaoundere Ngaoundere454 Cameroon Wallenberg Research Centre Stellenbosch University Stellenbosch South Africa Department of Robotics and Industrial Informatics School of Chemical Engineering and Mineral Industries University of Ngaoundere Ngaoundere454 Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

Recommendation systems are essential for enhancing digital experiences, but their reliance on internet connectivity limits accessibility in regions with limited or no access. This paper presents an offline content-based recommendation system designed to operate without internet dependency by leveraging precomputed Term Frequency-Inverse Document Frequency (TF-IDF) vectors and cosine similarity. To optimize offline performance, we store a locally computed TF-IDF matrix, allowing efficient retrieval of relevant media items through matrix multiplication instead of real-time computation. The system is evaluated using twenty simulated personas representing diverse user interests, demonstrating its ability to generate personalized relevant recommendations. By eliminating the need for online data access, our system makes educational content from Wikimedia Commons accessible in remote areas, schools, and offline learning environments. These findings highlight the potential of offline recommendation systems in bridging the digital divide and providing equitable access to personalized learning resources. © 2025 Elsevier B.V.. All rights reserved.

关键词： Interactive computer systems

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Unified regularity measures for sample-wise learning and generalization

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Visual Intelligence 2024年第1期2卷 1-20页

作者： Zhang, Chi Yuan, Meng Ma, Xiaoning Liu, Yu Lu, Haoang Wang, Le Su, Yuanqi Liu, Yuehu Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an 710049 China National KeyLaboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University Xi’an 710049 China National Engineering Research Center for Visual Information and Applications Xi’an Jiaotong University Xi’an 710049 China School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China

Fundamental machine learning theory shows that different samples contribute unequally to both the learning and testing processes. Recent studies on deep neural networks (DNNs) suggest that such sample differences are rooted in the distribution of intrinsic pattern information, namely sample regularity. Motivated by recent discoveries in network memorization and generalization, we propose a pair of sample regularity measures with a formulation-consistent representation for both processes. Specifically, the cumulative binary training/generalizing loss (CBTL/CBGL), the cumulative number of correct classifications of the training/test sample within the training phase, is proposed to quantify the stability in the memorization-generalization process, while forgetting/mal-generalizing events (ForEvents/MgEvents), i.e., the misclassification of previously learned or generalized samples, are utilized to represent the uncertainty of sample regularity with respect to optimization dynamics. The effectiveness and robustness of the proposed approaches for mini-batch stochastic gradient descent (SGD) optimization are validated through sample-wise analyses. Further training/test sample selection applications show that the proposed measures, which share the unified computing procedure, could benefit both tasks. © The Author(s) 2024.

关键词： Forgetting events Memorization-generalization mechanism Network generalization Sample regularity Sample reweighting

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Microsphere-assisted quantitative phase microscopy:a review

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Light(Advanced Manufacturing) 2024年第1期5卷 131-150页

作者： Vahid Abbasian Tobias Pahl Lucie Hüser Sylvain Lecler Paul Montgomery Peter Lehmann Arash Darafsheh Department of Radiation Oncology Washington University School of Medicine in St.LouisSt.LouisMO 63110USA Imaging Science Program McKelvey School of EngineeringWashington University in St.LouisSt.LouisMO 63130USA Measurement Technology Group Faculty of Electrical Engineering and Computer ScienceUniversity of KasselWilhelmshöher Allee 7134121 KasselGermany ICube Research Institute INSA StrasbourgUniversitéde StrasbourgCNRS67000 StrasbourgFrance

Light microscopes are the most widely used devices in life and material sciences that allow the study of the interaction of light with matter at a resolution better than that of the naked *** microscopes translate the spatial differences in the intensity of the reflected or transmitted light from an object to pixel brightness differences in the digital ***,a phase microscope converts the spatial differences in the phase of the light from or through an object to differences in pixel *** microscopy,a phase-based approach,has found application in various *** interferometry has brought nanometric axial resolution,the lateral resolution in quantitative phase microscopy(QPM)has still remained limited by diffraction,similar to other traditional microscopy *** the resolution has been the subject of intense investigation since the invention of the microscope in the 17th *** the past decade,microsphere-assisted microscopy(MAM)has emerged as a simple and effective approach to enhance the resolution in light *** can be integrated with QPM for 3D label-free imaging with enhanced ***,we review the integration of microspheres with coherence scanning interference and digital holographic microscopies,discussing the associated open questions,challenges,and opportunities.

关键词： Phase microscopy Digital holographic microscopy Microsphere Resolution

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Semantic Level Image Retrieval in Domain Adaptive Semantic Segmentation 11

Semantic Level Image Retrieval in Domain Adaptive Semantic S...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Zhu, Siyu Zhou, Fenfen Li, Kai Wen, Haonan Liu, Minghao Computer School Beijing Information Science and Technology University 102206 No.55 Taihang RoadChangping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University Beijing100027 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing100190 China Yuquan Road Shijingshan District Beijing100049 China

The traditional domain adaptation task is not sufficient in mining inter domain image context. We propose a solution from the perspective of image semantic level retrieval. Image semantic level retrieval is based on the high-level semantics of images, retrieving the similarity or correspondence between images in terms of content and scene between domains. Our work focuses on the meaning, structure, and contextual information expressed by the target image, and retrieves the most relevant image from the source domain to improve the accuracy of domain adaptation. Furthermore, in response to the negative transfer brought about by image retrieval, we will introduce a category level weight adjustment module to further improve the accuracy of semantic level retrieval and weaken the negative impact caused by low precision categories. Finally, we demonstrated the effectiveness of semantic level image retrieval through experiments. © 2024 The Authors.

关键词： Semantic Segmentation

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Category re-balancing based domain adaptive semantic segmentation 11

Category re-balancing based domain adaptive semantic segment...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Zhu, Siyu Zhou, Fenfen Li, Kai Liu, Minghao Wen, Haonan Computer School Beijing Information Science and Technology University 102206 No.55 Taihang RoadChangping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University Beijing100027 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Yuquan Road Shijingshan District Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing100190 China

The semantic segmentation task faces the bottleneck of high manual annotation costs. Domain adaptive learning provides an effective solution through inter domain knowledge transfer. However, existing domain adaptive semantic segmentation tasks face the situation of category asymmetry between scenes when dealing with complex scenes. Existing methods lack handling of class imbalance in the prediction process. In domain adaptive semantic segmentation tasks, class imbalance limits the performance of inter domain transfer. Our work focuses on the impact of category distribution on domain adaptive semantic segmentation tasks in image pairs. Inspired by long tail learning, we divided categories into head, middle, and tail categories, and designed effective strategies to enhance the contribution of tail categories in domain adaptation tasks. We introduce the concept of category re-balancing into domain adaptive image matching, and our method is lightweight and effective, proving its effectiveness on general datasets. © 2024 The Authors.

关键词： Semantic Segmentation

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An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments

arXiv

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arXiv 2024年

作者： Dhrafani, Devansh Liu, Yifei Jong, Andrew Shin, Ukcheol He, Yao Harp, Tyler Hu, Yaoyu Oh, Jean Scherer, Sebastian Carnegie Mellon University College of Engineering Department of Mechanical Engineering PittsburghPA United States Carnegie Mellon University School of Computer Science The Robotics Institute PittsburghPA United States

Robust depth perception in visually-degraded environments is crucial for autonomous aerial systems. Thermal imaging cameras, which capture infrared radiation, are robust to visual degradation. However, due to lack of a large-scale dataset, the use of thermal cameras for unmanned aerial system (UAS) depth perception has remained largely unexplored. This paper presents a stereo thermal depth perception dataset for autonomous aerial perception applications. The dataset consists of stereo thermal images, LiDAR, IMU and ground truth depth maps captured in urban and forest settings under diverse conditions like day, night, rain, and smoke. We benchmark representative stereo depth estimation algorithms, offering insights into their performance in degraded conditions. Models trained on our dataset generalize well to unseen smoky conditions, highlighting the robustness of stereo thermal imaging for depth perception. We aim for this work to enhance robotic perception in disaster scenarios, allowing for exploration and operations in previously unreachable areas. The dataset and source code are available at https://***. © 2024, CC BY.

关键词： Depth perception

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Seven Benefits of Using Series Elastic Actuators in the Design of an Affordable, Simple Controlled, and Functional Prosthetic Hand

Seven Benefits of Using Series Elastic Actuators in the Desi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Erfan Koochakzadeh Alireza Kargar Parsa Sattari Diba Ravanshid Rezvan Nasiri Research Institute for Robotics Artificial Intelligence and Information Science (RAIIS) Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper highlights the benefits of using series elastic actuators (SEA) in designing a cost-efficient, easily controlled, and functional prosthetic hand. The designed 3D-printed hand uses only two motors in an antagonistic configuration, transferring power to the fingers via pulleys, cables, and springs; i.e., the motors are in an SEA configuration with the load/fingers. In the designed underactuated prosthetic hand, the thumb is adjustable for various tasks, and the optimization of pulley diameters ensures synchronized finger movement during hand flexion and extension. Thanks to the SEA configuration of the motors and fingers, simple position control of the motor enables features like hand position control, morphological grasp, force control, impedance control, slippage detection, safe interaction, and efficient grasp. An extensive set of experiments has been conducted to evaluate the designed prosthetic hand’s performance. The experiments confirm the hand’s satisfactory performance while also highlighting the importance of improving the proposed design in different aspects. To attain better position control and morphological grasp, minimizing the cable-body and joint friction is recommended. A higher resolution of the current/torque sensor is needed for the precise force control and slippage detection. Finally, a motor brake system is required to achieve efficient grasping.

关键词： Hands Actuators Pulleys Thumb Position control Motors Synchronization Force control Springs Prosthetic hand

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ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras

arXiv

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arXiv 2024年

作者： Niu, Junkai Zhong, Sheng Lu, Xiuyuan Shen, Shaojie Gallego, Guillermo Zhou, Yi School of Robotics Hunan University Changsha China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong TU Berlin The Science of Intelligence Excellence Cluster The Robotics Institute Germany Einstein Center Digital Future Berlin Germany

Event-based visual odometry is a specific branch of visual Simultaneous Localization and Mapping (SLAM) techniques, which aims at solving tracking and mapping sub-problems (typically in parallel), by exploiting the special working principles of neuromorphic (i.e., event-based) cameras. Due to the motion-dependent nature of event data, explicit data association (i.e., feature matching) under large-baseline viewpoint changes is difficult to establish, making direct methods a more rational choice. However, state-of-the-art direct methods are limited by the high computational complexity of the mapping sub-problem and the degeneracy of camera pose tracking in certain degrees of freedom (DoF) in rotation. In this paper, we tackle these issues by building an event-based stereo visual-inertial odometry system on top of a direct pipeline [1]. Specifically, to speed up the mapping operation, we propose an efficient strategy for sampling contour points according to the local dynamics of events. The mapping performance is also improved in terms of structure completeness and local smoothness by merging the temporal stereo and static stereo results. To circumvent the degeneracy of camera pose tracking in recovering the pitch and yaw components of general 6-DoF motion, we introduce IMU measurements as motion priors via pre-integration. To this end, a compact back-end is proposed for continuously updating the IMU bias and predicting the linear velocity, enabling an accurate motion prediction for camera pose tracking. The resulting system scales well with modern high-resolution event cameras and leads to better global positioning accuracy in large-scale outdoor environments. Extensive evaluations on five publicly available datasets featuring different resolutions and scenarios justify the superior performance of the proposed system against five state-of-the-art methods. Compared to ESVO [1], our new pipeline significantly reduces the camera pose tracking error by 40%–80% and 20%—80%

关键词： Open source software

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