检索结果-内蒙古大学图书馆

Separability Membrane: 3D Active Contour for Point Cloud Surface Reconstruction

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Pratamasunu, Gulpi Qorik Oktagalu Hao, Guoqing Fukui, Kazuhiro Department of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba305-0085 Japan Department of Integrated Information Technology Aoyama Gakuin University Kanagawa Sagamihara252-5258 Japan

This paper proposes Separability Membrane, a robust 3D active contour for extracting a surface from 3D point cloud object. Our approach defines the surface of a 3D object as the boundary that maximizes the separability of point features, such as intensity, color, or local density, between its inner and outer regions based on Fisher’s ratio. Separability Membrane identifies the exact surface of a 3D object by maximizing class separability while controlling the rigidity of the 3D surface model with an adaptive B-spline surface that adjusts its properties based on the local and global separability. A key advantage of our method is its ability to accurately reconstruct surface boundaries even when they are ambiguous due to noise or outliers, without requiring any training data or conversion to volumetric representation. Evaluations on a synthetic 3D point cloud dataset and the 3DNet dataset demonstrate the membrane’s effectiveness and robustness under diverse conditions. © 2025, CC BY.

关键词： 3D reconstruction

Blockchain-Empowered Lifecycle Management for AI-Generated Content (AIGC) Products in Edge Networks

学校读者我要写书评

暂无评论

TechRxiv

TechRxiv 2023年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Miao, Chunyan Shen, Xuemin Jamalipour, Abbas The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The School of Electrical and Information Engineering University of Sydney Australia

The rapid development of Artificial Intelligence-Generated Content (AIGC) has brought daunting challenges regarding service latency, security, and trustworthiness. Recently, researchers presented the edge AIGC paradigm, effectively optimize the service latency by distributing AIGC services to edge devices. However, AIGC products are still unprotected and vulnerable to tampering and plagiarization. Moreover, as a kind of online non-fungible digital property, the free circulation of AIGC products is hindered by the lack of trustworthiness in open networks. In this article, for the first time, we present a blockchain-empowered framework to manage the lifecycle of edge AIGC products. Specifically, leveraging fraud proof, we first propose a protocol to protect the ownership and copyright of AIGC, called Proof-of-AIGC. Then, we design an incentive mechanism to guarantee the legitimate and timely executions of the funds-AIGC ownership exchanges among anonymous users. Furthermore, we build a multi-weight subjective logic-based reputation scheme, with which AIGC producers can determine which edge service provider is trustworthy and reliable to handle their services. Through numerical results, the superiority of the proposed approach is demonstrated. Last but not least, we discuss important open directions for further research. © 2023, CC BY.

关键词： Blockchain

Predicting the critical behavior of complex dynamic systems via learning the governing mechanisms

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Wang, Xiangrong Lu, Dan Wu, Zongze Xu, Weina Hou, Hongru Hu, Yanqing Moreno, Yamir College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China School of Reliability and Systems Engineering Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Southern University of Science and Technology Shenzhen China University of Zaragoza Zaragoza50018 Spain Department of Theoretical Physics University of Zaragoza Zaragoza50009 Spain

Critical points separate distinct dynamical regimes of complex systems, often delimiting functional or macroscopic phases in which the system operates. However, the long-term prediction of critical regimes and behaviors is challenging given the narrow set of parameters from which they emerge. Here, we propose a framework to learn the rules that govern the dynamic processes of a system. The learned governing rules further refine and guide the representative learning of neural networks from a series of dynamic graphs. This combination enables knowledge-based prediction for the critical behaviors of dynamical networked systems. We evaluate the performance of our framework in predicting two typical critical behaviors in spreading dynamics on various synthetic and real-world networks. Our results show that governing rules can be learned effectively and significantly improve prediction accuracy. Our framework demonstrates a scenario for facilitating the representability of deep neural networks through learning the underlying mechanism, which aims to steer applications for predicting complex behavior that learnable physical rules can drive. © 2025, CC BY.

关键词： Deep neural networks

Navigating A Mobile Robot Using Switching Distributed Sensor Networks

学校读者我要写书评

暂无评论

Navigating A Mobile Robot Using Switching Distributed Sensor...

European Control Conference (ECC)

作者： Xingkang He Ehsan Hashemi Karl H. Johansson Ericsson AB Sweden Department of Mechanical Engineering University of Alberta Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper proposes a method to navigate a mobile robot by estimating its state over a number of distributed sensor networks (DSNs) such that it can successively accomplish a sequence of tasks, i.e., its state enters each targeted set and stays inside no less than the desired time, under a resource-aware, time-efficient, and computation-and communication-constrained setting. We propose a new robot state estimation and navigation architecture, which integrates an event-triggered task-switching feedback controller for the robot and a two-time-scale distributed state estimator for each sensor. With the controller, the robot is able to accomplish a task by following a reference trajectory and switch to the next task when an event-triggered condition is fulfilled. With the estimator, each active sensor is able to estimate the robot state. We provide conditions to ensure that the state estimation error and the trajectory tracking deviation are upper bounded by two time-varying sequences, respectively. Furthermore, we find a sufficient condition for accomplishing a task and provide an upper bound of running time for the task. Numerical simulations of an indoor robot’s localization and navigation are provided to validate the proposed architecture

关键词：

xIDS-EnsembleGuard: An Explainable Ensemble Learning-based Intrusion Detection System

学校读者我要写书评

暂无评论

xIDS-EnsembleGuard: An Explainable Ensemble Learning-based I...

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

作者： Muhammad Adil Mian Ahmad Jan Safayat Bin Hakim Houbing Herbert Song Zhanpeng Jin Deptartment of Computer Science University at Buffalo Buffalo USA College of Computing and Informatics University of Sharjah Sharjah UAE School of Information Systems University of Maryland Baltimore County USA School of Future Technology South China University of Technology Guangdong China And University at Buffalo Buffalo USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the "EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios.

关键词： Deep learning Analytical models Accuracy Intrusion detection Training data Predictive models NSL-KDD Reliability Ensemble learning Long short term memory

Label Correlation Guided Feature Selection for Multi-label Learning 9th

学校读者我要写书评

暂无评论

Label Correlation Guided Feature Selection for Multi-label ...

19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multi-player non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

All in One: Multi-Task Prompting for Graph Neural Networks

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Sun, Xiangguo Cheng, Hong Li, Jia Liu, Bo Guan, Jihong Department of Systems Engineering and Engineering Management Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China School of Computer Science and Engineering Southeast University Purple Mountain Laboratories China Department of Computer Science and Technology Tongji University China

Recently, "pre-training and fine-tuning" has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these multiple tasks. This gap may even cause a "negative transfer" to the specific application, leading to poor results. Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks. In this paper, we propose a novel multi-task prompting method for graph models. Specifically, we first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. In this way, the prompting idea from NLP can be seamlessly introduced to the graph area. Then, to further narrow the gap between various graph tasks and state-of-the-art pre-training strategies, we further study the task space of various graph applications and reformulate downstream problems to the graph-level task. Afterward, we introduce meta-learning to efficiently learn a better initialization for the multi-task prompt of graphs so that our prompting framework can be more reliable and general for different tasks. We conduct extensive experiments, results from which demonstrate the superiority of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Smart Guard Towns: Multi-Drone systems with Deep Learning for Situational Awareness in Urban Management and Security 10

学校读者我要写书评

暂无评论

Smart Guard Towns: Multi-Drone Systems with Deep Learning fo...

10th International Conference on ICT for Smart Society, ICISS 2023

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Sasmoko Indrianti, Yasinta Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Bandung Indonesia School of Electric and Informatic Institut Teknologi Bandung Bandung Indonesia School of Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Bina Nusantara University Faculty of Humanities RIG Group in Educational Technology Primary Teacher Education Department Jakarta Indonesia Universitas Agung Podomoro Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350339543

The design of multi-drone systems with deep learning capabilities in Smart Guard Towns is examined in this research study to improve situational awareness for urban management and security. The study's main objective is to increase real-time understanding and monitoring of the urban environment through the deployment of cutting-edge technologies. The system intends to give comprehensive situational awareness, enabling improved choice-making and resource allocation, by utilizing multiple drones and the power of deep learning algorithms. The research explores the advantages of this method for managing urban environments, boosting security, and addressing the unique difficulties that Smart Guard Towns face. The report also explores possible future advances and ethical issues related to the use of multi-drone systems with deep learning in these situations. With the contribution of this research, lawmakers, urban planners, and technology developers may better apply such systems and progress the development of Smart Guard Towns to create safe and sustainable urban environments. © 2023 IEEE.

关键词： Drones