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检索条件"任意字段=12th International Conference on Information Systems and Computing Technology"

共 47326 条记录，以下是111-120 订阅

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Image-Based 3D Gaussian Rendering: A Hybrid Approach for Scene Visualization 12

Image-Based 3D Gaussian Rendering: A Hybrid Approach for Sce...

引用

12th international conference on information systems and computing technology

作者： Xuan, Jianfeng Huang, Yanling Guo, Yanan Jia, Bo Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Recently, substantial advancements have occurred in the field of 3D rendering, driven by the success of the 3D Gaussian splatting method. However, current 3D Gaussian models rely on structure-from-motion (SfM) feature points, which face limitations in rendering quality due to the restricted use of image feature information and their dependence on initial training viewpoints. To address these limitations, we propose a novel hybrid feature representation method that combines point cloud-based features with image semantic *** core of this method is to utilize a lightweight convolutional neural network to extract fine-grained semantic features from multi-view images and to employ a novel composite distance metric to precisely align these features with point cloud data. this approach ensures accurate alignment of features and enhances the richness of 3D scene representations. the proposed method significantly improves rendering quality and generalization ability, making it more robust in handling diverse and complex viewing scenarios. Experimental results validate the effectiveness of this method across a wide range of 3D rendering applications, demonstrating its exceptional generalization capabilities under complex viewing conditions.

关键词： hybrid feature representation 3D Gaussian image-based rendering

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Spiking Swin Transformer for UAV Object Detection Based on Event Cameras 12

Spiking Swin Transformer for UAV Object Detection Based on E...

引用

12th international conference on information systems and computing technology

作者： Zhang, Heng Chen, Nuo Li, Miao An, Wei Natl Univ Def Technol Coll Elect Sci & Technol Changsha Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Unmanned aerial vehicle (UAV) detection tasks have always been a hot issue in the field of computer vision. Event cameras focus solely on the motion within a scene, featuring high dynamic range and low latency. For small UAVs with high speeds, event cameras can acutely perceive their motion information in various complex scenarios. therefore, in this work, we utilize event cameras to capture event stream motion data of UAVs with low background redundancy. Simultaneously, we combine the Swin Transformer which pays attention to local spatial correlation with the Spiking Neural Network (SNN) which is sensitive to temporal features, and propose an energy-efficient backbone network for spatiotemporal feature extraction of small targets-Spiking Swin Transformer (SST). Utilizing the latest advancements in spike backpropagation research-surrogate gradient learning and Spikingjelly framework, based on Feature Pyramid Networks (FPN) and Single Shot MultiBox Detector (SSD), we propose a motion small target detection algorithm that can be directly trained on event stream datasets with SST as the backbone network. In this paper, we conducted experiments on our self-made event stream UAV object detection dataset and established a new state-of-the-art SNNs detection result.

关键词： Spiking Neural Networks Swin Transformer event cameras Feature Pyramid Networks Single Shot MultiBox Detector UAV object detection

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Data Augmentation and Neural Network for American Football Formation Recognition 12

Data Augmentation and Neural Network for American Football F...

引用

12th international conference on information systems and computing technology

作者： Hong, Audrey Tonic, Shad Zhang, Dong Lee, Dah-Jye Brigham Young Univ Dept Elect & Comp Engn Provo UT 84602 USA Sun Yat Sen Univ Dept Elect & Informat Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Data analytics converts raw data into actionable insights. Sports analytics is pivotal in developing strategies and preparing players to achieve their best performance. It is especially important for team sports. However, gathering game data from previous game video recordings for sport analytics is time-consuming and tedious for coaches and their assistants. It could take them hours or days to sift through video clips to gather relevant information to prepare for their next game. this manual process called "tagging" in American football is essential to game planning for coaches. Automating this manual process can provide great benefits for teams in reducing the effort for data gathering and increasing the time for practice. Particularly, being able to automatically classify the offensive play formation for each play is crucial for developing game plans. this paper reports on our feasibility study on using machine learning techniques to automate the manual process. We studied offensive play formations extensively and devised an data augmentation strategy to generate realistic data for training and testing of our neural network. Using these synthetic data for training and testing, our neural network achieves an accuracy of 100% in recognizing 25 commonly used play formations.

关键词： Machine Learning Neural Network American Football Data Augmentation Formation Analysis

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SCSE-UNet: A Spatial Channel Squeeze-and-Excitation UNet for Medical Image Segmentation 12

SCSE-UNet: A Spatial Channel Squeeze-and-Excitation UNet for...

引用

12th international conference on information systems and computing technology

作者： Liu, Xiuli Wu, Xiangqiong Chen, Yang Wang, Peng Hunan First Normal Univ Sch Comp Sci Changsha Peoples R China Hunan First Normal Univ Sch Elect Informat Changsha Peoples R China

ISBN: (纸本)9798350379860;9798350379877

As an effective convolutional neural network design, U-Net has shown notable performance in challenges involving the segmentation of medical images. Traditional U-Net models are still limited in their ability to handle complex image features and long-distance relationships, despite the variety of application scenarios and technological advancements. We propose a novel model that incorporates the Spatial Channel Squeeze-and-Excitation (SCSE) module to enhance the model's performance on medical segmentation tasks to further increase the segmentation accuracy and reduce the number of parameters. the proposed model improves the network's capacity to learn important features, especially those about tumor boundaries and regions of interest, by fusing deep convolutional neural networks with SCSE modules. the proposed model achieves higher segmentation accuracy with fewer parameters, according to experimental data, which proves that this approach can provide better segmentation performance and lower processing costs than the original segmentation techniques.

关键词： U-Net medical image segmentation attention mechanism

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A Domain Generalization Approach Based on Cost-sensitive Learning for Gaze Estimation 12

A Domain Generalization Approach Based on Cost-sensitive Lea...

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12th international conference on information systems and computing technology

作者： Yang, Guobo Zhang, Dong Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Appearance-based gaze estimation methods regress gaze directions from face images. Deep learning has become the dominant approach to appearance-based gaze estimation and has achieved promising performance within individual datasets. However, gaze estimation methods based on deep learning still perform poorly in cross-domain scenarios. In this paper, we propose a cost-sensitive learning approach for gaze estimation domain generalization. As is observed during cross-domain testing, estimated gazes with large deviations tend to cluster around regions with dense labels in the source domain. Addressing this issue has the potential to improve the generalization ability of the gaze estimation model. To achieve this, we assign weights to the losses generated by each training sample. these weights are determined by two factors: the distribution of the training samples and the rarity of the gaze direction. Experiment show that our method, without any additional network parameters, achieves state-of-the-art performance in gaze estimation domain generalization tasks and reduces overall deviation in cross-domain testing. It is even competitive compared to unsupervised domain adaptation methods for gaze estimation.

关键词： computer vision gaze estimation domain generalization imbalanced regression cost-sensitive learning

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information Sharing Scheme Using AoI for DTN-based Evacuation systems and Its Evaluation 12

Information Sharing Scheme Using AoI for DTN-based Evacuatio...

引用

12th international Symposium on computing and Networking Workshops

作者： Obayashi, Shoya Ohta, Tomoyuki Hiroshima City Univ Hiroshima Japan

ISBN: (纸本)9798331505356;9798331505349

In recent years, Japan has frequently experienced natural disasters such as earthquakes and typhoons, necessitating the rapid and accurate acquisition of evacuation support information. However, during such disasters, communication infrastructure may be damaged, making information sharing difficult. To address this issue, Delay/Disruption Tolerant Networking (DTN) technology has garnered attention. In this study, we propose a new information sharing scheme for evacuation support systems using DTN, leveraging the Age of information (AoI) to assess the freshness of information. the proposed scheme discards information that exceeds a certain AoI threshold, ensuring that evacuees can act based on the most recent and accurate information. this paper evaluates the effectiveness of the proposed scheme through simulations using real geographical information. Furthermore, we discuss the impact of the proposed scheme on evacuation time, the number of evacuees, and the average evacuation time.

关键词： DTN AoI Evacuation System Simulation

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Trusted Charity System Based on Blockchain Access Control 12

Trusted Charity System Based on Blockchain Access Control

引用

12th international conference of information and Communication technology, ICTech 2023

作者： Yu, Jingang Chen, Weifan Shenyang Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350332919

At present, there are widespread problems in traditional centralized charity systems such as unclear information about the flow of funds, centralized data easily tampered with, and data security not guaranteed, so public charity systems need a trusted and decentralized solution. Blockchain has the property of self-trust because of its tamper-proof and traceable nature. It enables mutual trust among stakeholders without the need for third-party proof. the adoption of blockchain technology can solve the trust problem in charity systems and improve the transparency and trust of charitable organizations. To this end, this paper proposes a trusted charity system based on blockchain access control by combining the traceable and tamper-evident characteristics of blockchain with the charity field. © 2023 IEEE.

关键词： Smart contract

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Res-GC Net: A Road Extraction Method with Multi-Scale Simplified Attention Mechanism for Remote Sensing Images 12

Res-GC Net: A Road Extraction Method with Multi-Scale Simpli...

引用

12th international conference on information systems and computing technology

作者： Wu, Haomeng Wang, Yi Shen, Yulin Wu, Di Zhang, Boqun Liu, Ze Harbin Space Star Data Syst Technol Co Ltd Syst Demonstrat Ctr Harbin Heilongjiang Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Harbin Space Star Data Syst Technol Co Ltd Urban Intelligent Comp Dept Harben Peoples R China

ISBN: (纸本)9798350379860;9798350379877

In the field of remote sensing image analysis, the accurate extraction of road networks is a critical task, particularly in scenarios with complex backgrounds and narrow shapes. To address this challenge, this study introduces a multi-scale GC Block network module that significantly enhances the effective location information in feature maps by simplifying the computation of spatial and channel attention. the module proposed in this paper improves the adaptability to complex road network backgrounds and reduces the computational load of parameters, thereby accelerating the reasoning speed. the network module is designed with flexibility, maintaining consistent input and output dimensions, and supports plug-and-play functionality. Furthermore, an automatically updating head structure has been designed, which effectively suppresses invalid location information in the mask matrix through multiple iterative processes, achieving high boundary segmentation. In experimental evaluations on the CHN6-CUG dataset, the Res-GC Net was compared with traditional semantic segmentation methods, with the key metric of road Intersection over Union (IOU) showing a 5.3% improvement over the best baseline result. the results demonstrate that our network exhibits a significant advantage in capturing road details and recognizing roads that are partially obscured.

关键词： Roads Neural networks Feature extraction Remote sensing Semantics Image segmentation

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Forecasting Server Resource Utilization: An Experimental Study with Long Short-Term Memory Model 12

Forecasting Server Resource Utilization: An Experimental Stu...

引用

12th international conference on information and Communication technology, ICoICT 2024

作者： Wong, Jun-Jie Eng Ong, Lee-Y Leow, Meng-Chew Lee, Ting-Wei Multimedia University Faculty of Information Science and Technology Melaka Malaysia

ISBN: (纸本)9798350363432

Cloud computing has indeed been a transformative milestone in the computing landscape, reshaping traditional practices. However, the expansion of cloud computing faces its hurdles, with forecasting computing resource utilization emerging as the top challenge. this research addresses the crucial need for enhanced monitoring and detection of server crashes in cloud computing environments. Leveraging deep learning techniques, the proposed Adaptive Transfer Learning model with Long Short-Term Memory offers a promising solution to better forecast server crashes. the study investigates the complexity and challenges of multivariate time series forecasting, particularly in datasets containing variables like CPU usage, Free Memory, and Free Drive Space. through the integration of transfer learning and adaptive tuning, the proposed framework aims to overcome challenges such as missing data and dynamic resource utilization trends. the paper presents a comprehensive overview of the experimental workflow, results, and analysis, which contribute to the improvement of server crash forecasting and resource utilization monitoring in cloud computing environments. Based on several experiments and evaluations, the proposed framework demonstrated significant improvements in forecasting accuracy, achieving a reduction in error rate. © 2024 IEEE.

关键词： Cloud platforms

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Research on Pilot Fatigue Model Based on Dynamic Workload 12

Research on Pilot Fatigue Model Based on Dynamic Workload

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12th international conference on information systems and computing technology

作者： Sun, Yage Zhang, Xiaoyan Xue, Hongjun Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

ISBN: (纸本)9798350379860;9798350379877

In this paper, a human fatigue evaluation model was constructed based on dynamic load. Firstly, objective physiological parameters such as eye movement data, heart rate, EEG data, EMG data, blood pressure and respiratory rate of pilots were collected, and the main factors influencing fatigue evaluation were identified as follows: eye movement data, heart rate and heart rate variability, EEG data and EMG data. A real-time model was established to determine the fatigue state of the pilot, and the fatigue state was categorized according to the workload: adaptive flight (zone D), light flight fatigue (zone A), moderate flight fatigue (zone B), and severe flight fatigue (zone C). On this basis, considering the establishment of a dynamic load on the fatigue evaluation model, load entropy and load variation rate were proposed. Load entropy represents the degree of confusion in load fluctuation and change, that is, the uncertainty of load change, and the load variation rate is a statistic for measuring the variation degree of each load level in dynamic load over a period of time. Based on these two indicators, fatigue changes can be described as: passive fatigue, overreaction, and abnormal work rhythm. the accurate evaluation of pilot fatigue is conducive to timely monitoring of behavior status, identifying potential health problems, taking appropriate intervention measures promptly, and enhancing flight safety.

关键词： Dynamic loading Fatigue state evaluation model Fatigue state classification Fatigue change condition

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