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VF-Lens: Enhancing Visual Perception of Visually Impaired Users in VR via Adversarial Learning with Visual Field Attention 32

VF-Lens: Enhancing Visual Perception of Visually Impaired Us...

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32nd IEEE conference on Virtual Reality and 3D User Interfaces, VR 2025

作者： Chen, Xiaoming Han, Dehao Qu, Qiang Shen, Yiran Beijing Technology and Business University School of Computer and Artificial Intelligence China The University of Sydney Australia The University of Sydney School of Computer Science Australia Shandong University School of Software China

ISBN: (纸本)9798331536459

This research aims to enhance the image perception of visually impaired users in VR environments. We propose VF-Lens, a model that adaptively compensates for light sensitivity based on the user's visual field impairment, acting as a virtual lens between the visually impaired users and the VR world. VF-Lens is designed as a tailored generative adversarial learning model with a generator and discriminator, offering applicability to various types of visual impairments while bypassing engineering complexities. The generator creates a "hyperimage"tailored to the user's visual field impairment, which then undergoes a particular regression process to predict and replicate the real perception of the visually impaired user. The discriminator then evaluates the similarity between the replicated perception and the original image. Through adversarial training, the generator can produce hyperimages that adapt to the user's visual field parameters, enabling them to perceive the image more similarly to normal-vision users. We further improve VF-Lens by proposing new "visual field attention"mechanisms that prioritize and refine visual information in the user's visual field. Extensive evaluation, encompassing both visually impaired participants and simulations, has been conducted to demonstrate the effectiveness of VF-Lens in improving visual perception for visually impaired users. Moreover, we establish a standardized evaluation process involving tailored metrics as well as objective and subjective evaluations to promote reusability and comparability for future research in this field. © 2025 IEEE.

关键词： Generative adversarial networks

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Wrist Fracture Detection Using Deep Learning

Wrist Fracture Detection Using Deep Learning

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Rafi, Shaik Mahmamad Anuradha, T. Srinivas, Kothapalli S. V. V. R. Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Information Technology Kanuru India

ISBN: (纸本)9798331529833

Effective treatment of wrist bone fractures depends on early and precise identification. The finding shows that disparities in accessing timely emergency services translate into adverse patient outcomes, especially in regions with fewer healthcare facilities and extended distances, mostly rural areas. Distal radial fractures or wrist fractures are among the commonly encountered musculoskeletal injuries and the treatment and subsequent management depend on timely and precise identification of the fracture pattern. This paper discusses a wrist fracture detection scheme using a deep learning approach for diagnosing wrist bone fractures from X-ray images which can close the health disparities due to the lack of adequate early diagnosis. and his team developed the system using DenseNet-201, MobileNetV2, ResNet-50, EfficientNetV2, U-net and Inception V3 architecture because each was selected for a certain use effect patient outcome. Wrist fractures, among the most common musculoskeletal injuries, require early and accurate diagnosis to optimize treatment and rehabilitation. This project presents a deep learning-based system to detect wrist fractures in X-ray images, designed to bridge healthcare inequalities by facilitating early and automated diagnosis. The system integrates DenseNet-201, MobileNetV2, ResNet-50, EfficientNetV2, U-Net, and Inception V3 models, each chosen for specific roles. DenseNet-201, MobileNetV2, and ResNet-50 are classification networks for fracture detection while U-Net is segmentation network to segment fractured areas. Furthermore, the results using EfficientNetV2 and Inception V3 improve diagnostic capability and image *** work compares the pros and cons of each model in terms of accuracy, computational time, and loss and presents recommendations for the most appropriate models for clinical applications in practice. © 2025 IEEE.

关键词： Deep learning

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State-Dependent Admission Control in Heterogeneous Queueing-Inventory System with Constant Retrial Rate 27th

State-Dependent Admission Control in Heterogeneous Queueing...

引用

27th international conference on Distributed computer and Communication Networks, DCCN 2024

作者： Melikov, Agassi Rumyantsev, Alexander Baku Engineering University Hasan Aliyev Street 120 Absheron Khirdalan CityAZ0101 Azerbaijan Institute of Applied Mathematical Research of the Karelian Research Centre of RAS Pushkinskaya Street 11 Petrozavodsk185910 Russia Center for Applied Mathematics Yaroslav-the-Wise Novgorod State University Bolshaya St. Petersburgskaya Street 41 V. Novgorod173001 Russia

ISBN: (纸本)9783031808524

The paper is dedicated to a retrial queueing-inventory system with constant retrial rate, heterogeneous customer classes (distinguished by various inventory demands) working under the class- and inventory level-based admission control to a service zone with class-specific servers (one server per class). The replenishment policy is a mixture of the two classical policies, (s, Q) and (s, S). The model is treated by matrix-analytic method in steady- and in transient states in Markov case, and by regenerative simulation in the non-Markov case, and interesting performance measures are derived. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Leak Detection in Urban Pipe Networks 28th

Leak Detection in Urban Pipe Networks

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28th international conference on Hydraulics, Water Resources, River and Coastal engineering, HYDRO 2023

作者： Ranjith, Akshay Vemavarapu, Venkata Srinivas Rao, Lakshminarayana Department of Civil Engineering Indian Institute of Science Bengaluru560012 India Indian Institute of Science Bengaluru560012 India Divecha Center for Climate Change Indian Institute of Science Bengaluru560012 India Indian Institute of Science Bengaluru India

ISBN: (纸本)9789819774661

The transportation of potable and wastewater through pipelines has become an essential aspect of modern society. Pipelines offer significant advantages such as ease of continuous operation, high efficiency, automation potential, and low energy requirements. However, the risk of pipeline bursts or leaks poses a substantial threat, leading to potential disasters. Consequently, the detection and localization of pipeline leaks have become a critical concern. This paper presents a methodology to detect and locate leaks within urban pipe networks. Towards this, investigations were carried out on a lab-scale water distribution network that provides a controlled environment for introducing leaks at various points within the pipe network and analyzing the effect of demand nodes on leaks by collecting real-time data on multiple flow parameters. It also has provision to study the effect of different pipe materials (ductile iron, galvanized iron, and polyvinyl chloride) with multiple leak points in 100 mm nominal pipe diameter, which better depicts reality in real-world urban pipe networks. The facility experimentally simulates leaks and collects real-time data on various fluid parameters. A digital twin was constructed using EPANET and WaterGEMS software to create a realistic simulation of the pipe network. The lab-scale water distribution network, in combination with the digital twin, offered a comprehensive and high-quality dataset, enabling the development of a robust leak detection methodology for urban pipe networks. By analyzing this dataset, an algorithm was trained to identify and localize leaks within the network under various flow rates (Reynolds number) to demonstrate the algorithm's effectiveness in leak detection and localization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Leak detection

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DCCMamba: A Dual-stream Cross-time and Cross-feature with Mamba for Multivariate Time Series Forecasting

DCCMamba: A Dual-stream Cross-time and Cross-feature with Ma...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Senlin Wang, Zhuoyue Zhao, Chunna Huang, Yaqun Xu, Jinpeng Yang, Yaoyuan School of Information Science & Engineering Yunnan University Kunming China Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley United States

ISBN: (纸本)9798350368741

In recent years, multivariate time series forecasting (MTSF) has gained significant attention. And transformer-based models have showed strong performance. However, the quadratic complexity of attention mechanisms leads to inefficiency and high overhead. The Mamba state-space model offers a more efficient alternative with lower complexity and fewer parameters. However, its unilateral nature limits the effective capture of time and feature dimensions. To address this, we propose A Dual-stream Cross-time and Cross-feature with Mamba (DCCMamba) for MTSF. Specifically, we employ a dual-stream structure to extract different information from the time and feature dimensions. One stream encodes data from a feature perspective and passes it through a Mamba layer to capture cross-time information. The other stream encodes the data from the time perspective, decomposes it into trend and seasonal components, and captures cross-feature information using a Mamba layer. Finally, we fuse the cross-feature information and cross-time information to get the final result. Experiments on four public datasets demonstrate that DCCMamba achieves state-of-the-art performance. © 2025 IEEE.

关键词： Cross-feature Cross-time dual-stream Mamba time series

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Optimizing Resource Costs With Machine Learning Techniques 2

Optimizing Resource Costs With Machine Learning Techniques

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Balamanigandan, R. Mahaveerakannan, R. Saraswathi, S. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

The number of SMEs opting for cloud computing rises, efficient utilisation of the cloud has become a crucial issue because of the rental nature of the resources. Current approaches tend to be more traditional, targeting only individual components such as the amount of computing capacity, which may not adequately capture or mitigate the factors that define most actual cloud workloads and their unpredictable behaviour. In light of this, this article presents a new strategy that applies anomaly detection learning algorithms and particle swarm optimization to achieve the right cloud resource configuration at the least cost possible. The suggested idea works independent of a specific context in a loop fashion and distinguishes exceptional cases through an anomaly detection strategy in the loop process and at the same time learns the usage patterns. More importantly, it adapts to changes in system utilisation, as well as the pricing models offered by various cloud providers. Realistic costs as well as system data using the Microsoft Azure proved significant with highlighting 85% costs shaving within 10 months. This research supports the effectiveness of our integrated solution for solving multifaceted issues related to resource management in cloud environments for SMEs. © 2025 IEEE.

关键词： Resource allocation

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RelaI2P: Relational Learning for Image-to-Point Cloud Registration

RelaI2P: Relational Learning for Image-to-Point Cloud Regist...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hou, Minghui Wang, Gang Wang, Zhiyang Ma, Baorui College of Computer Science and Technology Jilin University Changchun China College of Software Jilin University Changchun China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9798350368741

Cross-modality registration between 2D images and 3D point clouds is an important task in autonomous driving and robotics. Existing methods predict the correspondence between images and point clouds by matching patterns of pixel and point features learned by deep neural networks. However, due to the significant differences in their representation and feature processing, the feature spaces are vastly different. The insufficient feature interaction between image and point cloud branches leads to a lack of information necessary for relational reasoning, which is crucial for establishing the correspondence between pixels and points. To address these problems, we propose a Cross-Modality Relation Module (CMRM) that leverages the relations between different levels of features from images and point clouds. This module facilitates rich information exchange between the feature extractor branches corresponding to the two modalities. Additionally, we introduce a Relation-Aware Fusion Module (RAFM) that effectively integrates multimodal features and their relations. The experimental results on KITTI dataset show improvements over the state-of-the-art methods. The code will be publicly available at https://***/JLUrob/RelaI2P. © 2025 IEEE.

关键词： cross-modality relation image-to-point cloud registration relation-aware fusion

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Numerical simulation of an OWC-type wave energy converter with projecting walls in regular waves 6th

Numerical simulation of an OWC-type wave energy converter wi...

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6th international conference on Renewable Energies Offshore, RENEW 2024

作者： Tan, L. Chang, R.Y. Ikoma, T. Department of Oceanic Architecture and Engineering College of Science and Technology Nihon University Chiba Funabashi Japan

ISBN: (纸本)9781032905570

This paper presents a numerical investigation on the hydrodynamic characteristics of a projecting wall type oscillating water column (OWC) wave energy converter by a frequency-domain linear potential flow model and a CFD model based on Flow3D software. It is found that both the linear potential flow and CFD solutions are in reasonable agreements with the first order experimental results of OWC and air pressure responses. The linear potential solution is found to under-predict the primary-stage energy conversion performance at long wavelengths and the reason may be attributable to nonlinear effects. The airflow velocity response result near the OWC chamber orifice indicates that the nonlinear frequency components are non-negligible. The variation of the velocity magnitude counter near the OWC model is examined based on CFD simulations. The phase differences among the velocities, pressures and OWC response are analyzed, which are expected to be helpful in understanding the physical mechanisms of OWC devices. © 2025 The Author(s).

关键词： Potential flow

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LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

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31st international conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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Breaking Through the Spike: Spike Window Decoding for Accelerated and Precise Automatic Speech Recognition

Breaking Through the Spike: Spike Window Decoding for Accele...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Wei Zhang, Tian-Hao Luo, Chao Zhou, Hui Yang, Chao Qian, Xinyuan Yin, Xu-Cheng *** GROUP Shanghai China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Recently, end-to-end automatic speech recognition has become the mainstream approach in both industry and academia. To optimize system performance in specific scenarios, the Weighted Finite-State Transducer (WFST) is extensively used to integrate acoustic and language models, leveraging its capacity to implicitly fuse language models within static graphs, thereby ensuring robust recognition while also facilitating rapid error correction. However, WFST necessitates a frame-by-frame search of CTC posterior probabilities through autoregression, which significantly hampers inference speed. In this work, we thoroughly investigate the spike property of CTC outputs and further propose the conjecture that adjacent frames to non-blank spikes carry semantic information beneficial to the model. Building on this, we propose the Spike Window Decoding algorithm, which greatly improves the inference speed by making the number of frames decoded in WFST linearly related to the number of spiking frames in the CTC output, while guaranteeing the recognition performance. Our method achieves SOTA recognition accuracy with significantly accelerates decoding speed, proven across both AISHELL-1 and large-scale In-House datasets, establishing a pioneering approach for integrating CTC output with WFST. © 2025 IEEE.

关键词： automatic speech recognition spike window decoding wfst

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