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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wangdong Guo Qing Zhu Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER.

关键词： Emotion recognition Analytical models Accuracy Noise Noise reduction Speech recognition Complexity theory Noise measurement Speech processing Context modeling

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Federated Learning for Real-Time Decentralized Smile Detection in Virtual Reality Environments

Federated Learning for Real-Time Decentralized Smile Detecti...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Salabat Khan Anwar Ghani Syed Shehreyar Ali Naqvi Murad Ali Khan Muhammad Faseeh Do Hyeun Kim Muhammad Sharif Dept. of Computer Engineering Big Data Research Center Jeju National University South Korea Dept. of Computer Engineering Jeju National University South Korea Dept. of Computer Science Comsats University Islamabad Pakistan

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Facial expression detection is paramount in real-world applications, including patient monitoring, photo selection, and facial protection within the metaverse and virtual reality environments. Among various expressions, detecting smiles poses a significant challenge. Traditional methods relying on low-level face descriptors are being superseded by advancements leveraging Convolutional Neural Networks (CNNs) to enhance smile detection performance. In this research, we introduce and implement three CNN architectures tailored for smile detection from facial images. Our proposed model incorporates a fully convolutional neural network featuring two deep convolutional layers dedicated to smile detection. Additionally, pre-trained LeNet and DenseNet CNNs are employed for this task. The effectiveness of our approach is demonstrated through extensive evaluations of the Hromada dataset, encompassing images with and without smiles within the virtual reality environment. Moreover, we delve into detailed discussions on the practicality and efficacy of CNNs in facial expression detection, explicitly focusing on smile detection in real-time scenarios. Our experiments are conducted within a federated learning framework, enabling model inference and optimization for privacy preservation and scalability of the smile detection system in large-scale virtual reality applications. Through this decentralized approach, we address challenges related to real-time detection, model inference, and collaboration in the metaverse.

关键词： Training Solid modeling Federated learning Metaverse Scalability Collaboration Virtual reality Real-time systems Convolutional neural networks Faces

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Radio Map Estimation via Latent-Domain Plug-and-Play Denoisers

Radio Map Estimation via Latent-Domain Plug-and-Play Denoise...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Le Xu Lei Cheng Junting Chen Wenqiang Pu Xiao Fu School of Electrical Engineering and Computer Science Oregon State University Corvallis USA College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. data-driven deep neural model-based RME showed promising performance, yet requiring excessive training resources. This work puts forth an RME approach that can effectively incorporate learned information without training over radio map data. Our idea is to employ the plug-and-play (PnP) denoising scheme from computational imaging. The PnP framework allows incorporating denoisers trained over natural images to handle other types of data, e.g., ocean sound fields and medical images, due to the similarity of their denoising processes. Conventional PnP methods mostly use the learned denoisers in the data domain. Instead, the proposed approach applies PnP in the latent domain through a tailored algorithm design and spatial-spectral factorization of radio maps. This way, the proposed RME method exhibits enhanced scalability and noise robustness. Simulations are used to illustrate the effectiveness of the proposed approach.

关键词： Training Computational modeling Scalability Oceans Signal processing algorithms Estimation Sea measurements Numerical simulation Robustness Speech processing

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Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

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International Conference on computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

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A First Look at LLM-powered Smartphones

A First Look at LLM-powered Smartphones

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IEEE/ACM International Conference on Automated Software engineering - Workshops (ASE Workshops)

作者： Liangxuan Wu Yanjie Zhao Chao Wang Tianming Liu Haoyu Wang Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology

ISBN: (数字)9798400712494

ISBN: (纸本)9798331523169

The integration of Large Language Models (LLMs) into edge devices such as smartphones represents a significant leap in mobile technology, promising enhanced user experiences and novel functionalities. This paper presents a first look at LLM-powered smartphones, addressing four key aspects: the current market landscape, core functions enabled by integrated LLMs, potential security risks, and user perceptions. The findings reveal a rapidly evolving market with major manufacturers competing to integrate LLMs, innovative features that improve user interaction, significant security challenges, and mixed user perceptions that balance enthusiasm for new capabilities with privacy concerns. This study contributes to understanding LLM integration in mobile devices and its implications for users, manufacturers, and the broader technological landscape.

关键词： Industries Privacy Technological innovation Ethics Large language models Conferences User experience Security Smart phones Software engineering

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LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking

arXiv

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arXiv 2024年

作者： Zhao, Zijian Chen, Tingwei Meng, Fanyi Cai, Zhijie Li, Hang Li, Xiaoyang Zhu, Guangxu Shenzhen Research Institute of Big Data Shenzhen518115 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China Shenzhen518115 China

data-driven Wi-Fi localization and tracking have shown great promise due to their lower reliance on specialized hardware compared to model-based methods. However, most existing data collection techniques provide only coarse-grained ground truth or a limited number of labeled points, significantly hindering the advancement of data-driven approaches. While systems like lidar can deliver precise ground truth, their high costs make them inaccessible to many users. To address these challenges, we propose LoFi, a vision-aided label generator for Wi-Fi localization and tracking. LoFi can generate ground truth position coordinates solely from 2D images, offering high precision, low cost, and ease of use. Utilizing our method, we have compiled a Wi-Fi tracking and localization dataset using the ESP32-S3 and a webcam, which will be open-sourced along with the code upon publication. Copyright © 2024, The Authors. All rights reserved.

关键词： Wi-Fi

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Optimization of 3D Holographic Projection for a Medical Image System using Taguchi Method and Fuzzy Inference System

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Journal of Imaging science and Technology 2023年第4期67卷 1-7页

作者： Chou, Ming-Chung Huang, Tian-Hsiang Tsai, Jinn-Tsong Chen, Yen-Ming J. Hwang, Kao-Shing Huang, Wei-Tai Ho, Wen-Hsien Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Mechanical Engineering National Pingtung University of Science and Technology Pingtung Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan

Two dimensional projections of three dimensional (3D) objects are used to teach anatomy. These projections are relatively unrealistic, and medical students cannot easily and accurately grasp the position and shape of each anatomical structure. As a remedy, 3D holographic projections allow for 3D anatomical structures to be projected in space without the use of worn contraptions. This study designed a method for optimizing 3D holographic projections with respect to the quality characteristics of 3D imaging definition and stereoscopic quality. The Taguchi method was used to determine the factors that most greatly influenced these quality characteristics, and the Delphi method and fuzzy theory were used to quantify image quality. Among three main factors of material of the projection panel, thickness of the projection panel, and angle made by the projection panel with the horizontal plane, thickness was the most influential. The optimal combination of factors was acrylic material, a thickness of 1 mm, and an angle of 45°. This paper's method aids medical pedagogy and can be used by the holographic projection manufacturing industry to optimize 3D imaging definition and stereoscopic quality. © 2023 Society for Imaging science and Technology.

关键词： Taguchi methods

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Movable Antenna Enhanced Integrated Sensing and Communication Via Antenna Position Optimization

arXiv

引用

arXiv 2025年

作者： Ma, Wenyan Zhu, Lipeng Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

In this paper, we propose an integrated sensing and communication (ISAC) system aided by the movable-antenna (MA) array, which can improve the communication and sensing performance via flexible antenna movement over conventional fixed-position antenna (FPA) array. First, we consider the downlink multiuser communication, where each user is randomly distributed within a given three-dimensional zone with local movement. To reduce the overhead of frequent antenna movement, the antenna position vector (APV) is designed based on users’ statistical channel state information (CSI), so that the antennas only need to be moved in a large timescale. Then, for target sensing, the Cramer-Rao bounds (CRBs) of the estimation mean square error for different spatial angles of arrival (AoAs) are derived as functions of MAs’ positions. Based on the above, we formulate an optimization problem to maximize the expected minimum achievable rate among all communication users, with given constraints on the maximum acceptable CRB thresholds for target sensing. An alternating optimization algorithm is proposed to iteratively optimize one of the horizontal and vertical APVs of the MA array with the other being fixed. Numerical results demonstrate that our proposed MA arrays can significantly enlarge the trade-off region between communication and sensing performance compared to conventional FPA arrays with different inter-antenna spacing. It is also revealed that the steering vectors of the designed MA arrays exhibit low correlation in the angular domain, thus effectively reducing channel correlation among communication users to enhance their achievable rates, while alleviating ambiguity in target angle estimation to achieve improved sensing accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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DarkSAM: Fooling Segment Anything Model to Segment Nothing

arXiv

引用

arXiv 2024年

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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