IEEE transactions on artificial intelligence最新文献_第9页

Multimodality Consistency for Point Cloud Completion via Differentiable Rendering 基于可微分渲染的点云补全的多模态一致性

IEEE transactions on artificial intelligence Pub Date : 2025-01-10 DOI: 10.1109/TAI.2025.3527922

Ben Fei;Yixuan Li;Weidong Yang;Wen-Ming Chen;Zhijun Li

{"title":"Multimodality Consistency for Point Cloud Completion via Differentiable Rendering","authors":"Ben Fei;Yixuan Li;Weidong Yang;Wen-Ming Chen;Zhijun Li","doi":"10.1109/TAI.2025.3527922","DOIUrl":"https://doi.org/10.1109/TAI.2025.3527922","url":null,"abstract":"Point cloud completion aims to acquire complete and high-fidelity point clouds from partial and low-quality point clouds, which are used in remote sensing applications. Existing methods tend to solve this problem solely from the point cloud modality, limiting the completion process to only 3-D structure while overlooking the information from other modalities. Nevertheless, additional modalities possess valuable information that can greatly enhance the effectiveness of point cloud completion. The edge information in depth images can serve as a supervisory signal for ensuring accurate outlines and overall shape. To this end, we propose a brand-new point cloud completion network, dubbed multimodality differentiable rendering (<italic>MMDR</i>), which utilizes point-based differentiable rendering (DR) to obtain the depth images to ensure that the model preserves the point cloud structures from the depth image domain. Moreover, the attentional feature extractor (AFE) module is devised to exploit the global features inherent in the partial input, and the extracted global features together with the coordinates and features of the patch center are fed into the point roots predictor (PRP) module to obtain a set of point roots for the upsampling module with point upsampling Transformer (PU-Transformer). Furthermore, the multimodality consistency loss between the depth images from predicted point clouds and corresponding ground truth enables the PU-Transformer to generate a high-fidelity point cloud with predicted point agents. Extensive experiments conducted on various existing datasets give evidence that MMDR surpasses the off-the-shelf methods for point cloud completion after qualitative and quantitative analysis.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1746-1760"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Defense Against Adversarial Faces at the Source: Strengthened Faces Based on Hidden Disturbances 对抗源面防御：基于隐干扰的强化面

IEEE transactions on artificial intelligence Pub Date : 2025-01-10 DOI: 10.1109/TAI.2025.3527923

Shuangliang Li;Jinwei Wang;Hao Wu;Jiawei Zhang;Xin Cheng;Xiangyang Luo;Bin Ma

{"title":"Defense Against Adversarial Faces at the Source: Strengthened Faces Based on Hidden Disturbances","authors":"Shuangliang Li;Jinwei Wang;Hao Wu;Jiawei Zhang;Xin Cheng;Xiangyang Luo;Bin Ma","doi":"10.1109/TAI.2025.3527923","DOIUrl":"https://doi.org/10.1109/TAI.2025.3527923","url":null,"abstract":"Face recognition (FR) systems, while widely used across various sectors, are vulnerable to adversarial attacks, particularly those based on deep neural networks. Despite existing efforts to enhance the robustness of FR models, they still face the risk of secondary adversarial attacks. To address this, we propose a novel approach employing “strengthened face” with preemptive defensive perturbations. Strengthened face ensures original recognition accuracy while safeguarding FR systems against secondary attacks. In the white-box scenario, the strengthened face utilizes gradient-based and optimization-based methods to minimize feature representation differences between face pairs. For the black-box scenario, we propose shielded gradient sign descent (SGSD) to optimize the gradient update direction of strengthened faces, ensuring the transferability and effectiveness against unknown adversarial attacks. Experimental results demonstrate the efficacy of strengthened faces in defending against adversarial faces without compromising the performance of FR models or face image visual quality. Moreover, SGSD outperforms conventional methods, achieving an average performance improvement of 4% in transferability across different attack intensities.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1761-1775"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Network Output-Feedback Distributed Formation Control for NMASs Under Communication Delays and Switching Network 通信时延和交换网络下NMASs的神经网络输出反馈分布式编队控制

IEEE transactions on artificial intelligence Pub Date : 2025-01-09 DOI: 10.1109/TAI.2025.3527404

Haodong Zhou;Shaocheng Tong

{"title":"Neural Network Output-Feedback Distributed Formation Control for NMASs Under Communication Delays and Switching Network","authors":"Haodong Zhou;Shaocheng Tong","doi":"10.1109/TAI.2025.3527404","DOIUrl":"https://doi.org/10.1109/TAI.2025.3527404","url":null,"abstract":"This article studies the neural network (NN) output-feedback distributed formation control problem of nonlinear multiagent systems (NMASs) under communication delays and jointly connected switching network. Since the communication between agents is affected by time-varying delay and some agents cannot access the leader's information under jointly connected switching network, a communication-delay-related distributed formation observer is designed to estimate the leader's information and simultaneously mitigate the effects of communication delays. NNs are adopted to identify unknown functions, and an NN state observer is established to reconstruct unmeasurable states. Then, based on the designed distributed formation observer and NN state observer, an NN output-feedback distributed formation control algorithm is proposed by the backstepping control theory. It is proven that the designed communication-delay-related distributed formation observer errors converge to zero exponentially. Meanwhile, the proposed distributed NN formation control approach ensures the NMAS is stable, and the formation tracking errors converge to a small neighborhood around zero. Finally, we apply the output-feedback distributed formation control scheme to unmanned surface vehicles (USVs), the simulation results verify its effectiveness.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1591-1602"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stochastic Submodular Bandits With Delayed Composite Anonymous Bandit Feedback 具有延迟复合匿名强盗反馈的随机子模强盗

IEEE transactions on artificial intelligence Pub Date : 2025-01-09 DOI: 10.1109/TAI.2025.3527375

Mohammad Pedramfar;Vaneet Aggarwal

{"title":"Stochastic Submodular Bandits With Delayed Composite Anonymous Bandit Feedback","authors":"Mohammad Pedramfar;Vaneet Aggarwal","doi":"10.1109/TAI.2025.3527375","DOIUrl":"https://doi.org/10.1109/TAI.2025.3527375","url":null,"abstract":"This article investigates the problem of combinatorial multiarmed bandits with stochastic submodular (in expectation) rewards and full-bandit delayed feedback, where the delayed feedback is assumed to be composite and anonymous. In other words, the delayed feedback is composed of components of rewards from past actions, with unknown division among the subcomponents. Three models of delayed feedback: bounded adversarial, stochastic independent, and stochastic conditionally independent are studied, and regret bounds are derived for each of the delay models. Ignoring the problem dependent parameters, we show that regret bound for all the delay models is <inline-formula><tex-math>$tilde{O}(T^{2/3}+T^{1/3}nu)$</tex-math></inline-formula> for time horizon <inline-formula><tex-math>$T$</tex-math></inline-formula>, where <inline-formula><tex-math>$nu$</tex-math></inline-formula> is a delay parameter defined differently in the three cases, thus demonstrating an additive term in regret with delay in all the three delay models. The considered algorithm is demonstrated to outperform other full-bandit approaches with delayed composite anonymous feedback. We also demonstrate the generalizability of our analysis of the delayed composite anonymous feedback in combinatorial bandits as long as there exists an algorithm for the offline problem satisfying a certain robustness condition.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1727-1735"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CVDLLM: Automated Cardiovascular Disease Diagnosis With Large-Language-Model-Assisted Graph Attentive Feature Interaction CVDLLM：基于大语言模型辅助图关注特征交互的心血管疾病自动诊断

IEEE transactions on artificial intelligence Pub Date : 2025-01-09 DOI: 10.1109/TAI.2025.3527401

Xihe Qiu;Haoyu Wang;Xiaoyu Tan;Yaochu Jin

{"title":"CVDLLM: Automated Cardiovascular Disease Diagnosis With Large-Language-Model-Assisted Graph Attentive Feature Interaction","authors":"Xihe Qiu;Haoyu Wang;Xiaoyu Tan;Yaochu Jin","doi":"10.1109/TAI.2025.3527401","DOIUrl":"https://doi.org/10.1109/TAI.2025.3527401","url":null,"abstract":"Electrocardiogram (ECG) measurements are essential for detecting and treating cardiovascular disease (CVD). However, manual evaluation of ECGs is prone to errors due to morphological variations. Although machine learning methods have shown promise in diagnosing diseases, automatic CVD diagnosis based on ECGs is still suffering from low diagnosis accuracy due to the limited usage of time-series information and interlead correlations. In this article, we propose a large language model (LLM)-assisted graph attentive feature interaction learning framework (CVDLLM) for automatic ECG diagnosis. It utilizes ECG data from twelve leads to classify eight heart diseases, including rhythm abnormalities and normal conditions. Our framework combines convolutional and recurrent neural networks for independent time-series feature extraction from 12-lead ECG signals. By incorporating features extracted by heart rate variability (HRV) analysis, we employ graph attention neural networks (GAT) and self-attentive feature interaction mechanism (GSAT) for feature interaction and model learning. Leveraging LLMs with pretrained knowledge bases and advanced language comprehension, we extract and learn semantic embeddings from medical case data. This approach equips our framework with a deep semantic layer, significantly enhancing its capacity to understand complex medical texts. Additionally, by representing the twelve leads as a graph, our framework enables highly accurate disease diagnosis based on spatial and temporal interactions with 12-lead ECG signals. We evaluate the performance of our proposed framework and our framework achieves state-of-the-art performance with accuracy, precision, recall, and F1-score.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1575-1590"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge Intelligence: A Deep Distilled Model for Wearables to Enable Proactive Eldercare 边缘智能：可穿戴设备的深度提炼模型，以实现主动老年护理

IEEE transactions on artificial intelligence Pub Date : 2025-01-09 DOI: 10.1109/TAI.2025.3527400

Muhammad Fahim;S. M. Ahsan Kazmi;Vishal Sharma;Hyundong Shin;Trung Q. Duong

{"title":"Edge Intelligence: A Deep Distilled Model for Wearables to Enable Proactive Eldercare","authors":"Muhammad Fahim;S. M. Ahsan Kazmi;Vishal Sharma;Hyundong Shin;Trung Q. Duong","doi":"10.1109/TAI.2025.3527400","DOIUrl":"https://doi.org/10.1109/TAI.2025.3527400","url":null,"abstract":"Wearable devices are becoming affordable in our society to provide services from simple fitness tracking to the detection of heartbeat disorders. In the case of elderly populations, these devices have great potential to enable proactive eldercare, which can increase the number of years of independent living. The wearables can capture healthcare data continuously. For meaningful insight, deep learning models are preferable to process this data for robust outcomes. One of the major challenges includes deploying these models on edge devices, such as smartphones and wearables. The bottleneck is a large number of parameters and compute-intensive operations. In this research, we propose a novel knowledge distillation (KD) scheme by introducing a self-revision concept. This scheme effectively reduces model size and transfers knowledge from a deep model to a distilled model by filling learning gaps during the training. To evaluate our distilled model, a publicly available dataset, “growing old together validation (GOTOV)” is utilized, which is based on medical-grade standard wearables to monitor behavioral changes in the elderly. Our proposed model reduces the 0.7 million parameters to 1500, which enables edge intelligence. It achieves a 6% improvement in precision, a 9% increase in recall, and a 9% higher F1-score compared to the shallow model for recognizing elderly behavior.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1736-1745"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cyber Shadows: Neutralizing Security Threats With AI and Targeted Policy Measures 网络阴影：用人工智能和有针对性的政策措施消除安全威胁

IEEE transactions on artificial intelligence Pub Date : 2025-01-09 DOI: 10.1109/TAI.2025.3527398

Marc Schmitt;Pantelis Koutroumpis

引用次数: 0

Leveraging AI to Compromise IoT Device Privacy by Exploiting Hardware Imperfections 利用人工智能利用硬件缺陷来损害物联网设备隐私

IEEE transactions on artificial intelligence Pub Date : 2025-01-07 DOI: 10.1109/TAI.2025.3526139

Mirza Athar Baig;Asif Iqbal;Muhammad Naveed Aman;Biplab Sikdar

{"title":"Leveraging AI to Compromise IoT Device Privacy by Exploiting Hardware Imperfections","authors":"Mirza Athar Baig;Asif Iqbal;Muhammad Naveed Aman;Biplab Sikdar","doi":"10.1109/TAI.2025.3526139","DOIUrl":"https://doi.org/10.1109/TAI.2025.3526139","url":null,"abstract":"The constrained design, remote deployment, and sensitive data generated by Internet of Things (IoT) devices make them susceptible to various cyberattacks. One such attack is profiling IoT devices by tracking their packet transmissions. While existing methods mitigate these attacks using pseudonymous identities, we propose a novel attack strategy that exploits the physical layer characteristics of IoT devices. Specifically, we demonstrate how an attacker can leverage features extracted from device transmissions to identify packets originating from the same device. Once identified, the attacker can isolate the device's signals and potentially determine its physical location. This attack exploits the fact that microcontroller clock variations exist across devices, even within the same model line. By extracting transmission features and training machine learning (ML) models, we accurately identify the originating device of the packets. This study reveals inherent privacy vulnerabilities in IoT systems due to hardware imperfections that are beyond user control. These limitations have profound implications for the design of security frameworks in emerging ubiquitous sensing environments. Our experiments demonstrate that the proposed attack achieves 99% accuracy in real-world settings and can bypass privacy measures implemented at higher protocol layers. This work highlights the urgent need for privacy protection strategies across multiple layers of the IoT protocol stack.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1561-1574"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From Global to Hybrid: A Review of Supervised Deep Learning for 2-D Image Feature Representation 从全局到混合：二维图像特征表示的监督深度学习综述

IEEE transactions on artificial intelligence Pub Date : 2025-01-07 DOI: 10.1109/TAI.2025.3526138

Xinyu Dong;Qi Wang;Hongyu Deng;Zhenguo Yang;Weijian Ruan;Wu Liu;Liang Lei;Xue Wu;Youliang Tian

{"title":"From Global to Hybrid: A Review of Supervised Deep Learning for 2-D Image Feature Representation","authors":"Xinyu Dong;Qi Wang;Hongyu Deng;Zhenguo Yang;Weijian Ruan;Wu Liu;Liang Lei;Xue Wu;Youliang Tian","doi":"10.1109/TAI.2025.3526138","DOIUrl":"https://doi.org/10.1109/TAI.2025.3526138","url":null,"abstract":"Computer vision is the science that aims to enable computers to emulate human visual perception, and it encompasses various techniques and methods for extracting and interpreting information from two-dimensional images. Supervised deep 2-D image feature representation is a fundamental problem in computer vision that applies deep learning techniques to extract and process information from a given 2-D image under supervised settings. The goal is to obtain a feature vector that can be utilized for various downstream computer vision applications. The quality of supervised deep 2-D image feature representation algorithms directly affects the performance of downstream applications. However, most of the existing vision research only explores supervised deep 2-D image feature representation for specific subtasks. Therefore, a comprehensive discussion on this topic is needed. In this article, we propose a taxonomy of supervised deep 2-D image feature representation methods based on four categories: global representation, region representation, hash representation, and hybrid representation, and we introduce their typical approaches. Furthermore, we perform a comparative analysis of the representative methods on three fundamental tasks: image classification, object detection, and semantic segmentation, as well as other common tasks. We also discuss the limitations of supervised deep 2-D image feature representation and investigate future directions in image representation to facilitate the advancement of computer vision through image representation.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1540-1560"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Spatial-Transformation-Based Causality-Enhanced Model for Glioblastoma Progression Diagnosis 胶质母细胞瘤进展诊断的基于空间转换的因果关系增强模型

IEEE transactions on artificial intelligence Pub Date : 2025-01-07 DOI: 10.1109/TAI.2025.3526137

Qiang Li;Xinyue Li;Hong Jiang;Xiaohua Qian

{"title":"A Spatial-Transformation-Based Causality-Enhanced Model for Glioblastoma Progression Diagnosis","authors":"Qiang Li;Xinyue Li;Hong Jiang;Xiaohua Qian","doi":"10.1109/TAI.2025.3526137","DOIUrl":"https://doi.org/10.1109/TAI.2025.3526137","url":null,"abstract":"Differentiation between pseudoprogression and true tumor progression of glioblastoma (GBM) is crucial for choosing appropriate management strategies and increasing the chances of patient survival. Currently, there is a lack of noninvasive and effective methods in clinic for GBM progression diagnosis. Here, we propose an automated early diagnosis method based on diffusion tensor imaging (DTI) with a high potential for this diagnosis. A primary challenge for intelligent diagnostic methods lies in the limited accuracy and stability caused by data insufficiency and the fine-grained nature of diagnostic tasks. To address this challenge, we develop a spatial-transformation-based causality-enhanced model (ST-CEM). This model jointly improves data diversity and the effective utilization of clinically significant discriminative information. Specifically, first, a texture diverse augmentation scheme is designed based on a spatial transformation, which allows for greater texture diversification in the augmented data. Subsequently, an interference information contrastive strategy is developed, where nonlesion features that may introduce interference are actively extracted and decoupled with lesion features. Finally, a causality-enhanced mechanism is introduced to highlight the decoupled lesion features, thereby improving the diagnostic stability of the model. Extensive experiments verified the effectiveness of our model in diagnosis of GBM progression under small-sample conditions. The proposed model achieved an accuracy of 84.1%, precision of 85.8%, and recall of 90.3%, all of which outperform the existing works. Moreover, it demonstrated competitive performance on an additional lung nodule classification dataset.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1529-1539"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0