IEEE transactions on artificial intelligence最新文献_第7页

Boosting Few-Shot Semantic Segmentation With Prior-Driven Edge Feature Enhancement Network 基于先验驱动边缘特征增强网络的少镜头语义分割

IEEE transactions on artificial intelligence Pub Date : 2024-10-07 DOI: 10.1109/TAI.2024.3474650

Jingkai Ma;Shuang Bai;Wenchao Pan

{"title":"Boosting Few-Shot Semantic Segmentation With Prior-Driven Edge Feature Enhancement Network","authors":"Jingkai Ma;Shuang Bai;Wenchao Pan","doi":"10.1109/TAI.2024.3474650","DOIUrl":"https://doi.org/10.1109/TAI.2024.3474650","url":null,"abstract":"Few-shot semantic segmentation (FSS) focuses on segmenting objects of novel classes with only a small number of annotated samples and has achieved great development. However, compared with general semantic segmentation, inaccurate boundary predictions remain a serious problem in FSS. This is because, in scenarios with few samples, the extracted query features by the model struggle to contain sufficient detailed information to focus on the boundary of the target. To address this issue, we propose a prior-driven edge feature enhancement network (PDEFE) that utilizes the prior information of the object edges to enhance the query feature, thereby promoting the accurate segmentation of the target. Specifically, we first design an edge feature enhancement module (EFEM) that can utilize object edges to enhance the feature of the query object's boundaries. Furthermore, we also propose an edge prior mask generator (EPMG) to generate prior masks for edges based on the gradient information of the image, which can guide the model to pay more attention to the boundaries of the target in the query image. Extensive experiments on PASCAL-<inline-formula><tex-math>$5^{i}$</tex-math></inline-formula> and COCO-<inline-formula><tex-math>$20^{i}$</tex-math></inline-formula> demonstrate that PDEFE significantly improves upon two baseline detectors (up to 2.7<inline-formula><tex-math>$sim$</tex-math></inline-formula>4.2% mIoU in average), achieving state-of-the-art performance.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"211-220"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975726","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

Knowledge Probabilization in Ensemble Distillation: Improving Accuracy and Uncertainty Quantification for Object Detectors 集成蒸馏中的知识概率化：提高目标检测器的精度和不确定度量化

IEEE transactions on artificial intelligence Pub Date : 2024-10-07 DOI: 10.1109/TAI.2024.3474654

Yang Yang;Chao Wang;Lei Gong;Min Wu;Zhenghua Chen;Xiang Li;Xianglan Chen;Xuehai Zhou

{"title":"Knowledge Probabilization in Ensemble Distillation: Improving Accuracy and Uncertainty Quantification for Object Detectors","authors":"Yang Yang;Chao Wang;Lei Gong;Min Wu;Zhenghua Chen;Xiang Li;Xianglan Chen;Xuehai Zhou","doi":"10.1109/TAI.2024.3474654","DOIUrl":"https://doi.org/10.1109/TAI.2024.3474654","url":null,"abstract":"Ensemble object detectors have demonstrated remarkable effectiveness in enhancing prediction accuracy and uncertainty quantification. However, their widespread adoption is hindered by significant computational and storage demands, limiting their feasibility in resource-constrained settings. To overcome this, researchers have focused on distilling the knowledge from ensemble object detectors into a single model. In this article, we introduce probabilization based ensemble distillation (ProbED), an innovative ensemble distillation framework that consolidates knowledge from multiple object detectors into a single, resource-efficient model. Unlike traditional ensemble distillation methods that average the outputs of subteachers, ProbED captures comprehensive outcome distributions from all subteachers, providing a more nuanced approach to knowledge transfer. ProbED employs knowledge probabilization to achieve a sophisticated and refined aggregation of teacher knowledge, including feature knowledge, semantic knowledge, and localization knowledge, resulting in dual improvements in prediction accuracy and uncertainty quantification for the student model. In particular, ProED's novel knowledge probabilization-based approach to aggregating teacher knowledge is inspired by our empirical observations, which demonstrate that knowledge probabilization excels in effectively representing uncertainty, improving prediction, and facilitating robust knowledge transfer. Furthermore, we introduce a random smoothing perturbation technique to modify inputs within ProbED, further enhancing the distillation process. Extensive experiments highlight ProbED's ability to significantly enhance the prediction accuracy and uncertainty quantification of various object detectors, demonstrating its superior performance compared to other state-of-the-art techniques.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"221-233"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975973","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

Under the Influence: A Survey of Large Language Models in Fake News Detection

IEEE transactions on artificial intelligence Pub Date : 2024-10-02 DOI: 10.1109/TAI.2024.3471735

Soveatin Kuntur;Anna Wróblewska;Marcin Paprzycki;Maria Ganzha

引用次数: 0

Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity 基于社区结构和流行度的Web API合作网络演进

IEEE transactions on artificial intelligence Pub Date : 2024-10-02 DOI: 10.1109/TAI.2024.3472614

Guosheng Kang;Yang Wang;Jianxun Liu;Buqing Cao;Yong Xiao;Yu Xu

{"title":"Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity","authors":"Guosheng Kang;Yang Wang;Jianxun Liu;Buqing Cao;Yong Xiao;Yu Xu","doi":"10.1109/TAI.2024.3472614","DOIUrl":"https://doi.org/10.1109/TAI.2024.3472614","url":null,"abstract":"With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup–API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabάsi–Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi–Barabάsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6659-6671"},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825898","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

Optimal Control of Stochastic Markovian Jump Systems With Wiener and Poisson Noises: Two Reinforcement Learning Approaches 具有维纳和泊松噪声的随机马尔可夫跳跃系统的最优控制：两种强化学习方法

IEEE transactions on artificial intelligence Pub Date : 2024-10-02 DOI: 10.1109/TAI.2024.3471729

Zhiguo Yan;Tingkun Sun;Guolin Hu

引用次数: 0

Fair Machine Learning in Healthcare: A Survey 医疗保健中的公平机器学习：调查

IEEE transactions on artificial intelligence Pub Date : 2024-09-30 DOI: 10.1109/TAI.2024.3361836

Qizhang Feng;Mengnan Du;Na Zou;Xia Hu

{"title":"Fair Machine Learning in Healthcare: A Survey","authors":"Qizhang Feng;Mengnan Du;Na Zou;Xia Hu","doi":"10.1109/TAI.2024.3361836","DOIUrl":"https://doi.org/10.1109/TAI.2024.3361836","url":null,"abstract":"The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problems is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in ML and healthcare disparities. We adopt a framework based on the principles of distributive justice to categorize fairness concerns into two distinct classes: equal allocation and equal performance. We provide a critical review of the associated fairness metrics from a ML standpoint and examine biases and mitigation strategies across the stages of the ML lifecycle, discussing the relationship between biases and their countermeasures. The article concludes with a discussion on the pressing challenges that remain unaddressed in ensuring fairness in healthcare ML and proposes several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"493-507"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611859","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

Get Rid of Your Trail: Remotely Erasing Backdoors in Federated Learning 消除你的痕迹：远程清除联合学习中的后门

IEEE transactions on artificial intelligence Pub Date : 2024-09-23 DOI: 10.1109/TAI.2024.3465441

Manaar Alam;Hithem Lamri;Michail Maniatakos

{"title":"Get Rid of Your Trail: Remotely Erasing Backdoors in Federated Learning","authors":"Manaar Alam;Hithem Lamri;Michail Maniatakos","doi":"10.1109/TAI.2024.3465441","DOIUrl":"https://doi.org/10.1109/TAI.2024.3465441","url":null,"abstract":"Federated learning (FL) enables collaborative learning across multiple participants without exposing sensitive personal data. However, the distributed nature of FL and unvetted participants’ data makes it vulnerable to \u0000<italic>backdoor attacks</i>\u0000. In these attacks, adversaries selectively inject malicious functionality into the centralized model during training, leading to intentional misclassifications for specific adversary-chosen inputs. While previous research has demonstrated successful injections of persistent backdoors in FL, the persistence also poses a challenge, as their existence in the centralized model can prompt the central aggregation server to take preventive measures for penalizing the adversaries. Therefore, this article proposes a method \u0000<italic>that enables adversaries to effectively remove backdoors from the centralized model</i>\u0000 upon achieving their objectives or upon suspicion of possible detection. The proposed approach extends the concept of \u0000<italic>machine unlearning</i>\u0000 and presents strategies to preserve the performance of the centralized model and simultaneously prevent over-unlearning of information unrelated to backdoor patterns, making adversaries stealthy while removing backdoors. To the best of our knowledge, this is the first work exploring machine unlearning in FL to remove backdoors to the benefit of adversaries. Exhaustive evaluation considering various image classification scenarios demonstrates the efficacy of the proposed method for efficient backdoor removal from the centralized model, injected by state-of-the-art attacks across multiple configurations.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6683-6698"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825812","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

RATs-NAS: Redirection of Adjacent Trails on Graph Convolutional Networks for Predictor-Based Neural Architecture Search 基于预测器的神经结构搜索中图卷积网络相邻轨迹的重定向

IEEE transactions on artificial intelligence Pub Date : 2024-09-23 DOI: 10.1109/TAI.2024.3465433

Yu-Ming Zhang;Jun-Wei Hsieh;Chun-Chieh Lee;Kuo-Chin Fan

{"title":"RATs-NAS: Redirection of Adjacent Trails on Graph Convolutional Networks for Predictor-Based Neural Architecture Search","authors":"Yu-Ming Zhang;Jun-Wei Hsieh;Chun-Chieh Lee;Kuo-Chin Fan","doi":"10.1109/TAI.2024.3465433","DOIUrl":"https://doi.org/10.1109/TAI.2024.3465433","url":null,"abstract":"Manually designed convolutional neural networks (CNNs) architectures such as visual geometry group network (VGG), ResNet, DenseNet, and MobileNet have achieved high performance across various tasks, but design them is time-consuming and costly. Neural architecture search (NAS) automates the discovery of effective CNN architectures, reducing the need for experts. However, evaluating candidate architectures requires significant graphics processing unit (GPU) resources, leading to the use of predictor-based NAS, such as graph convolutional networks (GCN), which is the popular option to construct predictors. However, we discover that, even though the ability of GCN mimics the propagation of features of real architectures, the binary nature of the adjacency matrix limits its effectiveness. To address this, we propose redirection of adjacent trails (RATs), which adaptively learns trail weights within the adjacency matrix. Our RATs-GCN outperform other predictors by dynamically adjusting trail weights after each graph convolution layer. Additionally, the proposed divide search sampling (DSS) strategy, based on the observation of cell-based NAS that architectures with similar floating point operations (FLOPs) perform similarly, enhances search efficiency. Our RATs-NAS, which combine RATs-GCN and DSS, shows significant improvements over other predictor-based NAS methods on NASBench-101, NASBench-201, and NASBench-301.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6672-6682"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825948","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

Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids 基于时空图的智能电网虚假数据注入规避攻击生成与检测

IEEE transactions on artificial intelligence Pub Date : 2024-09-20 DOI: 10.1109/TAI.2024.3464511

Abdulrahman Takiddin;Muhammad Ismail;Rachad Atat;Erchin Serpedin

{"title":"Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids","authors":"Abdulrahman Takiddin;Muhammad Ismail;Rachad Atat;Erchin Serpedin","doi":"10.1109/TAI.2024.3464511","DOIUrl":"https://doi.org/10.1109/TAI.2024.3464511","url":null,"abstract":"Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? 2) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? 3) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-temporal features leads to 5%–26% and 2%–5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3%–11% higher degradation in DR compared to a random selection; 2) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3%–13% higher degradation in DR compared to traditional FDIAs; and 3) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5\u0000<inline-formula><tex-math>$-$</tex-math></inline-formula>\u000053% compared to benchmark detectors against FDIEAs.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6601-6616"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825896","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

NPE-DRL: Enhancing Perception Constrained Obstacle Avoidance With Nonexpert Policy Guided Reinforcement Learning NPE-DRL：用非专家策略引导的强化学习增强感知约束的避障

IEEE transactions on artificial intelligence Pub Date : 2024-09-20 DOI: 10.1109/TAI.2024.3464510

Yuhang Zhang;Chao Yan;Jiaping Xiao;Mir Feroskhan

{"title":"NPE-DRL: Enhancing Perception Constrained Obstacle Avoidance With Nonexpert Policy Guided Reinforcement Learning","authors":"Yuhang Zhang;Chao Yan;Jiaping Xiao;Mir Feroskhan","doi":"10.1109/TAI.2024.3464510","DOIUrl":"https://doi.org/10.1109/TAI.2024.3464510","url":null,"abstract":"Obstacle avoidance under constrained visual perception presents a significant challenge, requiring rapid detection and decision-making within partially observable environments, particularly for unmanned aerial vehicles (UAVs) maneuvering agilely in 3-D space. Compared with traditional methods, obstacle avoidance algorithms based on deep reinforcement learning (DRL) offer a better comprehension of the uncertain operational environment in an end-to-end manner, reducing computational complexity, and enhancing flexibility and scalability. However, the inherent trial-and-error learning mechanism of DRL necessitates numerous iterations for policy convergence, leading to sample inefficiency issues. Meanwhile, existing sample-efficient obstacle avoidance approaches that leverage imitation learning often heavily rely on offline expert demonstrations, which are not always feasible in hazardous environments. To address these challenges, we propose a novel obstacle avoidance approach based on nonexpert policy enhanced DRL (NPE-DRL). This approach integrates a fundamental DRL framework with prior knowledge derived from a nonexpert policy-guided imitation learning. During the training phase, the agent starts by online imitating the actions generated by the nonexpert policy during interactions and progressively shifts toward autonomously exploring the environment to generate the optimal policy. Both simulation and physical experiments validate that our approach improves sample efficiency and achieves a better exploration–exploitation balance in both virtual and real-world flights. Additionally, our NPE-DRL-based obstacle avoidance approach shows better adaptability in complex environments characterized by larger scales and denser obstacle configurations, demonstrating a significant improvement in UAVs’ obstacle avoidance capability. Code available at <uri>https://github.com/zzzzzyh111/NonExpert-Guided-Visual-UAV-Navigation-Gazebo</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"184-198"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976089","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