IEEE transactions on artificial intelligence最新文献

{"title":"A Comprehensive Exploration of Real-Time 3-D View Reconstruction Methods","authors":"Arya Agrawal;Teena Sharma;Nishchal K. Verma","doi":"10.1109/TAI.2024.3477425","DOIUrl":"https://doi.org/10.1109/TAI.2024.3477425","url":null,"abstract":"Real-time 3-D view reconstruction in an unfamiliar environment poses complexity for various applications due to varying conditions such as occlusion, latency, precision, etc. This article thoroughly examines and tests contemporary methodologies addressing challenges in 3-D view reconstruction. The methods being explored in this article are categorized into volumetric and mesh, generative adversarial network based, and open source library based methods. The exploration of these methods undergoes detailed discussions, encompassing methods, advantages, limitations, and empirical results. The real-time testing of each method is done on benchmarked datasets, including ShapeNet, Pascal 3D+, Pix3D, etc. The narrative highlights the crucial role of 3-D view reconstruction in domains such as robotics, virtual and augmented reality, medical imaging, cultural heritage preservation, etc. The article also anticipates future scopes by exploring generative models, unsupervised learning, and advanced sensor fusion to increase the robustness of the algorithms.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"5915-5927"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810371","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}

{"title":"Simplified Kernel-Based Cost-Sensitive Broad Learning System for Imbalanced Fault Diagnosis","authors":"Kaixiang Yang;Wuxing Chen;Yifan Shi;Zhiwen Yu;C. L. Philip Chen","doi":"10.1109/TAI.2024.3478191","DOIUrl":"https://doi.org/10.1109/TAI.2024.3478191","url":null,"abstract":"In the field of intelligent manufacturing, tackling the classification challenges caused by imbalanced data is crucial. Although the broad learning system (BLS) has been recognized as an effective and efficient method, its performance wanes with imbalanced datasets. Therefore, this article proposes a novel approach named simplified kernel-based cost-sensitive broad learning system (SKCSBLS) to address these issues. Based on the framework of cost-sensitive broad learning system (CSBLS) that assigns distinctive adjustment costs for individual classes, SKCSBLS emphasizes the importance of the minority class while mitigating the impact of data imbalance. Additionally, considering the complexity introduced by noisy or overlapping data points, we incorporate kernel mapping into the CSBLS. This improvement not only improves the system's capability to handle overlapping classes of samples, but also improves the overall classification effectiveness. Our experimental results highlight the potential of SKCSBLS in overcoming the challenges inherent in unbalanced data, providing a robust solution for advanced fault diagnosis in intelligent systems.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6629-6644"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825950","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}

{"title":"Partial Domain Adaptation for Building Borehole Lithology Model Under Weaker Geological Prior","authors":"Jing Li;Jichen Wang;Zerui Li;Yu Kang;Wenjun Lv","doi":"10.1109/TAI.2024.3476434","DOIUrl":"https://doi.org/10.1109/TAI.2024.3476434","url":null,"abstract":"Lithology identification plays a pivotal role in stratigraphic characterization and reservoir exploration. The promising field of intelligent logging lithology identification, which employs machine learning algorithms to infer lithology from logging curves, is gaining significant attention. However, models trained on labeled wells currently face challenges in accurately predicting the lithologies of new unlabeled wells due to significant discrepancies in data distribution among different wells caused by the complex sedimentary environment and variations in logging equipment. Additionally, there is no guarantee that newly drilled wells share the same lithology classes as previously explored ones. Therefore, our research aims to leverage source logging and lithology data along with target logging data to train a model capable of directly discerning the lithologies of target wells. The challenges are centered around the disparities in data distribution and the lack of prior knowledge regarding potential lithology classes in the target well. To tackle these concerns, we have made concerted efforts: 1) proposing a novel lithology identification framework, sample transferability weighting based partial domain adaptation (ST-PDA), to effectively address the practical scenario of encountering an unknown label space in target wells; 2) designing a sample transferability weighting module to assign higher weights to shared-class samples, thus effectively mitigating the negative transfer caused by unshared-class source samples; 3) developing a module, convolutional neural network with integrated channel attention mechanism (CG\u0000<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>\u0000CA), to serve as the backbone network for feature extraction; and 4) incorporating a target sample reconstruction module to enhance the feature representation and further facilitating positive transfer. Extensive experiments on 16 real-world wells demonstrated the strong performance of ST-PDA and highlighted the necessity of each component in the framework.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6645-6658"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825811","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}

{"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}

{"title":"Optimal Control of Stochastic Markovian Jump Systems With Wiener and Poisson Noises: Two Reinforcement Learning Approaches","authors":"Zhiguo Yan;Tingkun Sun;Guolin Hu","doi":"10.1109/TAI.2024.3471729","DOIUrl":"https://doi.org/10.1109/TAI.2024.3471729","url":null,"abstract":"This article investigates the infinite horizon optimal control problem for stochastic Markovian jump systems with Wiener and Poisson noises. First, a new policy iteration algorithm is designed by using integral reinforcement learning approach and subsystems transformation technique, which obtains the optimal solution without solving stochastic coupled algebraic Riccati equation (SCARE) directly. Second, through the transformation and substitution of the SCARE and feedback gain matrix, a policy iteration algorithm is devised to determine the optimal control strategy. This algorithm leverages only state trajectory information to obtain the optimal solution, and is updated in an unfixed form. Additionally, the algorithm remains unaffected by variations in Poisson jump intensity. Finally, an numerical example is given to verify the effectiveness and convergence of the proposed algorithms.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6591-6600"},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825813","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}

{"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}

{"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}

{"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}

{"title":"Reinforcement Learning for Solving Colored Traveling Salesman Problems: An Entropy-Insensitive Attention Approach","authors":"Tianyu Zhu;Xinli Shi;Xiangping Xu;Jinde Cao","doi":"10.1109/TAI.2024.3461630","DOIUrl":"https://doi.org/10.1109/TAI.2024.3461630","url":null,"abstract":"The utilization of neural network models for solving combinatorial optimization problems (COPs) has gained significant attention in recent years and has demonstrated encouraging outcomes in addressing analogous problems such as the traveling salesman problem (TSP). The multiple TSP (MTSP) has sparked the interest of researchers as a special kind of COPs. The colored TSP (CTSP) is a variation of the MTSP, which utilizes colors to distinguish the accessibility of cities to salesmen. This article proposes a gated entropy-insensitive attention model (GEIAM) to solve CTSP. In specific, the original problem is first modeled as a sequence and preprocessed by the problem feature extraction network of the model, and then solved by the autoregressive solution constructor subsequently. The policy (parameters of the neural network model) is trained via reinforcement learning (RL). The proposed approach is compared with several commercial solvers as well as heuristics and demonstrates superior solving speed with comparable solution quality.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6699-6708"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825899","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}

{"title":"Self-Model-Free Learning Versus Learning With External Rewards in Information Constrained Environments","authors":"Prachi Pratyusha Sahoo;Kyriakos G. Vamvoudakis","doi":"10.1109/TAI.2024.3433614","DOIUrl":"https://doi.org/10.1109/TAI.2024.3433614","url":null,"abstract":"In this article, we provide a model-free reinforcement learning (RL) framework that relies on internal reinforcement signals, called self-model-free RL, for learning agents that experience loss of the reinforcement signals in the form of packet drops and/or jamming attacks by malicious agents. The framework embeds a correcting mechanism in the form of a goal network to compensate for information loss and produce optimal and stabilizing policies. It also provides a trade-off scheme that reconstructs the reward using a goal network whenever the reinforcement signals are lost but utilizes true reinforcement signals when they are available. The stability of the equilibrium point is guaranteed despite fractional information loss in the reinforcement signals. Finally, simulation results validate the efficacy of the proposed work.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6566-6579"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825897","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}