IEEE transactions on artificial intelligence最新文献

{"title":"Similar or Related: Spectral-Based Item Relationship Mining With Graph Convolutional Network for Complementary Recommendation","authors":"Gang-Feng Ma;Xu-Hua Yang;Haixia Long;Yujiao Huang","doi":"10.1109/TAI.2025.3543820","DOIUrl":"https://doi.org/10.1109/TAI.2025.3543820","url":null,"abstract":"Complementary recommendation, which aims to recommend frequently copurchased items to users, has gained significant attention. Unlike traditional similarity-based recommendations, complementary recommendation focus on items that are related but not necessarily similar (e.g., computers and keyboards), that aligns with users’ purchasing habits. However, most of current complementary recommendation systems fail to effectively differentiate or measure these two types of relationships. In this article, we propose similar or related: spectral-based item relationship mining with graph convolutional network for complementary recommendation (SR-Rec). First, we design two spectral-based filters to fully mine the similarity and relevance information of items, thereby achieving effective discrimination between the two types of relationships. Then, we compute similarity and relevance scores between items separately, and employ a pairwise self-attention mechanism to measure the impact of these relationships on the final recommendations. Experimental results on three public open-source datasets demonstrate that SR-Rec outperforms state-of-the-art performance in complementary recommendation.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2193-2202"},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750898","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":"AI-Generated Versus Human Text: Introducing a New Dataset for Benchmarking and Analysis","authors":"Ali Al Bataineh;Rachel Sickler;Kerry Kurcz;Kristen Pedersen","doi":"10.1109/TAI.2025.3544183","DOIUrl":"https://doi.org/10.1109/TAI.2025.3544183","url":null,"abstract":"Artificial intelligence (AI) is increasingly embedded in our everyday lives. With the introduction of ChatGPT in November 2022 by OpenAI, people can now ask a bot to generate comprehensive writeups in seconds. This new transformative technology also introduces ethical, safety, and other general concerns. It is important to harness the power of AI to understand whether a body of text is generated by AI or whether it is organically human. In this article, we create and curate a medium-sized dataset of 10 000 records containing both human and machine-generated text and utilize it to train a reliable model to accurately distinguish between the two. First, we use DistilGPT-2 with various inputs to generate machine text. Then, we acquire an equal sample size of human-generated text. All the text is cleaned, explored, and visualized using the uniform manifold approximation and projection (UMAP) dimensionality reduction technique. Finally, the text is transformed into vectors using several techniques, including bag of words, term frequency-inverse document frequency, bidirectional encoder representations from transformer, and neural network-based embeddings. Machine learning experiments are then performed with traditional models such as logistic regression, random forest, and XGBoost, as well as deep learning models such as long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM. Across all vectorization strategies and machine learning algorithms, we measure accuracy, precision, recall, and F1 scores. We also time each exercise. Each model completes its training within an hour, and we observe scores above 90%. We then use the Shapley additive explanations (SHAP) package on machine learning models to explore if and how we can explain the model to further validate results. Lastly, we deploy our TF-IDF Random Forest model to a user-friendly web application using the Streamlit framework, allowing users without coding expertise to interact with the model.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2241-2252"},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10896944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixture Density Function Estimation in Shape Clustering","authors":"Kazunori Iwata","doi":"10.1109/TAI.2025.3543815","DOIUrl":"https://doi.org/10.1109/TAI.2025.3543815","url":null,"abstract":"Recent developments in measurement tools have made it easier to obtain shape data, a collection of point coordinates in vector space that are meaningful when some of them are gathered together. As a result, clustering of shape data becomes increasingly important. However, few studies still perform applicable clustering in various cases because some studies rely on their specific shape representations. Thus, we apply a simple and widely recognized representation and generative model to shape. A configuration matrix of the point coordinates is used for the representation, and it is the simplest and most well-accepted representation in conventional shape analysis. As a generative model, we consider the mixture density function, a well-known model in statistics for expressing a population density function, which is a linear combination of subpopulation density functions. The aim of this article is to present a mixture density-based model that will be useful for clustering shape data. The clustering of shapes involves estimating the parameters of the model, and this estimation is derived using an EM algorithm based on the model. As examples of promising shape-data applications, the computational analyses of ape skulls, American football formations, and baseball pitches were performed. In addition, we evaluated the performance of the EM algorithm by comparing it with other typical clustering methods. The theoretical results not only contribute to statistical estimation for shape data but also extend the clustering of nonvector shape data. The experimental results show that the derived EM algorithm performs well in shape clustering.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2178-2192"},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750844","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":"Unsupervised Multivariate Time Series Anomaly Detection by Feature Decoupling in Federated Learning Scenarios","authors":"Yifan He;Xi Ding;Yateng Tang;Jihong Guan;Shuigeng Zhou","doi":"10.1109/TAI.2025.3533437","DOIUrl":"https://doi.org/10.1109/TAI.2025.3533437","url":null,"abstract":"Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This article focuses on unsupervised anomaly detection from multivariate time series (MTS), as real-world data collected from sources such as wearable devices, medical equipment, and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce feature decoupling federated learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection (MTS-AD) algorithms with federated learning (FL) strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS-AD.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2013-2026"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750900","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":"Efficient Solution Validation of Constraint Satisfaction Problems on Neuromorphic Hardware: The Case of Sudoku Puzzles","authors":"Riccardo Pignari;Vittorio Fra;Enrico Macii;Gianvito Urgese","doi":"10.1109/TAI.2025.3536428","DOIUrl":"https://doi.org/10.1109/TAI.2025.3536428","url":null,"abstract":"Spiking neural networks (SNNs) offer an effective approach to solving constraint satisfaction problems (CSPs) by leveraging their temporal, event-driven dynamics. Moreover, neuromorphic hardware platforms provide the potential for achieving significant energy efficiency in implementing such models. Building upon these foundations, we present an enhanced, fully spiking pipeline for solving CSPs on the SpiNNaker neuromorphic hardware platform. Focusing on the use case of Sudoku puzzles, we demonstrate that the adoption of a constraint stabilization strategy, coupled with a neuron idling mechanism and a built-in validation process, enables this application to be realized through a series of additional layers of neurons capable of performing control logic operations, verifying solutions, and memorizing the network's state. Simulations conducted in the GPU-enhanced neuronal networks (GeNN) environment validate the contributions of each pipeline component before deployment on SpiNNaker. This approach offers three key advantages: 1) Improved success rates for solving CSPs, particularly for challenging instances from the hard class, surpassing state-of-the-art SNN-based solvers. 2) Reduced data transmission overhead by transmitting only the final activity state from SpiNNaker instead of all generated spikes. 3) Substantially decreased spike extraction time. Compared with previous work focused on the same use case, our approach achieves a significant reduction in the number of extracted spikes (54.63% to 99.98%) and extraction time (88.56% to 96.41%).","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2061-2072"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751074","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":"Path-Aware Few-Shot Knowledge Graph Completion","authors":"Shuo Yu;Yingbo Wang;Zhitao Wan;Yanming Shen;Qiang Zhang;Feng Xia","doi":"10.1109/TAI.2025.3540796","DOIUrl":"https://doi.org/10.1109/TAI.2025.3540796","url":null,"abstract":"Few-shot knowledge graph completion (FKGC) has emerged as a significant area of interest for addressing the long-tail problem in knowledge graphs. Traditional approaches often focus on the sparse few-shot neighborhood to derive semantic representation, overlooking other critical information forms such as relation paths. In this article, we introduce an innovative method, called PARE, which fully leverages relation paths to enhance the few-shot representation by simultaneously incorporating both neighborhood and relation path information. Inspired by the principles of information transmission, PARE directly models relation paths between entities and parameterizes the information interference within different relation paths. Through parameter learning, PARE effectively captures information propagation along relation paths while mitigating the influence of relation dependency. To preserve neighborhood information, we employ a two-step neighborhood aggregator to resolve few-shot neighbors’ ambiguity and develop a reconstruction module. By integrating the representations of relation paths and contextual neighborhoods, we achieve a comprehensive few-shot representation for two given entities. We utilize a matching processor for knowledge triplet evaluation. Extensive experiments demonstrate that our PARE model outperforms state-of-the-art baselines on widely-used benchmark datasets.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2133-2147"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750866","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":"High-Dimensional Hyperparameter Optimization via Adjoint Differentiation","authors":"Hongkun Dou;Hongjue Li;Jinyang Du;Leyuan Fang;Qing Gao;Yue Deng;Wen Yao","doi":"10.1109/TAI.2025.3540799","DOIUrl":"https://doi.org/10.1109/TAI.2025.3540799","url":null,"abstract":"As an emerging machine learning task, high-dimensional hyperparameter optimization (HO) aims at enhancing traditional deep learning models by simultaneously optimizing the neural networks’ weights and hyperparameters in a joint bilevel configuration. However, such nested objectives can impose nontrivial difficulties for the pursuit of the gradient of the validation risk with respect to the hyperparameters (a.k.a. hypergradient). To tackle this challenge, we revisit its bilevel objective from the novel perspective of continuous dynamics and then solve the whole HO problem with the adjoint state theory. The proposed HO framework, termed Adjoint Diff, is naturally scalable to a very deep neural network with high-dimensional hyperparameters because it only requires constant memory cost in training. Adjoint Diff is in fact, a general framework that some existing gradient-based HO algorithms are well interpreted by it with simple algebra. In addition, we further offer the Adjoint Diff+ framework by incorporating the prevalent momentum learning concept into the basic Adjoint Diff for enhanced convergence. Experimental results show that our Adjoint Diff frameworks outperform several state-of-the-art approaches on three high-dimensional HO instances including, designing a loss function for imbalanced data, selecting samples from noisy labels, and learning auxiliary tasks for fine-grained classification.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2148-2162"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751096","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":"Beyond Histogram Comparison: Distribution-Aware Simple-Path Graph Kernels","authors":"Wei Ye;Shuhao Tang;Hao Tian;Qijun Chen","doi":"10.1109/TAI.2025.3539642","DOIUrl":"https://doi.org/10.1109/TAI.2025.3539642","url":null,"abstract":"R-convolution graph kernels are conventional methods for graph classification. They decompose graphs into substructures and aggregate all the substructure similarity as graph similarity. However, the substructure similarity is based on graph isomorphism, which not only leads to binary similarity values but also cannot be aware of the probability distribution of substructures in each graph. Moreover, the simple sum aggregation is not aware of the probability distribution differences of substructures across graphs. These drawbacks cause inaccurate graph similarity. To resolve these problems, we propose a new method called the distribution-aware simple-path (DASP) graph kernel. The neural language models are employed to capture the probability distribution of substructures (specifically, simple paths) in each graph. A new metric called probabilistic Minkowski distance is developed to capture the probability distribution differences of simple paths across graphs. To further improve the performance, the label alphabet is expanded to enlarge the corpus of simple paths for the neural language models and DASP. Experiments demonstrate that DASP achieves the best classification accuracy on all the selected graph benchmark datasets.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2119-2132"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751094","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":"Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing","authors":"Ervin Moore;Ahmed Imteaj;Md Zarif Hossain;Shabnam Rezapour;M. Hadi Amini","doi":"10.1109/TAI.2025.3539258","DOIUrl":"https://doi.org/10.1109/TAI.2025.3539258","url":null,"abstract":"Federated learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant's data remains on the participant's devices and only the local model generated utilizing the local computational power is transmitted throughout the database. However, the distributed computational nature of FL creates the necessity to develop a mechanism that can remotely trigger any network agents, track their activities, and prevent threats to the overall process posed by malicious participants. Particularly, the FL paradigm may become vulnerable due to an active attack from the network participants, called a poisonous attack. In such an attack, the malicious participant acts as a benign agent capable of affecting the global model quality by uploading an obfuscated poisoned local model update to the server. This article presents a cross-device FL model that ensures trustworthiness, fairness, and authenticity in the underlying FL training process. We leverage trustworthiness by constructing a reputation-based trust model based on agents’ contributions toward model convergence. We ensure fairness by identifying and removing malicious agents from the training process through an outlier detection technique. Additionally, we establish authenticity by generating a token for each participating device through a distributed sensing mechanism and storing that unique token in a blockchain smart contract. Further, we insert the trust scores of all agents into a blockchain and validate their reputations using various consensus mechanisms that consider the computational task.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2110-2118"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751124","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":"A Novel Recursive Ensemble Feature Selection Framework for High-Dimensional Data","authors":"Xiaojian Ding;Zihan Xu;Yi Li;Fumin Ma;Shilin Chen","doi":"10.1109/TAI.2025.3538549","DOIUrl":"https://doi.org/10.1109/TAI.2025.3538549","url":null,"abstract":"Ensemble feature selection combines feature subsets with diversity, potentially providing a better approximation of the optimal feature subset. While extensive research has focused on enhancing diversity among ensemble members, its critical role during the aggregation process remains underexplored. To address this gap, we propose a novel Recursive Ensemble Feature Selection (REFS) framework that explicitly incorporates diversity into the aggregation phase to improve both robustness and accuracy. The framework comprises three key components: 1) a randomization-based feature mapping strategy (RS) to generate diverse base feature selectors optimized for performance; 2) a quantitative diversity metric (DM) to evaluate the complementarity of these selectors; and 3) a fuzzy aggregation (FA) method that leverages order statistics, rank scores, and weight information to effectively integrate multiple ranked feature lists. Experimental evaluations on fifteen real-world datasets demonstrate that REFS consistently outperforms competitive methods in terms of classification accuracy and resilience to parameter variations. By explicitly integrating diversity into the aggregation process, REFS provides a more comprehensive and effective approach to feature selection, paving the way for improved predictive performance across diverse applications.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2098-2109"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751095","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}