IEEE transactions on artificial intelligence最新文献

{"title":"Unsupervised Domain Adaptation on Point Clouds via High-Order Geometric Structure Modeling","authors":"Jiang-Xing Cheng;Huibin Lin;Chun-Yang Zhang;C. L. Philip Chen","doi":"10.1109/TAI.2024.3483199","DOIUrl":"https://doi.org/10.1109/TAI.2024.3483199","url":null,"abstract":"Point clouds can capture the precise geometric information of objects and scenes, which are an important source of 3-D data and one of the most popular 3-D geometric data structures for cognitions in many real-world applications like automatic driving and remote sensing. However, due to the influence of sensors and varieties of objects, the point clouds obtained by different devices may suffer obvious geometric changes, resulting in domain gaps that are prone to the neural networks trained in one domain failing to preserve the performance in other domains. To alleviate the above problem, this article proposes an unsupervised domain adaptation framework, named HO-GSM, as the first attempt to model high-order geometric structures of point clouds. First, we construct multiple self-supervised tasks to learn the invariant semantic and geometric features of the source and target domains, especially to capture the feature invariance of high-order geometric structures of point clouds. Second, the discriminative feature space of target domain is acquired by using contrastive learning to refine domain alignment to specific class level. Experiments on the PointDA-10 and GraspNetPC-10 collection of datasets show that the proposed HO-GSM can significantly outperform the state-of-the-art counterparts.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6121-6133"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810191","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":"Personalized Learning Path Problem Variations: Computational Complexity and AI Approaches","authors":"Sean A. Mochocki;Mark G. Reith;Brett J. Borghetti;Gilbert L. Peterson;John D. Jasper;Laurence D. Merkle","doi":"10.1109/TAI.2024.3483190","DOIUrl":"https://doi.org/10.1109/TAI.2024.3483190","url":null,"abstract":"E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use personalized learning paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs (KGs), student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the nondeterministic polynomial (NP)-hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of the PLP variations, researchers risk making invalid comparisons and conclusions when they use different metaheuristics for different PLP problems. To unify this conversation, this article formally proves the NP-completeness of two common PLP variations and their generalizations and uses them to categorize recent research in the PLP field. It then presents an instance of the PLP problem using real-world data and shows how this instance can be cast into two different NP-complete variations. This article then presents three artificial intelligence (AI) strategies, solving one of the PLP variations with back-tracking and branch and bound heuristics and also converting the PLP variation instance to XCSP<inline-formula><tex-math>${}^{3}$</tex-math></inline-formula>, an intermediate constraint satisfaction language to be resolved with a general constraint optimization solver. This article solves the other PLP variation instance using a greedy search heuristic. The article finishes by comparing the results of the two different PLP variations.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"574-588"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10722910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583176","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":"Prompt Learning for Few-Shot Question Answering via Self-Context Data Augmentation","authors":"Jian-Qiang Qiu;Chun-Yang Zhang;C. L. Philip Chen","doi":"10.1109/TAI.2024.3483201","DOIUrl":"https://doi.org/10.1109/TAI.2024.3483201","url":null,"abstract":"Pretrained language models (PLMs) have shown remarkable performance on question answering (QA) tasks, but they usually require fine-tuning (FT) that depends on a substantial quantity of QA pairs. Therefore, improving the performance of PLMs in scenarios with only a small number of training examples, also known as a few-shot setting, is of great practical significance. Current mitigation strategies for the few-shot QA task largely rely on pretraining a QA task-specific language model from scratch, overlooking the potential of foundational PLMs to generate QA pairs, particularly in the few-shot setting. To address this issue, we propose a prompt-based QA data augmentation method aimed at automating the creation of high-quality QA pairs. It employs the PFT method, adapting the question generation process of PLMs to the few-shot setting. Additionally, we introduce a dynamic text filling training strategy. This strategy simulates the progressive human learning process, thereby alleviating overfitting of PLMs in the few-shot setting and enhancing their reasoning capability to tackle complex questions. Extensive experiments demonstrate that the proposed method outperforms existing approaches across various few-shot configurations.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"589-603"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583178","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":"IEEE Transactions on Artificial Intelligence Publication Information","authors":"","doi":"10.1109/TAI.2024.3470571","DOIUrl":"https://doi.org/10.1109/TAI.2024.3470571","url":null,"abstract":"","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 10","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442996","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":"Efficient Evaluation Methods for Neural Architecture Search: A Survey","authors":"Xiaotian Song;Xiangning Xie;Zeqiong Lv;Gary G. Yen;Weiping Ding;Jiancheng Lv;Yanan Sun","doi":"10.1109/TAI.2024.3477457","DOIUrl":"https://doi.org/10.1109/TAI.2024.3477457","url":null,"abstract":"Neural architecture search (NAS) has received increasing attention because of its exceptional merits in automating the design of deep neural network (DNN) architectures. However, the performance evaluation process, as a key part of NAS, often requires training a large number of DNNs. This inevitably makes NAS computationally expensive. In past years, many efficient evaluation methods (EEMs) have been proposed to address this critical issue. In this article, we comprehensively survey these EEMs published up to date, and provide a detailed analysis to motivate the further development of this research direction. Specifically, we divide the existing EEMs into four categories based on the number of DNNs trained for constructing these EEMs. The categorization can reflect the degree of efficiency in principle, which can in turn help quickly grasp the methodological features. In surveying each category, we further discuss the design principles and analyze the strengths and weaknesses to clarify the landscape of existing EEMs, thus making easily understanding the research trends of EEMs. Furthermore, we also discuss the current challenges and issues to identify future research directions in this emerging topic. In summary, this survey provides a convenient overview of EEM for interested users, and they can easily select the proper EEM method for the tasks at hand. In addition, the researchers in the NAS field could continue exploring the future directions suggested in the article.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"5990-6011"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810406","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 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":"Learning Neural Network Classifiers by Distributing Nearest Neighbors on Adaptive Hypersphere","authors":"Xiaojing Zhang;Shuangrong Liu;Lin Wang;Bo Yang;Jiawei Fan","doi":"10.1109/TAI.2024.3477436","DOIUrl":"https://doi.org/10.1109/TAI.2024.3477436","url":null,"abstract":"In this study, the adaptive hypersphere nearest neighbors (ASNN) method is proposed as an optimization framework to enhance the generalization performance of neural network classifiers. In terms of the classification task, the neural network draws decision boundaries by constructing the discriminative features of samples. To learn those features, attributed to the flexibility and separability, the pair-wise constraint-based methods that consist of the pair-wise loss and an embedding space (e.g., hypersphere space) have gained considerable attention over the last decade. Despite their success, pair-wise constraint-based methods still suffer from premature convergence or divergence problems, driven by two main challenges. 1) The poor scalability of the embedding space constrains the variety of the distribution of embedded samples, thereby increasing the optimization difficulty. 2) It is hard to select suitable positive/negative pairs during the training. In order to address the aforementioned problems, we propose an adaptive hypersphere nearest neighbors method. On the one hand, we improve the scalability of features via a scale-adaptive hypersphere embedding space. On the other hand, we introduce a neighborhood-based probability loss, which magnifies the difference between pairs and enhances the discriminative power of features generated by the neural networks based on the nearest neighbor-based pairing strategy. Experiments on UCI datasets and image recognition tasks demonstrate that the proposed ASNN not only achieves improved intraclass consistency and interclass separability of samples, but also outperforms its competitive counterparts.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"234-249"},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976088","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":"Efficient CORDIC-Based Activation Functions for RNN Acceleration on FPGAs","authors":"Wan Shen;Junye Jiang;Minghan Li;Shuanglong Liu","doi":"10.1109/TAI.2024.3474648","DOIUrl":"https://doi.org/10.1109/TAI.2024.3474648","url":null,"abstract":"Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks, have emerged as standard tools for tackling a wide range of time series applications, such as natural language processing. However, deploying these models on edge devices presents great challenges due to limited computational resources. Additionally, the implementation of RNN activation functions on low-end hardware devices significantly impacts the overall network performance, as activations constitute the dominant part of execution time. In this work, we propose an efficient approach for implementing commonly used RNN activations, leveraging an optimized coordinate rotation digital computer algorithm (CORDIC). Moreover, we propose a unified hardware architecture for mapping the CORDIC-based method onto field-programmable gate arrays (FPGAs), which can be configured to implement multiple nonlinear activation functions. Our architecture reduces the computational time with fewer iterations in CORDIC compared with existing methods, rendering it particularly suitable for resource-constrained edge devices. Our design is implemented on a Xilinx Zynq-7000 device and evaluated across three RNNs and benchmark datasets. Experimental results demonstrate that our design achieves up to a 2<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula> speedup while maintaining model accuracy compared with the state-of-the-art designs.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"199-210"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975727","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}