IEEE transactions on artificial intelligence最新文献_第2页

Behavioral Decision-Making of Mobile Robots Simulating the Functions of Cerebellum, Basal Ganglia, and Hippocampus 模拟小脑、基底神经节和海马体功能的移动机器人行为决策

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3534150

Dongshu Wang;Qi Liu;Yihai Duan

{"title":"Behavioral Decision-Making of Mobile Robots Simulating the Functions of Cerebellum, Basal Ganglia, and Hippocampus","authors":"Dongshu Wang;Qi Liu;Yihai Duan","doi":"10.1109/TAI.2025.3534150","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534150","url":null,"abstract":"In unknown environments, behavioral decision-making of mobile robots is a crucial research topic in the field of robotics applications. To address the low learning ability and the difficulty of learning from the unknown environments for mobile robots, this work proposes a new learning model that integrates the supervised learning of the cerebellum, reinforcement learning of the basal ganglia, and memory consolidation of the hippocampus. First, to reduce the impact of noise on inputs and enhance the network's efficiency, a multineuron winning strategy and the refinement of the top-<inline-formula><tex-math>$k$</tex-math></inline-formula> competition mechanism have been adopted. Second, to increase the network's learning speed, a negative learning mechanism has been designed, which allows the robot to avoid obstacles more quickly by weakening the synaptic connections between error neurons. Third, to enhance the decision ability of cerebellar supervised learning, simulating the hippocampal memory consolidation mechanism, memory replay during the agent's offline state enables autonomous learning in the absence of real-time interactions. Finally, to better adjust the roles of cerebellar supervised learning and basal ganglia reinforcement learning in robot behavioral decision-making, a new similarity indicator has been designed. Simulation experiments and real-world experiments validate the effectiveness of the proposed model in this work.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1639-1650"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196570","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

FLyer: Federated Learning-Based Crop Yield Prediction for Agriculture 5.0 传单：基于联邦学习的农业作物产量预测5.0

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3534149

Tanushree Dey;Somnath Bera;Anwesha Mukherjee;Debashis De;Rajkumar Buyya

{"title":"FLyer: Federated Learning-Based Crop Yield Prediction for Agriculture 5.0","authors":"Tanushree Dey;Somnath Bera;Anwesha Mukherjee;Debashis De;Rajkumar Buyya","doi":"10.1109/TAI.2025.3534149","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534149","url":null,"abstract":"Crop yield prediction is a significant area of precision agriculture. In this article, we propose a crop yield prediction framework named FLyer, based on federated learning and edge computing. In FLyer, the soil and environmental data are locally processed inside the edge servers, and the model parameters are transmitted between the edge servers and the cloud with encrypted gradients. LSTM is used as the local and global models for data analysis. As the LSTM model can capture the temporal dependencies and hold the sequential nature of the data, we use LSTM in FLyer. By encrypting the gradients, the gradient information leakage ratio is reduced, and data privacy is protected. For gradient encryption, we use AES-256, and for data encryption during local storage we use RSA and AES-256. The results demonstrate that FLyer diminishes the latency by <inline-formula><tex-math>$boldsymbol{sim}$</tex-math></inline-formula>39% and energy consumption by <inline-formula><tex-math>$boldsymbol{sim}$</tex-math></inline-formula>40% than the conventional edge-cloud framework respectively. The experimental results show that the global model in FLyer achieves above 99% accuracy, precision, recall, and F1-score in crop yield prediction. The results also present that the local models also achieve <inline-formula><tex-math>$boldsymbol{>}$</tex-math></inline-formula>94% accuracy in crop yield prediction.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1943-1952"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519354","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

t-SNVAE: Deep Probabilistic Learning With Local and Global Structures for Industrial Process Monitoring t-SNVAE：用于工业过程监控的局部和全局结构深度概率学习

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3533438

Jian Huang;Zizhuo Liu;Xu Yang;Yupeng Liu;Zhaomin Lv;Kaixiang Peng;Okan K. Ersoy

{"title":"t-SNVAE: Deep Probabilistic Learning With Local and Global Structures for Industrial Process Monitoring","authors":"Jian Huang;Zizhuo Liu;Xu Yang;Yupeng Liu;Zhaomin Lv;Kaixiang Peng;Okan K. Ersoy","doi":"10.1109/TAI.2025.3533438","DOIUrl":"https://doi.org/10.1109/TAI.2025.3533438","url":null,"abstract":"Variational autoencoder (VAE) is a generative deep learning (DL) model with a probabilistic structure, which makes it tolerant to process uncertainties and more suitable for process monitoring. However, the probabilistic model may disrupt the topological structure of data and lead to the loss of neighborhood information. To address this issue, a process monitoring approach based on t-distributed stochastic neighbor variational autoencoder (t-SNVAE) is proposed to capture probabilistic features that elucidate both local and global structures within the raw data. Specifically, the distances between neighboring data points are transformed into joint probabilities by using t-SN embedding. Through minimizing the Kullback–Leibler divergence of joint probabilities between the original data and the reconstructed data, VAE learns Gaussian features containing both local and global neighborhood information. Finally, monitoring statistics are constructed for monitoring. The efficiency of the proposed approach is verified on a multiphase flow facility and a waste-water treatment process.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1603-1613"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196916","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

Learning From Mistakes: A Multilevel Optimization Framework 从错误中学习：多层优化框架

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3534151

Li Zhang;Bhanu Garg;Pradyumna Sridhara;Ramtin Hosseini;Pengtao Xie

{"title":"Learning From Mistakes: A Multilevel Optimization Framework","authors":"Li Zhang;Bhanu Garg;Pradyumna Sridhara;Ramtin Hosseini;Pengtao Xie","doi":"10.1109/TAI.2025.3534151","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534151","url":null,"abstract":"Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data reweighting, etc. However, most of these methods do not factor in variations in learning difficulty, which limits their performance in real-world applications. To address the above problems, we propose a framework that imitates the learning process of humans. In human learning, learners usually focus more on the topics where mistakes have been made in the past to deepen their understanding and master the knowledge. Inspired by this effective human learning technique, we propose a multilevel optimization framework, learning from mistakes (LFM), for machine learning. We formulate LFM as a three-stage optimization problem: 1) the learner learns, 2) the learner relearns based on the mistakes made before, and 3) the learner validates his learning. We develop an efficient algorithm to solve the optimization problem. We further apply our method to differentiable neural architecture search and data reweighting. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and other related datasets powerfully demonstrate the effectiveness of our approach. The code of LFM is available at: <uri>https://github.com/importZL/LFM</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1651-1663"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196873","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

SpikeNAS-Bench: Benchmarking NAS Algorithms for Spiking Neural Network Architecture SpikeNAS-Bench：对峰值神经网络架构的NAS算法进行基准测试

IEEE transactions on artificial intelligence Pub Date : 2025-01-27 DOI: 10.1109/TAI.2025.3534136

Gengchen Sun;Zhengkun Liu;Lin Gan;Hang Su;Ting Li;Wenfeng Zhao;Biao Sun

{"title":"SpikeNAS-Bench: Benchmarking NAS Algorithms for Spiking Neural Network Architecture","authors":"Gengchen Sun;Zhengkun Liu;Lin Gan;Hang Su;Ting Li;Wenfeng Zhao;Biao Sun","doi":"10.1109/TAI.2025.3534136","DOIUrl":"https://doi.org/10.1109/TAI.2025.3534136","url":null,"abstract":"In recent years, neural architecture search (NAS) has marked significant advancements, yet its efficacy is marred by the dependence on substantial computational resources. To mitigate this, the development of NAS benchmarks has emerged, offering datasets that enumerate all potential network architectures and their performances within a predefined search space. Nonetheless, these benchmarks predominantly focus on convolutional architectures, which are criticized for their limited interpretability and suboptimal hardware efficiency. Recognizing the untapped potential of spiking neural networks (SNNs)—often hailed as the third generation of neural networks due to their biological realism and computational thrift—this study introduces SpikeNAS-Bench. As a pioneering benchmark for SNN, SpikeNAS-Bench utilizes a cell-based search space, integrating leaky integrate-and-fire neurons with variable thresholds as candidate operations. It encompasses 15 625 candidate architectures, rigorously evaluated on CIFAR10, CIFAR100, and Tiny-ImageNet datasets. This article delves into the architectural nuances of SpikeNAS-Bench, leveraging various criteria to underscore the benchmark's utility and presenting insights that could steer future NAS algorithm designs. Moreover, we assess the benchmark's consistency through three distinct proxy types: zero-cost-based, early-stop-based, and predictor-based proxies. Additionally, the article benchmarks seven contemporary NAS algorithms to attest to SpikeNAS-Bench's broad applicability. We commit to providing training logs, diagnostic data for all candidate architectures, and we promise to release all code and datasets postacceptance, aiming to catalyze further exploration and innovation within the SNN domain.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 6","pages":"1614-1625"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196918","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

Privacy and Fairness in Machine Learning: A Survey 机器学习中的隐私和公平：一项调查

IEEE transactions on artificial intelligence Pub Date : 2025-01-22 DOI: 10.1109/TAI.2025.3531326

Sina Shaham;Arash Hajisafi;Minh K. Quan;Dinh C. Nguyen;Bhaskar Krishnamachari;Charith Peris;Gabriel Ghinita;Cyrus Shahabi;Pubudu N. Pathirana

{"title":"Privacy and Fairness in Machine Learning: A Survey","authors":"Sina Shaham;Arash Hajisafi;Minh K. Quan;Dinh C. Nguyen;Bhaskar Krishnamachari;Charith Peris;Gabriel Ghinita;Cyrus Shahabi;Pubudu N. Pathirana","doi":"10.1109/TAI.2025.3531326","DOIUrl":"https://doi.org/10.1109/TAI.2025.3531326","url":null,"abstract":"Privacy and fairness are two crucial pillars of responsible artificial intelligence (AI) and trustworthy machine learning (ML). Each objective has been independently studied in the literature with the aim of reducing utility loss in achieving them. Despite the significant interest attracted from both academia and industry, there remains an immediate demand for more in-depth research to unravel how these two objectives can be simultaneously integrated into ML models. As opposed to well-accepted trade-offs, i.e., privacy-utility and fairness-utility, the interrelation between privacy and fairness is not well-understood. While some works suggest a trade-off between the two objective functions, there are others that demonstrate the alignment of these functions in certain scenarios. To fill this research gap, we provide a thorough review of privacy and fairness in ML, including supervised, unsupervised, semisupervised, and reinforcement learning. After examining and consolidating the literature on both objectives, we present a holistic survey on the impact of privacy on fairness, the impact of fairness on privacy, existing architectures, their interaction in application domains, and algorithms that aim to achieve both objectives while minimizing the utility sacrificed. Finally, we identify research challenges in achieving concurrently privacy and fairness in ML, particularly focusing on large language models.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1706-1726"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519301","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

Deep Learning-Based Selective Feature Fusion for Litchi Fruit Detection Using Multimodal UAV Sensor Measurements 基于深度学习的多模态无人机荔枝果检测选择特征融合

IEEE transactions on artificial intelligence Pub Date : 2025-01-22 DOI: 10.1109/TAI.2025.3532205

Debarun Chakraborty;Bhabesh Deka

{"title":"Deep Learning-Based Selective Feature Fusion for Litchi Fruit Detection Using Multimodal UAV Sensor Measurements","authors":"Debarun Chakraborty;Bhabesh Deka","doi":"10.1109/TAI.2025.3532205","DOIUrl":"https://doi.org/10.1109/TAI.2025.3532205","url":null,"abstract":"In the field of precision agriculture, accurate crop detection is crucial for crop yield estimation, and health monitoring using photogrammetric measurements. Achieving high precision requires advance object detection models and multiscale feature fusion. This article addresses key research gaps in litchi crop monitoring, including the lack of a suitable dataset for litchi detection in natural environment and the limitations of conventional deep learning models in handling challenges such as occlusion, overlapping, and background complexities. First, we prepare high-resolution litchi dataset called “UAVLitchi” of 5000 images that include both RGB and multispectral images and next, we propose a selective feature fusion (SFF)-based architecture for litchi detection. By utilizing both RGB and multispectral images, this architecture effectively mitigates the challenges of visual detection arising from the complex cluster growth structure of litchis, offering a robust solution for accurate detection. The integration of SFF within a dual-channel mask-region based convolutional neural network (Mask-RCNN) leading to significant improvements in feature extraction for litchi detection. Experimental results demonstrate impressive performance, achieving an mean average precession (mAP50) of 94.65%, mAP75 of 89.23%, recall of 90.16%, and F1-score of 91.44%.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1932-1942"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519353","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

Positive Sample Mining: Fuzzy Threshold-Based Contrastive Learning for Enhanced Unsupervised Skeleton-Based Action Recognition 正样本挖掘：基于模糊阈值的增强无监督骨架动作识别的对比学习

IEEE transactions on artificial intelligence Pub Date : 2025-01-21 DOI: 10.1109/TAI.2025.3531831

Hengsheng Xu;Jianqi Zhong;Deliang Lian;Hanxu Hou;Wenming Cao

{"title":"Positive Sample Mining: Fuzzy Threshold-Based Contrastive Learning for Enhanced Unsupervised Skeleton-Based Action Recognition","authors":"Hengsheng Xu;Jianqi Zhong;Deliang Lian;Hanxu Hou;Wenming Cao","doi":"10.1109/TAI.2025.3531831","DOIUrl":"https://doi.org/10.1109/TAI.2025.3531831","url":null,"abstract":"Contrastive learning is one of the fundamental paradigms for unsupervised 3-D skeleton-based action recognition. Existing contrastive learning paradigms typically enhance model discrimination by increasing the distance between different action samples in the feature space. However, this approach can inadvertently lead to an increase in the intraclass distance for the same action category, thereby affecting the effectiveness of action recognition. To address this issue, we introduce an innovative unsupervised framework named fuzzy threshold-based contrastive learning (FTCL). This novel approach leverages the concept of fuzzy thresholds to handle sample partitioning within the feature space. In essence, given a dataset of human actions, we distinguish different action samples as “negative samples” and identical action samples as “positive samples.” By analyzing the similarity distribution between these positive and negative samples, we apply the principles of fuzzy thresholds to evaluate the attributes of the negative samples. This refined evaluation facilitates a judicious reassignment of positive and negative sample classifications, thus circumventing the challenges associated with increased intraclass distances. Furthermore, to obtain better action representations from skeleton data, we model and contrast skeleton data from different spatiotemporal perspectives, capturing rich spatiotemporal information in the feature representation of actions. Extensive experiments on the NTU-60, NTU-120, and PKU-MMD datasets were conducted to validate our proposed FTCL. The experimental results demonstrate that our approach achieves significant improvements.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1918-1931"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519248","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

Robust Control of Uncertain Quantum Systems Based on Physics-Informed Neural Networks and Sampling Learning 基于物理信息神经网络和采样学习的不确定量子系统鲁棒控制

IEEE transactions on artificial intelligence Pub Date : 2025-01-20 DOI: 10.1109/TAI.2025.3531330

Kai Zhang;Qi Yu;Sen Kuang

{"title":"Robust Control of Uncertain Quantum Systems Based on Physics-Informed Neural Networks and Sampling Learning","authors":"Kai Zhang;Qi Yu;Sen Kuang","doi":"10.1109/TAI.2025.3531330","DOIUrl":"https://doi.org/10.1109/TAI.2025.3531330","url":null,"abstract":"High-fidelity quantum control is one of the key elements in quantum computing and information processing. In view of possible inaccuracies in quantum system modeling and inevitable errors in control fields, the design of robust control fields is of great importance. In this article, we propose a neural network-based robust control strategy that incorporates physics-informed neural networks (PINNs) and sampling-based learning control techniques for uncertain closed and open quantum systems. We employ the gradient descent algorithm with momentum for the network training, where two methods including direct calculation and automatic differentiation are used to compute the gradient of the loss function with respect to network weights. The direct calculation method demonstrates the internal mechanism of the gradient computation, while the automatic differentiation technology is easier to utilize. We provide some guidelines for the parameter selection of the sampling learning algorithm in the PINN robust control scheme to ensure good control performance. In particular, for open quantum systems with uncertainties, we point out the necessity of fast control. Some simulation experiments are conducted on closed and open systems with uncertainties and the results show the effectiveness of the proposed PINN control scheme in achieving high-fidelity state transfer of uncertain quantum systems.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 7","pages":"1906-1917"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519473","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

2024 Index IEEE Transactions on Artificial Intelligence Vol. 5 2024 Index IEEE Transactions on Artificial Intelligence Vol.

IEEE transactions on artificial intelligence Pub Date : 2025-01-20 DOI: 10.1109/TAI.2025.3531741

引用次数: 0