IEEE transactions on artificial intelligence最新文献_第10页

Generative Representation Learning in Recurrent Neural Networks for Causal Timeseries Forecasting 递归神经网络在因果时间序列预测中的生成表示学习

IEEE transactions on artificial intelligence Pub Date : 2024-08-20 DOI: 10.1109/TAI.2024.3446465

Georgios Chatziparaskevas;Ioannis Mademlis;Ioannis Pitas

{"title":"Generative Representation Learning in Recurrent Neural Networks for Causal Timeseries Forecasting","authors":"Georgios Chatziparaskevas;Ioannis Mademlis;Ioannis Pitas","doi":"10.1109/TAI.2024.3446465","DOIUrl":"https://doi.org/10.1109/TAI.2024.3446465","url":null,"abstract":"Feed-forward deep neural networks (DNNs) are the state of the art in timeseries forecasting. A particularly significant scenario is the causal one: when an arbitrary subset of variables of a given multivariate timeseries is specified as forecasting target, with the remaining ones (exogenous variables) \u0000<italic>causing</i>\u0000 the target at each time instance. Then, the goal is to predict a temporal window of future target values, given a window of historical exogenous values. To this end, this article proposes a novel deep recurrent neural architecture, called generative-regressing recurrent neural network (GRRNN), which surpasses competing ones in causal forecasting evaluation metrics, by smartly combining generative learning and regression. During training, the generative module learns to synthesize historical target timeseries from historical exogenous inputs via conditional adversarial learning, thus internally encoding the input timeseries into semantically meaningful features. During a forward pass, these features are passed over as input to the regression module, which outputs the actual future target forecasts in a sequence-to-sequence fashion. Thus, the task of timeseries generation is synergistically combined with the task of timeseries forecasting, under an end-to-end multitask training setting. Methodologically, GRRNN contributes a novel augmentation of pure supervised learning, tailored to causal timeseries forecasting, which essentially forces the generative module to transform the historical exogenous timeseries to a more appropriate representation, before feeding it as input to the actual forecasting regressor. Extensive experimental evaluation on relevant public datasets obtained from disparate fields, ranging from air pollution data to sentiment analysis of social media posts, confirms that GRRNN achieves top performance in multistep long-term forecasting.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6412-6425"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810283","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

Neuro-Symbolic AI for Military Applications 用于军事应用的神经符号AI

IEEE transactions on artificial intelligence Pub Date : 2024-08-19 DOI: 10.1109/TAI.2024.3444746

Desta Haileselassie Hagos;Danda B. Rawat

{"title":"Neuro-Symbolic AI for Military Applications","authors":"Desta Haileselassie Hagos;Danda B. Rawat","doi":"10.1109/TAI.2024.3444746","DOIUrl":"https://doi.org/10.1109/TAI.2024.3444746","url":null,"abstract":"Artificial intelligence (AI) plays a significant role in enhancing the capabilities of defense systems, revolutionizing strategic decision-making, and shaping the future landscape of military operations. Neuro-Symbolic AI is an emerging approach that leverages and augments the strengths of neural networks and symbolic reasoning. These systems have the potential to be more impactful and flexible than traditional AI systems, making them well-suited for military applications. This article comprehensively explores the diverse dimensions and capabilities of Neuro-Symbolic AI, aiming to shed light on its potential applications in military contexts. We investigate its capacity to improve decision-making, automate complex intelligence analysis, and strengthen autonomous systems. We further explore its potential to solve complex tasks in various domains, in addition to its applications in military contexts. Through this exploration, we address ethical, strategic, and technical considerations crucial to the development and deployment of Neuro-Symbolic AI in military and civilian applications. Contributing to the growing body of research, this study represents a comprehensive exploration of the extensive possibilities offered by Neuro-Symbolic AI.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6012-6026"},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810182","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

Preference Prediction-Based Evolutionary Multiobjective Optimization for Gasoline Blending Scheduling 基于偏好预测的汽油调配调度进化多目标优化

IEEE transactions on artificial intelligence Pub Date : 2024-08-19 DOI: 10.1109/TAI.2024.3444736

Wenxuan Fang;Wei Du;Guo Yu;Renchu He;Yang Tang;Yaochu Jin

{"title":"Preference Prediction-Based Evolutionary Multiobjective Optimization for Gasoline Blending Scheduling","authors":"Wenxuan Fang;Wei Du;Guo Yu;Renchu He;Yang Tang;Yaochu Jin","doi":"10.1109/TAI.2024.3444736","DOIUrl":"https://doi.org/10.1109/TAI.2024.3444736","url":null,"abstract":"Gasoline blending scheduling is challenging, involving multiple conflicting objectives and a large decision space with many mixed integers. Due to these difficulties, one promising solution is to use preference-based multiobjective evolutionary algorithms (PBMOEAs). However, in practical applications, suitable preferences of decision makers are often difficult to generalize and summarize from their operational experience. This article proposes a novel framework called preference prediction-based evolutionary multiobjective optimization (PP-EMO). In PP-EMO, suitable preferences for a new environment can be automatically obtained from historical operational experience by a machine learning-based preference prediction model when we feed the model with the input of the optimization environment. We have found that the predicted preference is able to guide the optimization to efficiently obtain a set of promising scheduling scenarios. Finally, we conducted comparative tests across various environments, and the experimental results demonstrate that the proposed PP-EMO framework outperforms existing methods. Compared with no preference, PP-EMO reduces operating costs by about 25% and decreases blending errors by 50% under demanding operational conditions.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"79-92"},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975993","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

A Review on Transferability Estimation in Deep Transfer Learning 深度迁移学习中可迁移性估计的研究进展

IEEE transactions on artificial intelligence Pub Date : 2024-08-19 DOI: 10.1109/TAI.2024.3445892

Yihao Xue;Rui Yang;Xiaohan Chen;Weibo Liu;Zidong Wang;Xiaohui Liu

{"title":"A Review on Transferability Estimation in Deep Transfer Learning","authors":"Yihao Xue;Rui Yang;Xiaohan Chen;Weibo Liu;Zidong Wang;Xiaohui Liu","doi":"10.1109/TAI.2024.3445892","DOIUrl":"https://doi.org/10.1109/TAI.2024.3445892","url":null,"abstract":"Deep transfer learning has become increasingly prevalent in various fields such as industry and medical science in recent years. To ensure the successful implementation of target tasks and improve the transfer performance, it is meaningful to prevent negative transfer. However, the dissimilarity between the data from source domain and target domain can pose challenges to transfer learning. Additionally, different transfer models exhibit significant variations in the performance of target tasks, potentially leading to a negative transfer phenomenon. To mitigate the adverse effects of the above factors, transferability estimation methods are employed in this field to evaluate the transferability of the data and the models of various deep transfer learning methods. These methods ascertain transferability by incorporating mutual information between the data or models of the source domain and the target domain. This article furnishes a comprehensive overview of four categories of transferability estimation methods in recent years. It employs qualitative analysis to evaluate various transferability estimation approaches, assisting researchers in selecting appropriate methods. Furthermore, this article evaluates the open problems associated with transferability estimation methods, proposing potential emerging areas for further research. Last, the open-source datasets commonly used in transferability estimation studies are summarized in this study.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"5894-5914"},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810396","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

Adaptive Composite Fixed-Time RL-Optimized Control for Nonlinear Systems and Its Application to Intelligent Ship Autopilot 非线性系统的自适应复合定时rl优化控制及其在智能船舶自动驾驶仪中的应用

IEEE transactions on artificial intelligence Pub Date : 2024-08-19 DOI: 10.1109/TAI.2024.3444731

Siwen Liu;Yi Zuo;Tieshan Li;Huanqing Wang;Xiaoyang Gao;Yang Xiao

引用次数: 0

meMIA: Multilevel Ensemble Membership Inference Attack meMIA：多层集成成员推理攻击

IEEE transactions on artificial intelligence Pub Date : 2024-08-19 DOI: 10.1109/TAI.2024.3445326

Najeeb Ullah;Muhammad Naveed Aman;Biplab Sikdar

{"title":"meMIA: Multilevel Ensemble Membership Inference Attack","authors":"Najeeb Ullah;Muhammad Naveed Aman;Biplab Sikdar","doi":"10.1109/TAI.2024.3445326","DOIUrl":"https://doi.org/10.1109/TAI.2024.3445326","url":null,"abstract":"Leakage of private information in machine learning models can lead to breaches of confidentiality, identity theft, and unauthorized access to personal data. Ensuring the safe and trustworthy deployment of AI systems necessitates addressing privacy concerns to prevent unintentional disclosure and discrimination. One significant threat, membership inference (MI) attacks, exploit vulnerabilities in target learning models to determine if a given sample was part of the training set. However, the effectiveness of existing MI attacks is often limited by the number of classes in the dataset or the need for diverse multilevel adversarial features to exploit overfitted models. To enhance MI attack performance, we propose meMIA, a novel framework based on stacked ensemble learning. meMIA integrates embeddings from a neural network (NN) and a long short-term memory (LSTM) model, training a subsequent NN, termed the meta-model, on the concatenated embeddings. This method leverages the complementary strengths of NN and LSTM models; the LSTM captures order differences in confidence scores, while the NN discerns probability distribution differences between member and nonmember samples. We extensively evaluate meMIA on seven benchmark datasets, demonstrating that it surpasses current state-of-the-art MI attacks, achieving accuracy up to 94.6% and near-perfect recall. meMIA's superior performance, especially on datasets with fewer classes, underscores the urgent need for robust defenses against privacy attacks in machine learning, contributing to the safer and more ethical use of AI technologies.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"93-106"},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976087","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

Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives 生成式人工智能和大型语言模型的最新进展：现状、挑战和展望

IEEE transactions on artificial intelligence Pub Date : 2024-08-19 DOI: 10.1109/TAI.2024.3444742

Desta Haileselassie Hagos;Rick Battle;Danda B. Rawat

引用次数: 0

Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations 对话中多模态情绪识别的深度不平衡学习

IEEE transactions on artificial intelligence Pub Date : 2024-08-19 DOI: 10.1109/TAI.2024.3445325

Tao Meng;Yuntao Shou;Wei Ai;Nan Yin;Keqin Li

{"title":"Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations","authors":"Tao Meng;Yuntao Shou;Wei Ai;Nan Yin;Keqin Li","doi":"10.1109/TAI.2024.3445325","DOIUrl":"https://doi.org/10.1109/TAI.2024.3445325","url":null,"abstract":"The main task of multimodal emotion recognition in conversations (MERC) is to identify the emotions in modalities, e.g., text, audio, image, and video, which is a significant development direction for realizing machine intelligence. However, many data in MERC naturally exhibit an imbalanced distribution of emotion categories, and researchers ignore the negative impact of imbalanced data on emotion recognition. To tackle this problem, we systematically analyze it from three aspects: data augmentation, loss sensitivity, and sampling strategy, and propose the class boundary enhanced representation learning (CBERL) model. Concretely, we first design a multimodal generative adversarial network to address the imbalanced distribution of emotion categories in raw data. Second, a deep joint variational autoencoder is proposed to fuse complementary semantic information across modalities and obtain discriminative feature representations. Finally, we implement a multitask graph neural network with mask reconstruction and classification optimization to solve the problem of overfitting and underfitting in class boundary learning and achieve cross-modal emotion recognition. We have conducted extensive experiments on the interactive emotional dyadic motion capture (IEMOCAP) and multimodal emotion lines dataset (MELD) benchmark datasets, and the results show that CBERL has achieved a certain performance improvement in the effectiveness of emotion recognition. Especially on the minority class “fear” and “disgust” emotion labels, our model improves the accuracy and F1 value by 10% to 20%. Our code is publicly available at \u0000<uri>https://github.com/yuntaoshou/CBERL</uri>\u0000.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6472-6487"},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810285","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

An Approach for Privacy-Aware Mobile App Package Recommendation 一种具有隐私意识的移动应用包推荐方法

IEEE transactions on artificial intelligence Pub Date : 2024-08-16 DOI: 10.1109/TAI.2024.3443028

Shanpeng Liu;Buqing Cao;Jianxun Liu;Guosheng Kang;Min Shi;Xiong Li;Kenneth K. Fletcher

{"title":"An Approach for Privacy-Aware Mobile App Package Recommendation","authors":"Shanpeng Liu;Buqing Cao;Jianxun Liu;Guosheng Kang;Min Shi;Xiong Li;Kenneth K. Fletcher","doi":"10.1109/TAI.2024.3443028","DOIUrl":"https://doi.org/10.1109/TAI.2024.3443028","url":null,"abstract":"With the prosperity of the mobile Internet, the abundance of data makes it difficult for users to choose their favorite app. Thus, mobile app recommendation as an emerging topic attracts lots of attention. However, existing methods for app recommendation rarely consider recommendation accuracy under the privacy representation of user preferences. To address this problem, we propose a privacy-aware app package recommendation method named APP-Rec. Specifically, in this method: 1) treat an app and its associated heterogeneous entities (APP-Rec considers not only the apps themselves but also a variety of related factors—collectively referred to as heterogeneous entities, such as app category and app neighbors) as an app package and extract comprehensive features from the app package using an intrapackage attention network and an interpackage attention network to improve app recommendation; and 2) design a privacy module utilizing Laplace noise to achieve privacy preservation of user preferences. Experimental results show that APP-Rec outperforms the state-of-the-art methods in terms of area under the curve (AUC). Moreover, the privacy preservation of user preferences in APP-Rec is proved by theoretical analysis and experimental results.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6240-6252"},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810291","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

Reinforcement Learned Multiagent Cooperative Navigation in Hybrid Environment With Relational Graph Learning 基于关系图学习的混合环境下强化学习多智能体协同导航

IEEE transactions on artificial intelligence Pub Date : 2024-08-14 DOI: 10.1109/TAI.2024.3443783

Wen Ou;Biao Luo;Xiaodong Xu;Yu Feng;Yuqian Zhao

引用次数: 0