IEEE transactions on artificial intelligence最新文献_第8页

Reinforcement Learning for Solving Colored Traveling Salesman Problems: An Entropy-Insensitive Attention Approach 解决彩色旅行推销员问题的强化学习：对熵不敏感的注意力方法

IEEE transactions on artificial intelligence Pub Date : 2024-09-19 DOI: 10.1109/TAI.2024.3461630

Tianyu Zhu;Xinli Shi;Xiangping Xu;Jinde Cao

引用次数: 0

Self-Model-Free Learning Versus Learning With External Rewards in Information Constrained Environments 信息受限环境下的无自我模型学习与外部奖励学习

IEEE transactions on artificial intelligence Pub Date : 2024-09-18 DOI: 10.1109/TAI.2024.3433614

Prachi Pratyusha Sahoo;Kyriakos G. Vamvoudakis

引用次数: 0

Artificial Intelligence Across Europe: A Study on Awareness, Attitude and Trust 整个欧洲的人工智能：关于意识、态度和信任的研究

IEEE transactions on artificial intelligence Pub Date : 2024-09-17 DOI: 10.1109/TAI.2024.3461633

Teresa Scantamburlo;Atia Cortés;Francesca Foffano;Cristian Barrué;Veronica Distefano;Long Pham;Alessandro Fabris

{"title":"Artificial Intelligence Across Europe: A Study on Awareness, Attitude and Trust","authors":"Teresa Scantamburlo;Atia Cortés;Francesca Foffano;Cristian Barrué;Veronica Distefano;Long Pham;Alessandro Fabris","doi":"10.1109/TAI.2024.3461633","DOIUrl":"https://doi.org/10.1109/TAI.2024.3461633","url":null,"abstract":"This article presents the results of an extensive study investigating the opinions on artificial intelligence (AI) of a sample of 4006 European citizens from eight distinct countries (France, Germany, Italy, Netherlands, Poland, Romania, Spain, and Sweden). The aim of the study is to gain a better understanding of people's views and perceptions within the European context, which is already marked by important policy actions and regulatory processes. To survey the perceptions of the citizens of Europe, we design and validate a new questionnaire (PAICE) structured around three dimensions: people's awareness, attitude, and trust. We observe that while awareness is characterized by a low level of self-assessed competency, the attitude toward AI is very positive for more than half of the population. Reflecting on the collected results, we highlight implicit contradictions and identify trends that may interfere with the creation of an ecosystem of trust and the development of inclusive AI policies. The introduction of rules that ensure legal and ethical standards, along with the activity of high-level educational entities, and the promotion of AI literacy are identified as key factors in supporting a trustworthy AI ecosystem. We make some recommendations for AI governance focused on the European context and conclude with suggestions for future work.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 2","pages":"477-490"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535493","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}

引用次数: 0

Traffexplainer: A Framework Toward GNN-Based Interpretable Traffic Prediction Traffexplainer：一个基于gnn的可解释交通预测框架

IEEE transactions on artificial intelligence Pub Date : 2024-09-13 DOI: 10.1109/TAI.2024.3459857

Lingbai Kong;Hanchen Yang;Wengen Li;Yichao Zhang;Jihong Guan;Shuigeng Zhou

{"title":"Traffexplainer: A Framework Toward GNN-Based Interpretable Traffic Prediction","authors":"Lingbai Kong;Hanchen Yang;Wengen Li;Yichao Zhang;Jihong Guan;Shuigeng Zhou","doi":"10.1109/TAI.2024.3459857","DOIUrl":"https://doi.org/10.1109/TAI.2024.3459857","url":null,"abstract":"With the increasing traffic congestion problems in metropolises, traffic prediction plays an essential role in intelligent traffic systems. Notably, various deep learning models, especially graph neural networks (GNNs), achieve state-of-the-art performance in traffic prediction tasks but still lack interpretability. To interpret the critical information abstracted by traffic prediction models, we proposed a flexible framework termed Traffexplainer toward GNN-based interpretable traffic prediction. Traffexplainer is applicable to a wide range of GNNs without making any modifications to the original model structure. The framework consists of the GNN-based traffic prediction model and the perturbation-based hierarchical interpretation generator. Specifically, the hierarchical spatial mask and temporal mask are introduced to perturb the prediction model by modulating the values of input data. Then the prediction losses are backward propagated to the masks, which can identify the most critical features for traffic prediction, and further improve the prediction performance. We deploy the framework with five representative GNN-based traffic prediction models and analyze their prediction and interpretation performance on three real-world traffic flow datasets. The experiment results demonstrate that our framework can generate effective and faithful interpretations for GNN-based traffic prediction models, and also improve the prediction performance. The code will be publicly available at <uri>https://github.com/lingbai-kong/Traffexplainer</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 3","pages":"559-573"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10680338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583132","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}

引用次数: 0

OAFuser: Toward Omni-Aperture Fusion for Light Field Semantic Segmentation 面向全孔径融合的光场语义分割

IEEE transactions on artificial intelligence Pub Date : 2024-09-11 DOI: 10.1109/TAI.2024.3457931

Fei Teng;Jiaming Zhang;Kunyu Peng;Yaonan Wang;Rainer Stiefelhagen;Kailun Yang

{"title":"OAFuser: Toward Omni-Aperture Fusion for Light Field Semantic Segmentation","authors":"Fei Teng;Jiaming Zhang;Kunyu Peng;Yaonan Wang;Rainer Stiefelhagen;Kailun Yang","doi":"10.1109/TAI.2024.3457931","DOIUrl":"https://doi.org/10.1109/TAI.2024.3457931","url":null,"abstract":"Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: 1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. 2) A relative displacement difference exists in the data collected by different microlenses. To address these issues, we propose an \u0000<italic>omni-aperture fusion model (OAFuser)</i>\u0000 that leverages dense context from the central view and extracts the angular information from subaperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective \u0000<italic>subaperture fusion module (SAFM)</i>\u0000. This module efficiently embeds subaperture images in angular features, allowing the network to process each subaperture image with a minimal computational demand of only (\u0000<inline-formula><tex-math>${sim}1rm GFlops$</tex-math></inline-formula>\u0000). Furthermore, to address the mismatched spatial information across viewpoints, we present a \u0000<italic>center angular rectification module (CARM)</i>\u0000 to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of \u0000<italic>all evaluation metrics</i>\u0000 and sets a new record of \u0000<inline-formula><tex-math>$84.93%$</tex-math></inline-formula>\u0000 in mIoU on the UrbanLF-Real Extended dataset, with a gain of \u0000<inline-formula><tex-math>${+}3.69%$</tex-math></inline-formula>\u0000. The source code for OAFuser is available at \u0000<uri>https://github.com/FeiBryantkit/OAFuser</uri>\u0000.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6225-6239"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810372","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

Editorial: From Explainable Artificial Intelligence (xAI) to Understandable Artificial Intelligence (uAI) 社论：从可解释的人工智能（xAI）到可理解的人工智能（uAI）

IEEE transactions on artificial intelligence Pub Date : 2024-09-10 DOI: 10.1109/TAI.2024.3439048

Hussein Abbass;Keeley Crockett;Jonathan Garibaldi;Alexander Gegov;Uzay Kaymak;Joao Miguel C. Sousa

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IEEE Transactions on Artificial Intelligence Publication Information IEEE Transactions on Artificial Intelligence 出版信息

IEEE transactions on artificial intelligence Pub Date : 2024-09-10 DOI: 10.1109/TAI.2024.3449732

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Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask Genetic Programming 基于多任务遗传规划的有偏目标柔性作业车间动态调度

IEEE transactions on artificial intelligence Pub Date : 2024-09-09 DOI: 10.1109/TAI.2024.3456086

Fangfang Zhang;Gaofeng Shi;Yi Mei;Mengjie Zhang

{"title":"Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask Genetic Programming","authors":"Fangfang Zhang;Gaofeng Shi;Yi Mei;Mengjie Zhang","doi":"10.1109/TAI.2024.3456086","DOIUrl":"https://doi.org/10.1109/TAI.2024.3456086","url":null,"abstract":"Dynamic flexible job shop scheduling is an important combinatorial optimization problem that has rich real-world applications such as product processing in manufacturing. Genetic programming has been successfully used to learn scheduling heuristics for dynamic flexible job shop scheduling. Intuitively, users prefer small and effective scheduling heuristics that can not only generate promising schedules but also are computationally efficient and easy to be understood. However, a scheduling heuristic with better effectiveness tends to have a larger size, and the effectiveness of rules and rule size are potentially conflicting objectives. With the traditional dominance relation-based multiobjective algorithms, there is a search bias toward rule size, since rule size is much easier to optimized than effectiveness, and larger rules are easily abandoned, resulting in the loss of effectiveness. To address this issue, this article develops a novel multiobjective genetic programming algorithm that takes size and effectiveness of scheduling heuristics for optimization via multitask learning mechanism. Specifically, we construct two tasks for the multiobjective optimization with biased objectives using different search mechanisms for each task. The focus of the proposed algorithm is to improve the effectiveness of learned small rules by knowledge sharing between constructed tasks which is implemented with the crossover operator. The results show that our proposed algorithm performs significantly better, i.e., with smaller and more effective scheduling heuristics, than the state-of-the-art algorithms in the examined scenarios. By analyzing the population diversity, we find that the proposed algorithm has a good balance between exploration and exploitation during the evolutionary process.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"169-183"},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976034","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

Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency 基于细粒度线索和噪声不一致性的人脸伪造检测

IEEE transactions on artificial intelligence Pub Date : 2024-09-06 DOI: 10.1109/TAI.2024.3455311

Dengyong Zhang;Ruiyi He;Xin Liao;Feng Li;Jiaxin Chen;Gaobo Yang

{"title":"Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency","authors":"Dengyong Zhang;Ruiyi He;Xin Liao;Feng Li;Jiaxin Chen;Gaobo Yang","doi":"10.1109/TAI.2024.3455311","DOIUrl":"https://doi.org/10.1109/TAI.2024.3455311","url":null,"abstract":"Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"144-158"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976035","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 Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach 基于shap误差补偿的改进的可解释电价预测模型

IEEE transactions on artificial intelligence Pub Date : 2024-09-06 DOI: 10.1109/TAI.2024.3455313

Leena Heistrene;Juri Belikov;Dmitry Baimel;Liran Katzir;Ram Machlev;Kfir Levy;Shie Mannor;Yoash Levron

{"title":"An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach","authors":"Leena Heistrene;Juri Belikov;Dmitry Baimel;Liran Katzir;Ram Machlev;Kfir Levy;Shie Mannor;Yoash Levron","doi":"10.1109/TAI.2024.3455313","DOIUrl":"https://doi.org/10.1109/TAI.2024.3455313","url":null,"abstract":"Forecasting errors in power markets, even as small as 1%, can have significant financial implications. However, even high-performance artificial intelligence (AI) based electricity price forecasting (EPF) models have instances when their prediction error is much higher than those shown by mean performance metrics. To date, explainable AI has been used to enhance the model transparency and trustworthiness of AI-based EPF models. However, this article demonstrates that insights from explainable AI (XAI) techniques can be expanded beyond its primary task of explanatory visualizations. This work presents a XAI-based error compensation approach to improve model performance and identify irregular predictions. The first phase of the proposed approach involves error quantification through a Shapley additive explanations (SHAP) based corrector model that fine-tunes the base predictor's forecasts. Using this corrector model's SHAP explanations, the proposed approach distinguishes high-accuracy predictions from lower ones in the second stage. Additionally, these explanations are more simplified than the base model, making them easier for nonexpert users such as bidding agents. Performance enhancement and insightful user-centric explanations are crucial for real-world scenarios such as price spikes during network congestion, high renewable penetration, and fluctuating fuel costs. Case studies discussed here show the efficacy of the proposed approach independent of model architecture, feature combination, or behavioral patterns of electricity prices in different markets.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"159-168"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976085","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