IEEE transactions on artificial intelligence最新文献_第6页

Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations

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

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

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

MSCS: Multiscale Consistency Supervision With CNN-Transformer Collaboration for Semisupervised Histopathology Image Semantic Segmentation

IEEE transactions on artificial intelligence Pub Date : 2024-08-14 DOI: 10.1109/TAI.2024.3443794

Min-En Hsieh;Chien-Yu Chiou;Hung-Wen Tsai;Yu-Cheng Chang;Pau-Choo Chung

{"title":"MSCS: Multiscale Consistency Supervision With CNN-Transformer Collaboration for Semisupervised Histopathology Image Semantic Segmentation","authors":"Min-En Hsieh;Chien-Yu Chiou;Hung-Wen Tsai;Yu-Cheng Chang;Pau-Choo Chung","doi":"10.1109/TAI.2024.3443794","DOIUrl":"https://doi.org/10.1109/TAI.2024.3443794","url":null,"abstract":"This study proposes a multiscale consistency supervision (MSCS) strategy that combines a semisupervised learning approach with multimagnification learning to ease the labeling load and improve the prediction accuracy of histopathology image semantic segmentation. The MSCS strategy incorporates multiview complementary information into the semisupervised learning process, where this information includes that obtained from multiscale views (i.e., cells and tissues) and encoders with different decision perspectives. The strategy is implemented through the collaboration between convolutional neural network (CNN) and Transformer encoders, where the former encoder excels at capturing local spatial relationships in the input images and the latter encoder excels at capturing global relationships. In the proposed approach, the learning process is performed using two asymmetric multiscale fusion networks, designated as MSUnetFusion and MSUSegFormer. MSUnetFusion learns the cell-level features using CNN and the tissue-level features using Transformer. In contrast, MSUSegFormer learns both features using only Transformer. MSCS enforces prediction consistency between the two networks to enhance the prediction performance for unlabeled training data. The experimental results show that MSCS outperforms both supervised and semisupervised methods for the segmentation of hepatocellular carcinoma (HCC) and colorectal cancer (CRC) datasets, even when only limited labeled data are available. Overall, MSCS appears to provide a promising solution for histopathology image semantic segmentation.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6356-6368"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810376","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

Migrant Resettlement by Evolutionary Multiobjective Optimization

IEEE transactions on artificial intelligence Pub Date : 2024-08-14 DOI: 10.1109/TAI.2024.3443790

Dan-Xuan Liu;Yu-Ran Gu;Chao Qian;Xin Mu;Ke Tang

{"title":"Migrant Resettlement by Evolutionary Multiobjective Optimization","authors":"Dan-Xuan Liu;Yu-Ran Gu;Chao Qian;Xin Mu;Ke Tang","doi":"10.1109/TAI.2024.3443790","DOIUrl":"https://doi.org/10.1109/TAI.2024.3443790","url":null,"abstract":"Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this article, we propose a new framework called migrant resettlement by evolutionary multiobjective optimization (MR-EMO), which reformulates migrant resettlement as a biobjective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a multiobjective evolutionary algorithm (MOEA) to solve the biobjective problem. We implement MR-EMO using three MOEAs: the popular nondominated sorting genetic algorithm II (NSGA-II), MOEA based on decomposition (MOEA/D) as well as the theoretically grounded global simple evolutionary multiobjective optimizer (GSEMO). To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO using matrix-swap mutation and repair mechanism (GSEMO-SR), which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"51-65"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975725","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 Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism 使用分流抑制机制的基于深度学习的人群计数方法

IEEE transactions on artificial intelligence Pub Date : 2024-08-14 DOI: 10.1109/TAI.2024.3443789

Fok Hing Chi Tivive;Abdesselam Bouzerdoum;Son Lam Phung;Hoang Thanh Le;Hamza Baali

{"title":"A Deep Learning-Based Method for Crowd Counting Using Shunting Inhibition Mechanism","authors":"Fok Hing Chi Tivive;Abdesselam Bouzerdoum;Son Lam Phung;Hoang Thanh Le;Hamza Baali","doi":"10.1109/TAI.2024.3443789","DOIUrl":"https://doi.org/10.1109/TAI.2024.3443789","url":null,"abstract":"Image-based crowd counting has gained significant attention due to its widespread applications in security and surveillance. Recent advancements in deep learning have led to the development of numerous methods that have achieved remarkable success in accurately counting crowds. However, many of the existing deep learning methods, which have large model sizes, are unsuitable for deployment on edge devices. This article introduces a novel network architecture and processing element designed to create an efficient and compact deep learning model for crowd counting. The processing element, referred to as the shunting inhibitory neuron, generates complex decision boundaries, making it more powerful than the traditional perceptron. It is employed in both the encoder and decoder modules of the proposed model for feature extraction. Furthermore, the decoder includes alternating convolutional and transformer layers, which provide local receptive fields and global self-attention, respectively. This design captures rich contextual information that is used for generating accurate segmentation and density maps. The self-attention mechanism is implemented using convolution modulation instead of matrix multiplication to reduce computational costs. Experiments conducted on three challenging crowd counting datasets demonstrate that the proposed deep learning network, which comprises a small model size, achieves crowd counting performance comparable to that of state-of-the-art techniques. Codes are available at \u0000<uri>https://github.com/ftivive/SINet</uri>\u0000.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 11","pages":"5733-5745"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600171","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

Intrusion Detection Approach for Industrial Internet of Things Traffic Using Deep Recurrent Reinforcement Learning Assisted Federated Learning

IEEE transactions on artificial intelligence Pub Date : 2024-08-14 DOI: 10.1109/TAI.2024.3443787

Amandeep Kaur

{"title":"Intrusion Detection Approach for Industrial Internet of Things Traffic Using Deep Recurrent Reinforcement Learning Assisted Federated Learning","authors":"Amandeep Kaur","doi":"10.1109/TAI.2024.3443787","DOIUrl":"https://doi.org/10.1109/TAI.2024.3443787","url":null,"abstract":"The rapid growth of industrial Internet of Things (IIoT) applications generates massive amount of heterogeneous data that are prone to cyberattacks. The imperative is to secure industrial data from adversarial attacks and develop a robust and secure framework capable of withstanding sophisticated attacks. Toward this machine learning (ML) algorithms are used for intrusion detection by analyzing the devices’ network traffic. However, classical ML models work on entire datasets that are located on a central server and are not a suitable choice for a secure intrusion detection framework. We propose the federated learning (FL)-based network intrusion detection model for IIoT scenarios which only share learned parameters with the central server and keep the data intact to local servers only. The proposed model is assisted with gated recurrent units (GRUs) for FL training to extract temporal dependencies of network traffic attacks in order to improve intrusion detection accuracy. Additionally, to increase the model aggregation rate of FL, we integrate deep reinforcement learning (DRL) to select of IIoT devices with high quality while keeping data privacy and energy-efficiency as main concerns. In contrast to earlier approaches, we consider nonindependent and identically distributed (non-IID) data over recent IIoT datasets. Experimental findings indicate that the proposed framework outperforms state-of-the-art FL and non-FL intrusion detection models in terms of accuracy, precision, recall, F1-score, and receiver operating characterstics (ROC).","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"37-50"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975728","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

Event-Triggered Fuzzy Adaptive Stabilization of Parabolic PDE–ODE Systems

IEEE transactions on artificial intelligence Pub Date : 2024-08-13 DOI: 10.1109/TAI.2024.3443011

Yuan-Xin Li;Bo Xu;Xing-Yu Zhang

{"title":"Event-Triggered Fuzzy Adaptive Stabilization of Parabolic PDE–ODE Systems","authors":"Yuan-Xin Li;Bo Xu;Xing-Yu Zhang","doi":"10.1109/TAI.2024.3443011","DOIUrl":"https://doi.org/10.1109/TAI.2024.3443011","url":null,"abstract":"Artificial intelligence (AI) offers fuzzy logic system (FLS) technique as one of the popular AI agents and decision-making tools for control systems to deal with uncertain nonlinearities. This article is concerned with the event-triggered intelligent fuzzy adaptive stabilization of a class of reaction-diffusion systems based on parabolic partial differential equations-ordinary differential equations (PDE–ODEs). The studied system type is an ODE subsystem with nonlinear and unknown control coefficients for controlling PDEs. The original PDE is transformed into a new target system through the infinite-dimensional transformation method, and a state feedback controller for the transformed system is designed with the adaptive backstepping method to stabilize the system. An event-triggered strategy based on a relative threshold is designed into the backstepping framework. When the triggering condition of the system is met, the control signal of the ODE subsystem is updated. The designed control scheme ensures that all closed-loop signals are bounded; in addition, the original system states can converge to zero. Finally, the simulation example demonstrates that the event-triggered control (ETC)-based stability control technology has a good control effect.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6580-6590"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825769","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

Optimal Output Feedback Tracking Control for Takagi–Sugeno Fuzzy Systems

IEEE transactions on artificial intelligence Pub Date : 2024-08-13 DOI: 10.1109/TAI.2024.3443004

Wenting Song;Shaocheng Tong

引用次数: 0

Compact Multitasking Multichromosome Genetic Algorithm for Heuristic Selection in Ontology Matching 本体匹配中启发式选择的紧凑型多任务多染色体遗传算法

IEEE transactions on artificial intelligence Pub Date : 2024-08-13 DOI: 10.1109/TAI.2024.3442731

Xingsi Xue;Jerry Chun-Wei Lin;Tong Su

{"title":"Compact Multitasking Multichromosome Genetic Algorithm for Heuristic Selection in Ontology Matching","authors":"Xingsi Xue;Jerry Chun-Wei Lin;Tong Su","doi":"10.1109/TAI.2024.3442731","DOIUrl":"https://doi.org/10.1109/TAI.2024.3442731","url":null,"abstract":"Ontology matching (OM) is critical for knowledge integration and system interoperability on the semantic web, tasked with identifying semantically related entities across different ontologies. Despite its importance, the complexity of terminology semantics and the large number of potential matches present significant challenges. Existing methods often struggle to balance between accurately capturing the multifaceted nature of semantic relationships and computational efficiency. This work introduces a novel approach, a compact multitasking multichromosome genetic algorithm for Heuristic selection (HS) in OM, designed to navigate the nuanced hierarchical structure of ontologies and diverse entity mapping preferences. Our method combines compact genetic algorithms with multichromosome optimization for entity sequencing and assigning HS, alongside an adaptive knowledge transfer mechanism to finely balance exploration and exploitation efforts. Evaluated on the ontology alignment evaluation initiative's benchmark, our algorithm demonstrates superior ability to produce high-quality ontology alignments efficiently, surpassing comparative methods in both effectiveness and efficiency. These findings underscore the potential of advanced genetic algorithms in enhancing OM processes, offering significant contributions to the broader AI field by improving the interoperability and knowledge integration capabilities of semantic web technologies.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6752-6766"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825810","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