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A three-way utility decision model with multi-label based on fuzzy neighborhood environments

Applied Soft computing 2025年 180卷

作者： Qi, Zhaohui Hu, Jiafeng Li, Hui Dai, Jianhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha Hunan 410081 China College of Information Science and Engineering Hunan Normal University Changsha 410081 China Institute of Interdisciplinary Studies Hunan Normal University Changsha 410081 China

Recently, there has been extensive research conducted on the Multi-Attribute Decision Making (MADM) problem, particularly focusing on the growing number of attributes involved. The importance of attribute weight has become a crucial factor in MADM scenarios. Conventional approaches for determining attribute weights are no longer sufficient for this domain. The primary obstacle is to efficiently assign attribute weights in order to get effective decision-making. This research presents a novel three-way utility decision (3WUD) model that combines multi-label and fuzzy neighborhood environments. Initially, we construct a Multi-Label Fuzzy Neighborhood Decision System (MLFNDS) utilizing an innovative method to calculate the multi-label conditional probability. Afterward, we determine the label importance and create a utility function that takes into account multi-label. Furthermore, we present a novel approach to assess the attribute importance by considering both algebraic and information-theoretic viewpoints. Ultimately, we have constructed a 3WUD model within multi-label fuzzy neighborhood environments to determine the definitive ranking of all objects. Additionally, four evaluation indicators are utilized to assess the efficiency, logic, and excellence of the suggested methodology. In summary, this paper provides a new perspective on three-way decision (3WD) and MADM research and expands its application scope. In addition, it also provides a new method and perspective for determining attribute weights in MADM. © 2025

关键词： Multi-label conditional probability Multi-label decision making Multi-label fuzzy neighborhood decision system Multi-label utility function Three-way decision

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Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding

arXiv

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arXiv 2024年

作者： Gao, Rong Liu, Xin Xing, Bohao Yu, Zitong Schuller, Bjorn W. Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory School of Engineering Sciences Lappeenranta-Lahti University of Technology LUT Finland School of Computing and Information Technology Great Bay University China Group on Language Audio & Music Imperial College London United Kingdom School of Medicine and Health Technical University of Munich Germany

In this work, we focus on a special group of human body language — the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. This characteristic introduces two novel challenges regarding micro-gestures that are worth rethinking. The first is whether strategies designed for other action recognition are entirely applicable to micro-gestures. The second is whether micro-gestures, as supplementary data, can provide additional insights for emotional understanding. In recognizing micro-gestures, we explored various augmentation strategies that take into account the subtle spatial and brief temporal characteristics of micro-gestures, often accompanied by repetitiveness, to determine more suitable augmentation methods. Considering the significance of temporal domain information for micro-gestures, we introduce a simple and efficient plug-and-play spatiotemporal balancing fusion method. We not only studied our method on the considered micro-gesture dataset but also conducted experiments on mainstream action datasets. The results show that our approach performs well in micro-gesture recognition and on other datasets, achieving state-of-the-art performance compared to previous micro-gesture recognition methods. For emotional understanding based on microgestures, we construct complex emotional reasoning scenarios. Our evaluation, conducted with large language models, shows that micro-gestures play a significant and positive role in enhancing comprehensive emotional understanding. We confirm that our new insights contribute to advancing research in micro-gesture and emotional artificial intelligence. Our code is available at: https://***/ErichG/MG based Emotion *** Codes 68T01 Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Negative Emotion Recognition Method Based on Rational Graph Attention Network and Broad Learning 21

Negative Emotion Recognition Method Based on Rational Graph ...

引用

21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Peng, Sancheng Chen, Guanghao Cao, Lihong Zeng, Rong Zhou, Yongmei Li, Xinguang Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies China School of Information and OptoelectronicScience and Engineering South China Normal University China School of Information Science and Technoogy Guangdong University of Foreign Studies China

Negative emotion recognition in textual conversations aims to identify the negative emotion of each utterance from textual conversations, which has become a hot research topic in recent years. However, enabling machines to recognize negative emotions in textual conversations is a challenging task, because there are contexts for peoples'emotional expression in conversations. To address the problem, we propose a method for negative emotion recognition based on rational graph attention network (RGAT) and broad learning (BL), namely RGAT-BL. We use pre-training model Roberta to generate the initial vector for textual conversations. Then, we adopt Bi-LSTM to extract local features and context semantic features of textual vectors, so as to obtain utterance-level features. Thirdly, we employ RGAT to extract long-distance dependency among speakers, so as to obtain the speaker-level features. At last, we use BL to process the above two connected features, so as to conduct the classified output of negative emotions. Compared with baseline models on three datasets, the experimental results show that the weighted-F1 and macro-F1 values of the proposed method are better than the baseline model on the three datasets. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Emotion Recognition

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Modeling on virtual network embedding using reinforcement learning

Modeling on virtual network embedding using reinforcement le...

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作者： Wang, Cong Zheng, Fanghui Zheng, Guangcong Peng, Sancheng Tian, Zejie Guo, Yujia Li, Guorui Yuan, Ying School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

It is well known that virtual network (VN) embedding (VNE) aims to solve how to efficiently allocate physical resources to a VN. However, this issue has been proved to be an NP-hard problem. Besides, as most of the existing approaches are based on heuristic algorithms, which is easy to fall into local optimal. To address the challenge, we formalize the problem as a mixed integer programming problem and propose a novel VNE method based on reinforcement learning in this article. And to solve the problem, we introduce a pointer network to generate virtual node mapping strategies through an attention mechanism, and design a reward function related to link resource consumption to build the connection between node mapping and link mapping stages of VNE. In addition, we present a policy gradient optimization mechanism to leverage the reward information obtained from the sampled solutions, and design an active search based process to automatically update the parameters of the neural network and to obtain near-optimal embedding solution. The experimental results show that the proposed method can improve the performance in average physical node utilization and long-term revenue to cost ratio comparing than that of the existing models. © 2020 John Wiley & Sons, Ltd.

关键词： Reinforcement learning

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Node transfer with graph contrastive learning for class-imbalanced node classification

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Neural Networks 2025年 190卷 107674页

作者： Li, Yangding Zhao, Xiangchao Zeng, Yangyang Feng, Hao Chai, Jiawei Xie, Hao Fu, Shaobin Zhang, Shichao College of Information Science and Engineering Hunan Normal University Changsha China Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

In graph representation learning, the class imbalance problem is a significant challenge that has received much attention from academics. Although current approaches have shown promising results, they have not adequately addressed the problems of node quantity imbalance and feature space imbalance in datasets. This research presents a node transfer with graph contrastive learning framework (NT-GCL) that aims to improve the representation capabilities of graph neural networks for minority classes nodes by balancing node quantity and feature space distributions. First, the proposed node transfer algorithm redistributes misclassified nodes from majority classes to achieve a balanced distribution of node quantity and feature space. This approach effectively prevents the feature space of minority classes from being compressed by majority classes during information propagation, further mitigating potential imbalance issues. Subsequently, the self-supervised contrastive learning strategy is employed to train the model without relying on labels, reducing the bias introduced by labeled data. Experiments conducted with various encoders on six public datasets demonstrate that NT-GCL exhibits strong competitiveness in class-imbalanced node classification. © 2025 Elsevier Ltd

关键词： Class imbalance Graph contrastive learning Graph neural network Node classification Node transfer

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Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

arXiv

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arXiv 2024年

作者： Dong, Li Peng, Yubo Jiang, Feibo Wang, Kezhi Yang, Kun School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China School of Intelligent Software and Engineering Nanjing University Suzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Department of Computer Science Brunel University London United Kingdom

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Yangding Li Shaobin Fu Hao Feng Yangyang Zeng Jinghao Wang Zhihao Jiang Lvyun Zhang School of Information Science and Engineering Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China School of Big Data and Computer Science Hechi University Guangxi China

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive.

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Grammatical Variation between China English and American English: The Case of difficulties/difficulty (in) doing 21

Grammatical Variation between China English and American Eng...

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Proceedings of the 2021 4th International Conference on Algorithms, computing and Artificial Intelligence

作者： Lixin Xia Hui Hu Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies China and Center for Lexicographical Studies Guangdong University of Foreign Studies China Center for Lexicographical Studies Guangdong University of Foreign Studies China

ISBN: (纸本)9781450385053

This study aims to investigate the factors that determine the presence or absence of the preposition in in the construction difficulties/difficulty (in) doing in China English and American English respectively, and to explore the difference between the two varieties on the prepositional use. The search strings difficulty (in) *ing and [have] difficulties (in) *ing were retrieved in the Corpus of Contemporary American English (COCA) and then in the Corpus of China English (CCE). By analyzing the statistics of the singular form from the two corpora, a conclusion can be made that the construction difficulty in *ing is preferred in formal registers in both English varieties as opposed to informal ones. And the difference on the prepositional use between the two English varieties lies in the process of prepositional gerund being replaced by a directly linked gerund. The results of the plural form indicate that the complexity principle is operative in determining the prepositional use in the construction for the two English varieties. Importantly, the findings of this study have great implications for the assessment of the previous claims about the construction. And the study offers a new insight into the analyses of the construction and the difference between American English and China English in the use of the construction. Last but not the least, the study is meaningful to Natural language Processing with a better understanding of the use of the investigated construction in the two English varieties.

关键词： China English Natural language Processing American English grammatical figconstruction Computational linguistics

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CESMP: Chinese-English Segment-aligned Multi-field Patent Data 7

CESMP: Chinese-English Segment-aligned Multi-field Patent Da...

引用

7th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2021

作者： Liu, Wuying Wang, Lin Hu, Fumao Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangzhou510420 China Shanghai International Studies University Xianda College of Economics and Humanities Shanghai200083 China Luoyang Institute of Science and Technology School of Foreign Languages Luoyang471023 China

ISBN: (纸本)9781665441490

Patent data from various countries in the world implies the essence of scientific discovery and technological innovation of all human beings, but language differences have become a huge obstacle to patent data retrieval and communication. We hope to build a bridge from Chinese to English in the patent domain, so that English speakers can make better use of Chinese patent data. With the help of natural language processing technologies such as optical character recognition, Chinese text processing, machine translation and English text processing, we construct digital Chinese-English segment-aligned multi-field patent (CESMP) data from scanned Chinese patents. The current CESMP data consists of 610,310 patent documents in XML format. Each patent document contains six required fields (date, publication, ipc, title, abstract, and claim) and four optional fields (cpc, wipo, originalapplicant, and currentowner), among which the wipo, title, abstract, and claim fields are aligned with Chinese and English segments. Supported by well-structured bilingual patent data, on the one hand, the resource construction algorithms can efficiently build a bilingual patent dictionary and a parallel patent segment bank;on the other hand, the deep natural language processing algorithms can be effectively implemented into many practical intelligent applications such as cross-language patent retrieval, patent spam filtering, patent network analysis, patent machine translation, etc. © 2021 IEEE.

关键词： Optical character recognition

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SAV-SE: Scene-aware Audio-Visual Speech Enhancement with Selective State Space Model

arXiv

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arXiv 2024年

作者： Qian, Xinyuan Gao, Jiaran Zhang, Yaodan Zhang, Qiquan Liu, Hexin Garcia, Leibny Paola Li, Haizhou School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Telecommunications The University of New South Wales Sydney2052 Australia College of Computing and Data Science Nanyang Technological University Singapore Center for Language and Speech Processing Johns Hopkins University United States Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen51872 China

Speech enhancement plays an essential role in various applications, and the integration of visual information has been demonstrated to bring substantial advantages. However, the majority of current research concentrates on the examination of facial and lip movements, which can be compromised or entirely inaccessible in scenarios where occlusions occur or when the camera view is distant. Whereas contextual visual cues from the surrounding environment have been overlooked: for example, when we see a dog bark, our brain has the innate ability to discern and filter out the barking noise. To this end, in this paper, we introduce a novel task, i.e. Scene-aware AudioVisual Speech Enhancement (SAV-SE). To our best knowledge, this is the first proposal to use rich contextual information from synchronized video as auxiliary cues to indicate the type of noise, which eventually improves the speech enhancement performance. Specifically, we propose the VC-S2E method, which incorporates the Conformer and Mamba modules for their complementary strengths. Extensive experiments are conducted on public MUSIC, AVSpeech and AudioSet datasets, where the results demonstrate the superiority of VC-S2E over other competitive methods. We will make the source code publicly available. Project demo page: https://***/ © 2024, CC BY.

关键词： Speech enhancement

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