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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xianrui Wang Andreas Brendel Gongping Huang Yichen Yang Walter Kellermann Jingdong Chen CIAIC and Shaanxi Provincial Key Laboratory of Artificial Intelligence Northwestern Polytechnical University Xi’an China Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

Spatial information can help improve source separation performance. Numerous spatially informed source extraction methods based on the independent vector analysis (IVA) have been developed, which can achieve reasonably good performance in non- or weakly reverberant environments. However, the performance of those methods degrades quickly as the reverberation increases. The underlying reason is that those methods are derived based on the multiplicative transfer function model with a rank-1 assumption, which does not hold true if reverberation is strong. To circumvent this issue, this paper proposes to use the convolutive transfer function (CTF) model to improve the source extraction performance and develop a spatially informed IVA algorithm. Simulations demonstrate the efficacy of the developed method even in highly reverberant environments.

关键词： Analytical models Source separation Transfer functions signal processing algorithms Reverberation Speech processing

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An Evolutionary Multi-Objective Neural Architecture Search Approach to Advancing Cognitive Diagnosis in Intelligent Education

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Yang, Shangshang Ma, Haiping Bi, Ying Tian, Ye Zhang, Limiao Jin, Yaochu Zhang, Xingyi School of Artificial Intelligence Anhui University Hefei China Institutes of Physical Science and Information Technology Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China School of Electrical and Information Engineering Computational Intelligence Laboratory Zhengzhou University Zhengzhou China School of Computer Science and Technology Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei Anhui China School of Engineering Westlake University Hangzhou China

As a pivotal technique in intelligent education systems, cognitive diagnosis (CD) serves to reveal students’ knowledge proficiency for better tackling subsequent tasks. Unfortunately, due to pursuing high model interpretability, existing manually designed models for CD often hold simplistic architectures, which cannot cope with intricate data in modern education platforms. Furthermore, the bias of human design limits the emergence of novel and effective CD models. To develop interpretable and more effective models, thus this paper proposes an evolutionary multi-objective neural architecture search (NAS) approach for CD. Specifically, we first adopt a comprehensive search space for the NAS task of CD: all candidate models can be encompassed by a general model that deals with three distinct types of inputs. Then, an innovative model interpretability objective is devised to formulate the architecture search task as a bi-objective optimization problem (BOP). To solve the BOP, we employ a multi-objective genetic programming (MOGP) as the search strategy to explore the search space. To make the employed MOGP search well, all architectures are first encoded by trees for easy optimization, and we devise a genetic operation and a population initialization strategy to expedite its convergence. Finally, the proposed approach is actually a MOGP-based NAS approach for CD. Extensive experiments show that CD models searched by the proposed approach exhibit significantly better performance than existing models and hold as good interpretability as handcrafted models. Besides, the effectiveness of the proposed MOGP search strategy, the devised objective, and tailored strategies are validated. IEEE

关键词： Computer architecture

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2D Human Pose Estimation with Explicit Anatomical keypoints Structure Constraints

arXiv

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arXiv 2022年

作者： Ji, Zhangjian Wang, Zilong Zhang, Ming Chen, Yapeng Qian, Yuhua The Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan030006 China The Institute of Big Data Science and Industry The Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China

Recently, human pose estimation mainly focuses on how to design a more effective and better deep network structure as human features extractor, and most designed feature extraction networks only introduce the position of each anatomical keypoint to guide their training process. However, we found that some human anatomical keypoints kept their topology invariance, which can help to localize them more accurately when detecting the keypoints on the feature map. But to the best of our knowledge, there is no literature that has specifically studied it. Thus, in this paper, we present a novel 2D human pose estimation method with explicit anatomical keypoints structure constraints, which introduces the topology constraint term that consisting of the differences between the distance and direction of the keypoint-to-keypoint and their groundtruth in the loss object. More importantly, our proposed model can be plugged in the most existing bottom-up or top-down human pose estimation methods and improve their performance. The extensive experiments on the benchmark dataset: COCO keypoint dataset, show that our methods perform favorably against the most existing bottom-up and top-down human pose estimation methods, especially for Lite-HRNet, when our model is plugged into it, its AP scores separately raise by 2.9% and 3.3% on COCO val2017 and test-dev2017 datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Multi-View Fusion Graph Attention Network for Multilabel Class Incremental Learning

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Information Fusion 2025年 123卷

作者： Tan, Anhui Wang, Yu Wu, Wei-Zhi Ding, Weiping Liang, Jiye School of Mathematical Sciences Huaqiao University Quanzhou362021 China School of Information Engineering Zhejiang Ocean University Zhoushan316022 China School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan030006 China

Multilabel Class-Incremental Learning (MLCIL) refers to a variant of class-incremental learning and multilabel learning where models are required to learn from images or data associated with multiple labels, and new sets of classes are introduced incrementally. However, most existing MLCIL methods tend to rely heavily on limited single-view features, which makes it challenging for them to effectively capture class-specific characteristics and the correlations between different labels. Furthermore, MLCIL faces difficulties related to both intra-class and inter-class imbalances, which arise from the varying frequencies of class occurrences during each incremental session. To address these issues, we propose a novel MLCIL model called the Multi-View Fusion Graph Attention Network (MVGAT). First, the MVGAT architecture includes a multi-view feature extraction module that fuses class node features from three different perspectives of images, effectively capturing both local and global class-specific information. Second, MVGAT introduces a multi-view attention fusion module that combines the multi-view class node features based on label correlations. Importantly, the attention fusion modules trained in previous learning sessions are preserved, helping to mitigate catastrophic forgetting by providing independent probability predictions for their respective learned classes. Additionally, MVGAT is equipped with a pseudo-label correction module to enhance the accuracy of pseudo-labels by integrating predictions from the current session with those from historical frozen attention fusion modules. Moreover, an asymmetric loss function has been developed to balance intra-class and inter-class performance by dynamically adjusting negative focus parameters based on class occurrence frequency. Finally, experimental results on benchmark datasets demonstrate that MVGAT outperforms existing state-of-the-art methods. © 2025 Elsevier B.V.

关键词： Labeled data

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年 27卷 3716-3728页

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Document-Level Event Extraction with Integrating Extended Semantics and Label Hierarchy Information 23

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23rd Chinese National Conference on computational Linguistics, CCL 2024

作者： Fu, Yujiao Liao, Jian Li, Yang Guo, Zhangfeng Wang, Suge School of Computer and Information Technology Shanxi University Shanxi 030006 China Shanxi University of Finance and Economics Shanxi 030006 China Key Laboratory Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Shanxi 030006 China

ISBN: (纸本)9780000000002

Document-level event extraction is an important task in natural language processing, facing the challenges of scattered arguments and multi event mention, existing research typically extracts arguments from all sentences in a document and captures relationships between entities through argument role modeling, thereby ignoring the differences in association between events and sentences in documents. This paper proposes a document-level event extraction method that integrates extended semantic and hierarchical label information. Firstly, using large language model to semantically extend text and event type labels along with role labels to introduce richer contextual semantic information. Secondly, based on the event type detection module of correlation difference, sentences highly related to the event type in the document are obtained, and the problem of argument dispersion is alleviated by constraining the extraction range of candidate entities. Furthermore, for the multiple event types mentioned in the document, arguments are extracted from candidate entities using directed acyclic graphs to obtain all event elements. Experimental results on the ChFinAnn and DuEE-Fin datasets indicate that the proposed method effectively alleviates the problem of scattered arguments belonging to multiple events compared to baseline models, and effectively improve the performance of event extraction. © 2024 China National Conference on computational Linguistics.

关键词： computational linguistics

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A Knowledge Acquisition Method for Consumer Electronics Based on Triadic Hesitant Fuzzy Concept

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IEEE Transactions on Consumer Electronics 2025年

作者： Xin, Xianwei Zhao, Ye Wang, Chenyang Xu, Jiucheng Taleb, Tarik Henan Normal University School of Computer and Information Engineering Xinxiang453007 China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan237016 China Computer and Software Engineering School of College of Shenzhen University Guangdong Shenzhen518060 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Henan Province 453007 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum44801 Germany

With the growing intelligence and personalization of consumer electronic products, extracting effective information has become a key challenge in Consumer Electronics (CE). Triadic Concept Analysis (TCA), an important extension of Formal Concept Analysis (FCA), is widely applied but often struggles to handle the hesitant fuzzy characteristics in real-world data. To address this gap, we propose a Triadic Hesitant Fuzzy Concept (THFC) model. First, we introduce the concept of a triadic hesitant fuzzy background, integrating the fuzzy and hesitant nature of data in the CE environment. We propose a knowledge acquisition method based on THFC, which constructs a multi-granularity knowledge space with a dynamic adjustment mechanism. This method captures subtle distinctions between different types of information and their hierarchical structures in the context of CE, offering significant support for knowledge discovery and decision-making in complex environments. Building on this, we design a variable-precision hybrid similarity measurement (VP-HSM) method to extract core, adjunct, and edge concepts, thus providing more precise and structured knowledge for data analysis and decision-making in CE. Experimental results on publicly available datasets show that the proposed method significantly improves classification accuracy and stability, effectively handling hesitant fuzzy data and improving the accuracy of concept extraction. © 1975-2011 IEEE.

关键词： Data accuracy

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Evaluating edge artificial intelligence-driven supply chain management platforms using collaborative large-scale fuzzy information fusion

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Applied Soft Computing 2024年 159卷

作者： Zhang, Chao Zhang, Jingjing Sangaiah, Arun Kumar Li, Deyu Li, Wentao School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan Shanxi030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon College of Artificial Intelligence Southwest University Chongqing400715 China

The rapid development of edge artificial intelligence (AI) has brought about revolutionary changes in supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision-making process, injecting more innovative elements into SCM. In this context, SCM platforms require new technological developments and effective evaluations, as they collectively drive the efficient coordination of complex business processes. SCM platforms enable the seamless coordination of various supply chain elements, facilitating streamlined operations and enhanced decision-making processes. The evaluation of these platforms not only serves to validate their performance and effectiveness, but also contributes to continual improvements in design and application, and addressing the evolving demands within the realms of global commerce and dynamic market conditions. The ongoing assessment and enhancement of SCM platforms play a crucial role in optimizing resource allocation, improving production efficiency, and fostering adaptability to changing market dynamics. However, the acquisition of assessment data often introduces imprecise data. Additionally, the large-scale nature and bounded rationality of decision-makers (DMs) significantly impact the evaluation of SCM platforms. This article aims to explore a collaborative large-scale information fusion approach and provide a robust fuzzy framework for evaluating SCM platforms. This approach employs a combination of spherical fuzzy sets (SFSs), large-scale group decision-making (LSGDM), behavioral theories and three-way decisions (TWD) to thoroughly explore collaborative large-scale information fusion and its practical application in assessing SCM platforms. It introduces an innovative spherical fuzzy (SF) LSGDM technique and integrates TWD with the inclusion of prospect theory (PT) and regret theory (RT) to mitigate potential decision risks. The developed collaborative large-scale information f

关键词： Supply chain management

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A Document-Level Emotion Change Analysis Method Based on DualGATs for Prose 23

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23rd Chinese National Conference on computational Linguistics, CCL 2024

作者： Li, Ailin Li, Yang Wang, Suge Li, Shuqi School of Computer and Information Technology Shanxi University Shanxi 030006 China Shanxi University of Finance and Economics Shanxi 030006 China Key Laboratory Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Shanxi 030006 China

ISBN: (纸本)9780000000002

In Prose, the author’s emotions evolve within passages or sentences,transitioning from sadness to happiness, or from joy to anger. To this end, this paper constructs a dataset of prose emotion changes and proposes emotion analysis method based on multi-knowledge fusion utilizing Dual-Graph Attention Network. Firstly, this paper incorporates image knowledge to get the deep representation of sentence. Secondly, the context-weighted dependency graph and the discourse-weighted dependency graph are constructed, and the sentence representation is established by integrating context knowledge and discourse structure. Additionally, a pleasure valence recognition layer is designed to integrate pleasure valence information into the sentence representation. Experimental results indicate the method is effective for identifying emotion changes, which can provide help for understanding the ideological and emotional changes in prose comprehension. © 2024 China National Conference on computational Linguistics.

关键词： computational linguistics

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DATG: Data Augmentation with Transformer-Based Generation for Low-Resource Named Entity Recognition

DATG: Data Augmentation with Transformer-Based Generation fo...

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Chinese Automation Congress (CAC)

作者： Qian Yili Xu Haonan Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China

Data augmentation is an important technique for enhancing machine learning performance. In this study, we propose a novel generative data augmentation method for named entity recognition, which addresses the challenge of limited labeled data in professional domains such as biomedical and chemical. The proposed method involves organizing input entities into a Transformer model to produce tagged sentences that are specifically related to the input entities. By leveraging external entity dictionaries, we effectively generate new tagged data and demonstrate its superiority over other data augmentation methods. Our approach offers a potential solution to the NER challenges faced in low-resource languages and specialized domains.

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