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SSRN 2024年

作者： Zhang, Shaoxia Zhai, Yanhui Li, Deyu Zhang, Chao School of Information Shanxi University of Finance and Economics Shanxi Taiyuan030006 China School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China

Formal Concept Analysis (FCA) is a method rooted in order theory, with the aim of analyzing and visually representing concepts. Decision implication serves as a fundamental means of knowledge representation in FCA in the case of decision-making. This paper extends the scope of knowledge discovery within FCA in single domains to the realm of multi-domains, with introducing a framework for knowledge representation and reasoning within correlative data from the perspectives of cross-domain and multi-granularity. Firstly, we delve into the acquisition and modeling of decision knowledge within correlative data, and introduce the concept of multi-granularity decision implication. We then establish multi-granularity decision implication logic to study the completeness, non-redundancy and optimality of multi-granularity decision implications and introduce inference rules with semantical compatibility. Furthermore, we define lattice fusion decision context to seamlessly integrate information within correlative data and construct a multi-granularity decision implication basis (MGDIB) based on lattice fusion decision context. Finally, we conduct an experiment of generating MGDIB based on GroupLens_MovieLens dataset. © 2024, The Authors. All rights reserved.

关键词： Knowledge representation

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A unified sample selection framework for output noise filtering: an error-bound perspective

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 800-865页

作者： Gaoxia Jiang Wenjian Wang Yuhua Qian Jiye Liang School of Computer and Information Technology Shanxi University Taiyuan PR China School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan PR China Institute of Big Data Science and Industry Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan PR China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan PR China

The existence of output noise will bring difficulties to supervised learning. Noise filtering, aiming to detect and remove polluted samples, is one of the main ways to deal with the noise on outputs. However, most of the filters are heuristic and could not explain the filtering in uence on the generalization error (GE) bound. The hyper-parameters in various filters are specified manually or empirically, and they are usually unable to adapt to the data environment. The filter with an improper hyper-parameter may overclean, leading to a weak generalization ability. This paper proposes a unified framework of optimal sample selection (OSS) for the output noise filtering from the perspective of error bound. The covering distance filter (CDF) under the framework is presented to deal with noisy outputs in regression and ordinal classification problems. Firstly, two necessary and sufficient conditions for a fixed goodness of fit in regression are deduced from the perspective of GE bound. They provide the unified theoretical framework for determining the filtering effectiveness and optimizing the size of removed samples. The optimal sample size has the adaptability to the environmental changes in the sample size, the noise ratio, and noise variance. It offers a choice of tuning the hyper-parameter and could prevent filters from overcleansing. Meanwhile, the OSS framework can be integrated with any noise estimator and produces a new filter. Then the covering interval is proposed to separate low-noise and high-noise samples, and the effectiveness is proved in regression. The covering distance is introduced as an unbiased estimator of high noises. Further, the CDF algorithm is designed by integrating the cover distance with the OSS framework. Finally, it is verified that the CDF not only recognizes noise labels correctly but also brings down the prediction errors on real apparent age data set. Experimental results on benchmark regression and ordinal classification data sets dem

关键词： output noise generalization error bound optimal sample selection covering distance filtering supervised learning

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A Stable Intelligent Model-Based Framework Using Graph Convolutional Networks and MULTIMOORA for Clinical Decision Support Systems

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IEEE Transactions on Consumer Electronics 2024年

作者： Zhang, Chao Li, Xiaochuan Bai, Wenhui Sangaiah, Arun Kumar Li, Deyu Shanxi University School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan030006 China National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Douliou64002 Taiwan Sunway University School of Engineering and Technology Selangor Petaling Jaya47500 Malaysia Chandigarh University University Centre for Research and Development Punjab Mohali140413 India

Artificial intelligence (AI) is rapidly advancing in consumer electronics, particularly in clinical decision support systems (CDSSs). AI-driven devices enhance the effectiveness of healthcare-related electronics via predictive health analytics, reshaping medical data collection, analysis, and decision-making. Nevertheless, due to the abundance of imprecise, hesitant, and fuzzy information in healthcare data stemming from consumer electronics, AI may misinterpret the data, resulting in erroneous and unstable medical decisions. Therefore, this paper endeavors to establish a stable intelligent medical decision model, employing two essential tools: graph convolutional networks (GCNs) and MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form). First, a granular computing (GrC) framework based on GCNs and MULTIMOORA is established. In this framework, GCNs are initially employed to process multi-modal data within the hesitant fuzzy linguistic (HFL) background. Second, a comprehensive HFL information system (IS) is constructed. Third, three types of multi-granularity (MG) HFL methods are developed via MULTIMOORA. To better address the bounded rationality of decision-makers (DMs), regret theory (RT) is utilized to consolidate the multi-modal data. Finally, the efficacy and practically of the proposed decision model are assessed via a case study on medication selections. © 1975-2011 IEEE.

关键词： Electronic health record

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Sentiment classification method based on multitasking and multimodal interactive learning 22

Sentiment classification method based on multitasking and mu...

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22nd Chinese National Conference on computational Linguistics, CCL 2023

作者： Xue, Peng Li, Yang Wang, Suge Liao, Jian Zheng, Jianxing Fu, Yujie Li, Deyu 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 of Ministry of Education Shanxi University Shanxi 030006 China

ISBN: (纸本)9781713876229

With the rapid development of social media, multimodal data has shown explosive growth. How to mine and understand emotional information from multimodal data has become a popular research direction. However, existing multimodal sentiment analysis methods based on text, video, and audio often fuse high-level and low-level features of different modalities, ignoring the differences between different levels of modal features. Therefore, this article proposes a self supervised dynamic fusion model for multitasking and multimodal interactive learning, centered around text modality and supplemented by audio and video modalities. Through a multi-layer structure, a hierarchical fusion representation of single modal feature representation and pairwise modal features was constructed, enabling the model to fuse features from different levels. A fusion strategy was designed to gradually transition from low-level features to high-level features. In order to further strengthen multimodal feature fusion, distributed similarity Loss function and heterogeneous Loss function are used to learn common and specific representations of modes. On this basis, multi task learning is utilized to obtain the consistency and difference features of modalities. Through separate experiments on the CMU-MOSI and CMU-MOSEI datasets, the experimental results show that the sentiment classification performance of our model is superior to the baseline model. © 2023 China National Conference on computational Linguistics.

关键词： Multi-task learning

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A Conflict Degree-Guided Quantum Group Consensus Method Based on Graph Convolutional Networks with Enhanced Sentiment Analysis for Consumer Electronics Products

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IEEE Transactions on Consumer Electronics 2024年

作者： Zhang, Chao Liang, Xinyu Sangaiah, Arun Kumar Li, Deyu Shanxi University School of Computer and Information Technology Key Laboratory of Computational Intelligence Chinese Information Processing Ministry of Education Taiyuan030006 China National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Douliou64002 Taiwan Sunway University School of Engineering and Technology Petaling Jaya Selangor 47500 Malaysia Chandigarh University University Centre for Research and Development Punjab Mohali140413 India

Artificial intelligence-based human-computer interaction (AI-HCI) and consumer electronics (CE) are closely linked, making AI-HCI crucial in e-commerce by providing intelligent and personalized product recommendations. In this context, consumers often select from multiple websites and products when purchasing CE products. This makes CE product selections conflicting and random to reach a consensus, often modeled as a quantum group consensus problem. Therefore, this paper aims to enhance consumer selections via intelligent decision support using online reviews. First, an ensemble technique, multi-objective salp swarm optimization bi-directional long short-term memory (MOSSO-BiLSTM) with gate recurrent unit (GRU), is employed for sentiment analysis (SA). Second, graph convolutional networks (GCN) is used to calculate weights for key indicators. Meanwhile, according to hybrid distances, an improved fuzzy C-means three-way (FCMTW) clustering analysis is explored. Third, a conflict degree-guided quantum two-stage consensus reaching process (CRP) is designed. It includes identification rules determined by quantum frameworks and modification rules determined by regret theory (RT). Fourth, multi-objective optimization by ratio analysis plus the full MULTIplicative form (MULTIMOORA) and RT are combined for stable optimal product selections. Finally, a case study of selecting solar emergency lights verifies the viability of the proposed methodology. © 1975-2011 IEEE.

关键词： Quantum electronics

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Sparse Regularized Correlation Filter for Uav Object Tracking with Adaptive Contextual Learning and keyfilter Selection

SSRN

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SSRN 2023年

作者： Ji, Zhangjian Feng, Kai Qian, Yuhua Liang, Jiye School of Computer & Information Technology Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

Recently, correlation filter has been widely applied in unmanned aerial vehicle (UAV) tracking due to its high frame rates, robustness and low calculation resources. However, it is fragile because of two inherent defects, i.e, boundary effect and filter corruption. To handle them, in this work, we propose a novel `1 regularization correlation filter with adaptive contextual learning and keyfilter selection for UAV tracking. Firstly, we adaptively detect the positions of effective contextual distractors by the aid of the distribution of local maximum values on the response map of current frame which is generated by using the previous correlation filter model. Next, we eliminate inconsistent labels for the tracked target by removing one on each distractor and develop a new score scheme for each distractor. Then, we can select the keyfilter from the filters pool by finding the maximal similarity between the target at the current frame and the target template corresponding to each filter in the filters pool. Finally, quantitative and qualitative experiments on three authoritative UAV datasets show that the proposed method is superior to the state-of-the-art tracking methods based on correlation filter framework. © 2023, The Authors. All rights reserved.

关键词： Antennas

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Multiple kernel concept factorization algorithm based on global fusion

arXiv

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arXiv 2024年

作者： Li, Fei Du, Liang Ren, Chaohong School of Computer and Information Technology Shanxi University Taiyuan Shanxi030006 China Institute of Big Data Science and Industry Shanxi University Taiyuan Shanxi030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Taiyuan Shanxi030006 China

Non-negative Matrix Factorization (NMF) algorithm can only be used to find low rank approximation of original non-negative data while Concept Factorization (CF) algorithm extends matrix factorization to single non-linear kernel space，improving learning ability and adaptability of matrix factorization． In unsupervised environment， to design or select proper kernel function for specific dataset， a new algorithm called Globalized Multiple Kernel CF (GMKCF) was proposed． Multiple candidate kernel functions were input in the same time and learned in the CF framework based on global linear fusion，obtaining a clustering result with high quality and stability and solving the problem of kernel function selection that the CF faced． The convergence of the proposed algorithm was verified by solving the model with alternate iteration． The experimental results on several real databases show that the proposed algorithm outperforms comparison algorithms in data clustering， such as Kernel K-Means (KKM) ， Spectral Clustering (SC) ， Kernel CF (KCF) ， Co-regularized multi-view spectral clustering (Coreg) ， and Ｒobust Multiple KKM (ＲMKKM) ． © 2024, CC BY.

关键词： Non-negative matrix factorization

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Correction to: Incremental maintenance of three-way regions with variations of objects and values in hybrid incomplete decision systems

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Applied intelligence 2022年第7期53卷 8504-8505页

作者： Yang, Chuanjian Ge, Hao Xu, Yi School of Computer and Information Engineering Chuzhou University Chuzhou China School of Electronic and Electrical Engineering Chuzhou University Chuzhou China Key Laboratory of Computation Intelligence and Signal Processing Anhui University Hefei China

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Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach

arXiv

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arXiv 2024年

作者： Zhu, Yabin Wang, Qianwu Li, Chenglong Tang, Jin Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Public Safety and Emergency Management Anhui University of Science and Technology Hefei231131 China School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Center for Big Data and Population Health of IHM China

The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this work, we contribute a large-scale Visible-Thermal video benchmark for MOT, called VT-MOT. VT-MOT has the following main advantages. 1) The data is large scale and high diversity. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. 2) The cross-modal alignment is highly accurate. We invite several professionals to perform both spatial and temporal alignment frame by frame. 3) The annotation is dense and high-quality. VT-MOT has 3.99 million annotation boxes annotated and double-checked by professionals, including heavy occlusion and object re-acquisition (object disappear and reappear) challenges. To provide a strong baseline, we design a simple yet effective tracking framework, which effectively fuses temporal information and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods. From the evaluation results and analysis, we specify several potential future directions for visible-thermal MOT. The project is released in https://***/wqw123wqw/PFTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Exploring the Role of Edge Distribution in Graph Convolutional Networks

SSRN

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SSRN 2023年

作者： He, Liancheng Bai, Liang Yang, Xian Liang, Jiye Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Institute of Intelligent Information Processing Shanxi University Shanxi Taiyuan030006 China Alliance Manchester Business School The University of Manchester Manchester United Kingdom

Graph Convolutional Networks (GCNs) have been found to show excellent performance in processing graph-structured data by learning node representations through the aggregation of information from neighbors. While most GCN models assume strong homophily in the networks they process, some models are able to handle heterophilous graphs as well. The performance of these models can often be influenced by the selection of neighbors that participate in the node representation learning process. In this paper, we investigate the role of neighbors in GCNs from the perspective of edge distribution through both theoretical and empirical analyses. Based on our findings, we propose a new GCN model, the Graph Convolution Network with Improved Edge Distribution (GCN-IED), which generates both direct edges and hidden edges. Direct edges are generated based on the similarity between local neighborhoods, while hidden edges are implemented by aggregating information from multi-hop neighbors. Extensive experiments were conducted on various graph benchmark datasets to evaluate the effectiveness of our proposed method. The results of these experiments demonstrate that GCN-IED performs better on heterophilous datasets than other state-of-the-art GCN methods. © 2023, The Authors. All rights reserved.

关键词： Convolution

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