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IEEE International Conference on data Mining (ICDM)

作者： Bingbing Dong Zan Zhang Jiao Li Yi Zhu Chenyang Bu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Big Knowledge Science Hefei University of Technology Hefei China School of Information Engineering Yangzhou University Yangzhou China

In the era of big data, fragmented knowledge, multisource heterogeneity, and different representation forms of the same entities in various data sources have posed considerable challenges to entity fusion. How to effectively integrate multisource knowledge for the same entities has provoked vast amounts of attention and research from multiple disciplines. Most existing methods for entity fusion can be categorized into two classes: one is to establish an association between the same entities, and the other is to delete duplicate entities after knowledge fusion and create a new fusion entity. However, in these two classes of methods, the former does not achieve true knowledge fusion and semantic interoperability, while the latter may cause irreversible loss of original information. In this paper, we propose a novel entity fusion scheme: Hypernode. Hypernode fuses the same entity in different data sources into a new entity while retaining the original data. We verify the effectiveness of Hypernode on multiple models of link prediction experiments. Several practical application cases illustrate the applicability of Hypernode in data traceability, open domain knowledge fusion, and multi-modal knowledge graph fusion.

关键词： Fuses Soft sensors Semantics Predictive models Big data Cognition data mining

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Analysis of Quality Issues in Production With Multi-view Coordination Assets

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IFAC-PapersOnLine 2022年第10期55卷 2938-2943页

作者： Sebastian Kropatschek Thorsten Steuer Elmar Kiesling Kristof Meixner Iman Ayatollahi Patrik Sommer Stefan Biffl Austrian Center for Digital Production Vienna Austria Institute of Data Process and Knowledge Engineering WU Wien Vienna Austria Neuman Aluminium CAG Holding GmbH Austria Christian Doppler Laboratory SQI Vienna Austria Institute of Information Systems Engineering TU Wien Austria

The diffusion of the Industry 4.0 paradigm has led to a proliferation of data that is generated by production assets on the shop floor. This data opens up new opportunities for the analysis of quality issues, but it also makes identifying, selecting, and correctly interpreting data all the more critical. This involves a multitude of domain experts that design, operate and maintain production equipment. Major challenges they face in this context are (i) to map and integrate their domain knowledge on potential failure modes and effects, products, processes and production assets; and (ii) to coordinate their actions to systematically investigate and address the most important issues first. To address these challenges, this paper introduces the FMEA-linked-to-PPR Asset Issue Analysis (FPI) Model, a multi-view coordination asset, to guide quality issue analyses. The model integrates cross-domain knowledge and facilitates tracking the investigation state of quality analyses in teams of domain experts. A preliminary evaluation on a real-world use case indicates the FPI model to facilitate effective cross-domain analytic processes and the efficient identification of potential causes for quality issues.

关键词： knowledge management in production Quality management Monitoring of product quality control performance Multi-view modeling of manufacturing operations

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Hierarchical Context Modeling Network for Landmark Recognition

Hierarchical Context Modeling Network for Landmark Recogniti...

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IEEE International Conference on data Mining (ICDM)

作者： Xing Bao Huan Zheng Zhao Zhang Zhengjun Zha Meng Wang School of Information Science and Technology University of Science and Technology of China Hefei China Aerospace Information Research Institute Suzhou China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China

Landmark recognition stands as a prominent classification challenge within the domain of vision and perception, involving the identification and localization of landmarks in images. However, existing landmark recognition methods often fall short in delivering satisfactory performance. A critical issue lies in the lack of simultaneous exploration of local, regional, and global modeling. Intuitively, the local details, regional features, and global structure within an image, especially of a building, significantly contribute to landmark recognition. To address this issue, we propose a novel approach, termed Hierarchical Context Modeling Network (HCMNet), for landmark recognition. In our methodology, we introduce a Hierarchical Context Modeling Block (HCMB) designed with a triplet-branch structure to model image context in a hierarchical manner. These branches consist of the local branch, which leverages a convolutional layer to capture local details, the regional branch focused on extracting regional features through large-kernel asymmetrical convolutional layers, and the global branch aims at encompassing the broader context by incorporating channel attention. To enhance the modeling capacity, we assemble multiple HCMBs into the HCMNet, creating a hierarchical structure. Comprehensive experiments validate the efficacy of the proposed HCMNet for landmark recognition, showcasing its superior performance compared to current methods. The proposed hierarchical context modeling approach proves instrumental in capturing the intricate features essential for accurate landmark recognition.

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Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

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IEEE International Conference on data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization

Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic C...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenyang Bu Yuxin Liu Lizhong Zhang Tao Zhu Wenjian Luo Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China School of Computer Science and Information Engineering University of South China Hengyang Hunan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong China

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to “track the moving optima”, rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm 4 4 https://***/DMiC-Lab-HFUT/SMDE-Transfer.

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Unsupervised Kernel-based Multi-view Feature Selection with Robust Self-representation and Binary Hashing 39

Unsupervised Kernel-based Multi-view Feature Selection with ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hu, Rongyao Gan, Jiangzhang Zhan, Mengmeng Li, Li Wei, Mengling School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054 China School of Computer Science and Technology Hainan University Haikou 570228 China Beijing Information Science and Technology University Beijing 100101 China

ISBN: (纸本)157735897X

Unsupervised multi-view feature selection involves selecting a subset of crucial features across diverse views to diminish feature dimensionality without leveraging label information. While numerous studies may risk losing semantic information when applied to real-world multi-view datasets. In this study, we introduce a novel model, Unsupervised Kernel-based Multi-view Feature selection with Robust self-representation and Binary hashing (UKMFS), which aims to identify robust consistent graph representation across views and leverage binary hashing codes to guide feature selection. Specifically, we first explore the underlying geometry by unifying the dimension of multi-view data with non-linear kernel mapping. Then, we search for consistent graphs across views by fusing unique graph representations of each view in a self-representation manner. Additionally, we impose low-rank constraints on the graph of each view to mitigate noise and unimportant parts to preserve the main structures and patterns. Furthermore, we design an unsupervised hashing feature selection model to exploit reliable binary labels across views and weighted matrices from each view. Finally, an effective optimization method is customized to solve the formulated problem. Comprehensive experiments on public multiview datasets indicate that our proposed method achieves state-of-the-art performance compared with the representative comparison methods regarding the clustering and the feature selection task. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Maritime Unmanned Rescue System-of-Systems Design Based on Attention Transformer

Maritime Unmanned Rescue System-of-Systems Design Based on A...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Wei Ding Zhenjun Ming Guoxin Wang Yan Yan Anton Van Beek Hongfei Zhan School of Mechanical Engineering Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing China Shanghai Aircraft Design and Research Institute Shanghai China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

When natural or man-made disasters occur at sea, a maritime unmanned rescue system-of-systems (MURSoSs), as an important guarantee for the safety of people's lives and property, has a rapid response to emergency rescue services. In the design process of MURSoSs, it is often faced with problems such as unexplainable mechanisms and inaccurate modeling due to the characteristics of multi-level coupling and irregular emergence. A design method of MURSoSs based on an attention Transformer is proposed. First, this paper decomposes the MURSoSs into a multi-level organizational structure composed of individual performance, group structure, and overall effectiveness. Second, the maritime unmanned rescue simulation environment is constructed, to obtain the multi-level evolution data of group structure and overall effectiveness under different individual performance, and the attention Transformer is used to mine the functional correlation relationship between levels. Finally, the experimental simulation prediction is carried out by using this function relationship. Taking the design of MURSoSs as an example, the results show that the constructed model accurately quantifies the correlation relationship between levels and effectively reveal the internal logic of the emergence process of maritime rescue.

关键词： Couplings Correlation Autonomous systems Disasters Design methodology Transformers Logic

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Causal-Inspired Multitask Learning for Video-Based Human Pose Estimation 39

Causal-Inspired Multitask Learning for Video-Based Human Pos...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Haipeng Wu, Sifan Wang, Zhigang Yin, Yifang Jiao, Yingying Lyu, Yingda Liu, Zhenguang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China Institute for Infocomm Research (I2R) A*STAR Singapore Public Computer Education and Research Center Jilin University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China

ISBN: (纸本)157735897X

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies. However, they overlook the causal relationships in the joints, leading to models that may be overly tailored and thus estimate poorly to challenging scenes. Therefore, adequate causal reasoning capability, coupled with good interpretability of model, are both indispensable and prerequisite for achieving reliable results. In this paper, we pioneer a causal perspective on pose estimation and introduce a causal-inspired multitask learning framework, consisting of two stages. In the first stage, we try to endow the model with causal spatio-temporal modeling ability by introducing two self-supervision auxiliary tasks. Specifically, these auxiliary tasks enable the network to infer challenging keypoints based on observed keypoint information, thereby imbuing causal reasoning capabilities into the model and making it robust to challenging scenes. In the second stage, we argue that not all feature tokens contribute equally to pose estimation. Prioritizing causal (keypoint-relevant) tokens is crucial to achieve reliable results, which could improve the interpretability of the model. To this end, we propose a Token Causal Importance Selection module to identify the causal tokens and non-causal tokens (e.g., background and objects). Additionally, non-causal tokens could provide potentially beneficial cues but may be redundant. We further introduce a non-causal tokens clustering module to merge the similar non-causal tokens. Extensive experiments show that our method outperforms state-of-the-art methods on three large-scale benchmark datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

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DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

arXiv

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arXiv 2022年

作者： Yang, Guang Cao, Juan Sheng, Qiang Qi, Peng Li, Xirong Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

The daily practice of sharing images on social media raises a severe issue about privacy leakage. To address the issue, privacy-leaking image detection is studied recently, with the goal to automatically identify images that may leak privacy. Recent advance on this task benefits from focusing on crucial objects via pretrained object detectors and modeling their correlation. However, these methods have two limitations: 1) they neglect other important elements like scenes, textures, and objects beyond the capacity of pretrained object detectors;2) the correlation among objects is fixed, but a fixed correlation is not appropriate for all the images. To overcome the limitations, we propose the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically finds out crucial regions including objects and other important elements, and models their correlation adaptively for each input image. To find out crucial regions, we cluster spatially-correlated feature channels into several region-aware feature maps. Further, we dynamically model the correlation with the self-attention mechanism and explore the interaction among the regions with a graph convolutional network. The DRAG achieved an accuracy of 87% on the largest dataset for privacy-leaking image detection, which is 10 percentage points higher than the state of the art. The further case study demonstrates that it found out crucial regions containing not only objects but other important elements like textures. The code and more details are in https://***/guang-yanng/DRAG. © 2022, CC BY.

关键词： Object detection

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Understanding the Evolution of the Concept of Artificial Intelligence in Different Publication Venues 16th

Understanding the Evolution of the Concept of Artificial Int...

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16th International Conference on Diversity, Divergence, Dialogue, iConference 2021

作者： Xiao, Lianjie Jiang, Weiwei Qin, Kai Ding, Ying Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China Nanjing University of Finance and Economics Nanjing210023 China The University of Texas at Austin AustinTX78705 United States

ISBN: (纸本)9783030712914

This study aims to clarify the contours of artificial intelligence (AI) and present how it has evolved in different publication venues in the different stages of its development. Based on the noun phrases extracted from scientific papers of AI, the authors constructed the co-occurrence network of noun phrases to visualize the context of AI. There exists a tension between the original descriptive concept of AI as defined initially in the workshop at Dartmouth College and the relatively recent, vague, and extensible concept of AI. AI is used as a technique or boundary object by different scientific publication venues. This paper creatively applies the boundary object theory to clarify the contours of the concept of AI. Besides, the results will bridge the gap between different stakeholders (e.g., AI researchers, government policy makers, and business entities) in different communities together and promote the efficient and effective discussion and communication about AI. © 2021, Springer Nature Switzerland AG.

关键词： Publishing

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