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作者： Cao, Mingwei Zheng, Liping Jia, Wei Liu, Xiaoping Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei230009 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Structure from motion has attracted a lot of research in recent years, with new state-of-the-art approaches coming almost every year. One of its advantages over 3D reconstruction is that it can be used for any cameras (UAVs, depth sensor, light field) and produces relatively accurate point clouds and camera parameters. One of its disadvantages compared to other approaches is that it is computationally expensive. In this paper, we design a novel structure-from-motion framework to reduce the computational cost and implement a parallel bundle adjustment on GPU device for large-scale optimization. In our framework, the local bundle adjustment is added into the architecture of the incremental structure from motion;namely, the point clouds and camera’s parameters are optimized when an additional number of images was added. Then, the purpose is not only to improve the quality of the produced point clouds but also to reduce computation time via parallel bundle adjustment. We conduct extensively experiments on several challenging datasets and make comparison with the state-of-the-art methods. Experimental results show that the proposed method has the best performance in terms of accuracy and efficiency. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cameras

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Fast One-Stage Unsupervised Domain Adaptive Person Search

arXiv

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arXiv 2024年

作者： Cui, Tianxiang Wang, Huibing Peng, Jinjia Deng, Ruoxi Fu, Xianping Wang, Yang The College of Information and Science Technology Dalian Maritime University China The College School of Cyber Security and Computer Hebei University China The College of Computer Science and Artificial Intelligence Wenzhou University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China

Unsupervised person search aims to localize a particular target person from a gallery set of scene images without annotations, which is extremely challenging due to the unexpected variations of the unlabeled domains. However, most existing methods dedicate to developing multi-stage models to adapt domain variations while using clustering for iterative model training, which inevitably increases model complexity. To address this issue, we propose a Fast One-stage Unsupervised person Search (FOUS) which complementary integrates domain adaptaion with label adaptaion within an end-to-end manner without iterative clustering. To minimize the domain discrepancy, FOUS introduced an Attention-based Domain Alignment Module (ADAM) which can not only align various domains for both detection and ReID tasks but also construct an attention mechanism to reduce the adverse impacts of low-quality candidates resulting from unsupervised detection. Moreover, to avoid the redundant iterative clustering mode, FOUS adopts a prototype-guided labeling method which minimizes redundant correlation computations for partial samples and assigns noisy coarse label groups efficiently. The coarse label groups will be continuously refined via label-flexible training network with an adaptive selection strategy. With the adapted domains and labels, FOUS can achieve the state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU and PRW. The code is available at https://***/whbdmu/FOUS © 2024, CC0.

关键词： Iterative methods

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MPDNet: A 3D Missing Part Detection Network Based on Point Cloud Segmentation

MPDNet: A 3D Missing Part Detection Network Based on Point C...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhaoxin Fan Hongyan Liu Jun He Min Zhang Xiaoyong Du Key Labs of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Tsinghua University China

Utilizing computer vision technologies for machinery missing part detection has been a hot research topic recently. Most of existing methods take images as input and utilize 2D object detection pipelines for detecting fault regions. However, 2D models can't handle the situation when occlusion exists. To tackle the issue, in this paper, we propose a novel model named MPDNet, which exploits 3D point cloud pairs as input for missing part detection. In MPDNet, the missing part detection problem is transformed into a binary segmentation problem. The key idea behind our approach is that difference between two point clouds can be fully perceived in the latent feature space if they are encoded by the same encoder. Specially, we firstly propose a shared encode and abnormal lift module to find and enlarge difference between the target point cloud to be diagnosed and its corresponding predefined source point cloud without any fault. Then an attention based decoder is proposed to segment the source point cloud into two clusters: points that are missing in the target point cloud and points that are preserved in the target point cloud. Meanwhile, to make the model robust, a point cloud construction module is proposed as an auxiliary task to help the shared encoder to learn more discriminative features. Experiments on both synthetic and real world datasets have demonstrated the effectiveness of our model.

关键词： Fault diagnosis Solid modeling Three-dimensional displays Pipelines Object detection Signal processing Decoding

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Hypernode: Entity Fusion for data Traceability and Link Prediction

Hypernode: Entity Fusion for Data Traceability and Link Pred...

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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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A Coffee-Ring Effect-Based Paper Sensor Chip for the Determination of Β-Lactoglobulin in Foods Via a Smartphone

SSRN

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SSRN 2022年

作者： Lu, Yang Yang, Jingyi Zhang, Yong Wu, Lin State Key Laboratory for Food Nutrition and Safety College of Food Science and Engineering Tianjin University of Science and Technology Tianjin300457 China State Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

To determine allergen β-lactoglobulin (β-LG) in foods, a method by a coffee-ring effect (CRE)-based paper sensor chip combined with a smartphone has been developed. The strategy was based on the principle of fluorescence quenching by the quantum dot-aptamer-graphene oxide (QD-Apt-GO) and where the degree of quenching was inversely proportional to the concentration of β-LG. First, the prepared β-LG solution was added to the QD-aptamer fluorescent probe solution followed by an appropriate amount of GO. After the reaction, a certain amount of the mixture was dispensed onto the detection zone of the sensor chip, resulting in the spread of the analyte solution by evaporation-driven flow. The QD-aptamer and the QD-Apt-β-LG in the mixed solution formed a visible fluorescent coffee-ring. For signal quantitation, an image of the coffee-ring was taken by a smartphone and imported into self-programmed software for analysis. The quantitative results for β-LG were acquired rapidly based on the magnitude of the generated fluorescent coffee-ring. The limit of detection (LOD) for β-LG was 0.048 mg L -1 and the linear range for the optimized assay was 0.39–1000 mg L -1 . The method was validated for analysis of real food samples and the results were consistent with those of a commercial ELISA assay kit. The smartphone-based assay provides a simple, economic, compact, and visual detection platform for the determination of β-LG in foods. © 2022, The Authors. All rights reserved.

关键词： Graphene

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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

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IEEE Transactions on Affective Computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

arXiv

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arXiv 2022年

作者： Liu, Kang Xue, Feng Guo, Dan Wu, Le Li, Shujie Hong, Richang Hefei University of Technology School of Computer Science and Information Engineering Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province 485 Danxia Road Anhui Province Hefei230601 China Hefei University of Technology School of Software Key Laboratory of Knowledge Engineering with Big Data Intelligent Interconnected Systems Laboratory of Anhui Province 485 Danxia Road Anhui Province Hefei230601 China

In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm is to incorporate complete multimodal deep features of the items into the recommendation module. However, the existing studies ignore the mismatch problem between multimodal feature extraction (MFE) and user interest modeling (UIM). That is, MFE and UIM have different emphases. Specifically, MFE is migrated from and adapted to upstream tasks such as image classification. In addition, it is mainly a content-oriented and non-personalized process, while UIM, with its greater focus on understanding user interaction, is essentially a user-oriented and personalized process. Therefore, the direct incorporation of MFE into UIM for purely user-oriented tasks, tends to introduce a large number of preference-independent multimodal noise and contaminate the embedding representations in UIM. This paper aims at solving the mismatch problem between MFE and UIM, so as to generate high-quality embedding representations and better model multimodal user preferences. Towards this end, we develop a novel model, multimodal entity graph collaborative filtering, short for MEGCF. The UIM of the proposed model captures the semantic correlation between interactions and the features obtained from MFE, thus making a better match between MFE and UIM. More precisely, semantic-rich entities are first extracted from the multimodal data, since they are more relevant to user preferences than other multimodal information. These entities are then integrated into the user-item interaction graph. Afterwards, a symmetric linear Graph Convolution Network (GCN) module is constructed to perform message propagation over the graph, in order to capture both high-order semantic correlation and collaborative filtering signals. Finally, the sentiment information fr

关键词： Sentiment analysis

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Generalized Category Discovery with Large Language Models in the Loop

arXiv

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arXiv 2023年

作者： An, Wenbin Shi, Wenkai Tian, Feng Lin, Haonan Wang, QianYing Wu, Yaqiang Cai, Mingxiang Wang, Luyan Chen, Yan Zhu, Haiping Chen, Ping School of Automation Science and Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China Lenovo Research China University of Massachusetts Boston United States

Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on novel categories and struggle to reveal semantic meanings of the discovered clusters, which limits their applications in the real world. To mitigate the above issues, we propose Loop, an end-to-end active-learning framework that introduces Large Language Models (LLMs) 1 into the training loop, which can boost model performance and generate category names without relying on any human efforts. Specifically, we first propose Local Inconsistent Sampling (LIS) to select samples that have a higher probability of falling to wrong clusters, based on neighborhood prediction consistency and entropy of cluster assignment probabilities. Then we propose a Scalable Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples. Based on the feedback from LLMs, we perform Refined Neighborhood Contrastive Learning (RNCL) to pull samples and their neighbors closer to learn clustering-friendly representations. Finally, we select representative samples from clusters corresponding to novel categories to allow LLMs to generate category names for them. Extensive experiments on three benchmark datasets show that Loop outperforms SOTA models by a large margin and generates accurate category names for the discovered clusters. Code and data are available at https://***/Lackel/LOOP. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Local anatomy for personalised privacy protection

引用

International Journal of Information and Computer Security 2021年第2-3期15卷 254-271页

作者： Li, Boyu Liu, Yanheng Wang, Minghai Sun, Geng Li, Bin College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China Big Data and Network Management Center Jilin University Changchun China College of Mathematics Jilin University Changchun China

Anonymisation technique has been extensively studied and widely applied for privacy-preserving data publishing. However, most existing methods ignore personal anonymity requirements. In these approaches, the microdata consist of three categories of attribute: explicit-identifier, quasi-identifier and sensitive attribute. In fact, the data sensitivity should be determined by individuals. An attribute is semi-sensitive if it contains both QI and sensitive values. In this paper, we propose a novel anonymisation approach, called local anatomy, to address personalised privacy protection. Local anatomy partitions the tuples who consider the value as sensitive into buckets inside each attribute. We conduct some experiments to illustrate that local anatomy can protect all the sensitive values and preserve great information utility. Additionally, we also present the concept of intelligent anonymisation system as our direction of future work. © 2021 Inderscience Enterprises Ltd.

关键词： Sensitive data

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Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

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ACM Transactions on Recommender Systems 2024年第4期2卷 1-27页

作者： Zhiyong Cheng Jianhua Dong Fan Liu Lei Zhu Xun Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computing National University of Singapore Singapore Singapore School of Electronic and Information Engineering Tongji University Shanghai China School of Information Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China and Hefei Comprehensive National Science Center Hefei China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

关键词： Multi-Behavior Recommendation disentangled representation learning graph convolutional network multi-behavior recommendation multi-task learning

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