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High-Dimensional Multi-Label data Stream Classification With Concept Drifting Detection

IEEE Transactions on knowledge and data engineering 2023年第8期35卷 8085-8099页

作者： Li, Peipei Zhang, Haixiang Hu, Xuegang Wu, Xindong Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Second People's Hospital of Hefei Anhui Hefei230011 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China School of Computer Science and Information Engineering Anhui Hefei230002 China

Multi-label data streams such as Web texts and images have been popular on the Web. These data present the characteristics of multiple label, high dimensionality, high volume, high velocity and especial concept drift etc. Thus, multi-label data stream classification is a very challenging and significant task especially in the handling of high-dimensional data with concept drifts. However, this challenge has received little attention from the research community. Therefore, we propose the max-relevance and min-redundancy based algorithm adaptation approach for the efficient and effective classification on multi-label data streams with high-dimensional attributes and concept drifts. In order to reduce the impact from the high-dimensional data with noisy attributes, we first refine the minimal-redundancy-maximal-relevance criterion based on mutual information to select qualified features in multi-label data streams. Second, we propose the data distribution based concept drifting detection approach to distinguish concept drifts hidden in data streams. Finally, we build an incremental ensemble classification model for efficiently classifying multi-label data streams. Extensive studies show that our approach can get optimal subsets of features while maintaining a good performance in the multi-label classification, as compared to several state-of-the-art multi-label feature selection algorithms using two efficient multi-label classification methods as base classifiers. Meanwhile, our approach is superior to three well-known multi-label data stream classification approaches in the effectiveness and efficiency. © 1989-2012 IEEE.

关键词： Classification (of information)

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Multi-Cluster Feature Selection Based on Isometric Mapping

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 570-572页

作者： Yadi Wang Zefeng Zhang Yinghao Lin Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004 Institute of Data and Knowledge Engineering School of Computer and Information EngineeringHenan UniversityKaifeng 475004China

Dear editor,This letter presents an unsupervised feature selection method based on machine *** selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of d... 详细信息

关键词： problem letter dimensionality

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Context-Aware Semantic Type Identification for Relational Attributes

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Journal of Computer Science & Technology 2023年第4期38卷 927-946页

作者：丁玥郭雨荷卢卫李海翔张美慧李晖潘安群杜小勇 Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China Tencent(Beijing)Technology Company Limited Beijing 100080China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China College of Computer Science and Technology Guizhou UniversityGuiyang 550025China Tencent(Shenzhen)Technology Company Limited Shenzhen 518057China

Identifying semantic types for attributes in relations,known as attribute semantic type(AST)identification,plays an important role in many data analysis tasks,such as data cleaning,schema matching,and keyword search in ***,due to a lack of unified naming standards across prevalent information systems(*** islands),AST identification still remains as an open *** tackle this problem,we propose a context-aware method to figure out the ASTs for relations in this *** transform the AST identification into a multi-class classification problem and propose a schema context aware(SCA)model to learn the representation from a collection of relations associated with attribute values and schema *** on the learned representation,we predict the AST for a given attribute from an underlying relation,wherein the predicted AST is mapped to one of the labeled *** improve the performance for AST identification,especially for the case that the predicted semantic types of attributes are not included in the labeled ASTs,we then introduce knowledge base embeddings(***)to enhance the above representation and construct a schema context aware model with knowledge base enhanced(SCA-KB)to get a stable and robust *** experiments based on real datasets demonstrate that our context-aware method outperforms the state-of-the-art approaches by a large margin,up to 6.14%and 25.17%in terms of macro average F1 score,and up to 0.28%and 9.56%in terms of weighted F1 score over high-quality and low-quality datasets respectively.

关键词： attribute semantic type(AST)identification context-aware semantic embedding knowledge base embedding

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Few-shot 3D Point Cloud Segmentation via Relation Consistency-guided Heterogeneous Prototypes

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IEEE Transactions on Multimedia 2025年 27卷 3158-3170页

作者： Wei, Lili Lang, Congyan Xu, Zheming Liang, Liqian Liu, Jun Ministry of Education Key Laboratory of Big Data & Artificial Intelligence in Transportation China Beijing Jiaotong University School of Computer Science & Technology Beijing100044 China Lancaster University LA1 4YW Lancaster United Kingdom

Few-shot 3D point cloud semantic segmentation is a challenging task due to the lack of labeled point clouds (support set). To segment unlabeled query point clouds, existing prototype-based methods learn 3D prototypes from point features of the support set and then measure their distances to the query points. However, such homogeneous 3D prototypes are often of low quality because they overlook the valuable heterogeneous information buried in the support set, such as semantic labels and projected 2D depth maps. To address this issue, in this paper, we propose a novel Relation Consistency-guided Heterogeneous Prototype learning framework (RCHP), which improves prototype quality by integrating heterogeneous information using large multi-modal models (e.g. CLIP). RCHP achieves this through two core components: Heterogeneous Prototype Generation module which collaborates with 3D networks and CLIP to generate heterogeneous prototypes, and Heterogeneous Prototype Fusion module which effectively fuses heterogeneous prototypes to obtain high-quality prototypes. Furthermore, to bridge the gap between heterogeneous prototypes, we introduce a Heterogeneous Relation Consistency loss, which transfers more reliable inter-class relations (i.e., inter-prototype relations) from refined prototypes to heterogeneous ones. Extensive experiments conducted on five point cloud segmentation datasets, including four indoor datasets (S3DIS, ScanNet, SceneNN, NYU Depth V2) and one outdoor dataset (Semantic3D), demonstrate the superiority and generalization capability of our method, outperforming state-of-the-art approaches across all datasets. The code will be released as soon as the paper is accepted. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows 8

Task Offloading Optimization Based on Position Prediction in...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zheng, Dawen Wang, Lusheng Kai, Caihong Peng, Min Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665450515

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay. © 2022 IEEE.

关键词： Edge computing

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Enhancing graph representation learning via type-aware decoupling and node influence allocation

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Applied Intelligence 2025年第6期55卷 1-14页

作者： Zhu, Guochang Hu, Jun Liu, Li Zhang, Qinghua Wang, Guoyin School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongwen Road Chongqing Chongqing400065 China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongwen Road Chongqing Chongqing400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China

The traditional graph representation methods can fit the information of graph with low-dimensional vectors, but they cannot interpret their composition, resulting in insufficient security. Graph decoupling, as a method of graph representation, can analyze the latent factors composing the graph representation vectors. However, in current graph decoupling methods, the number of factors is a hyperparameter, and enforce uniform decoupling vector dimensions which leads to information loss or redundancy. To address these issues, we propose a type-aware graph decoupling based on influence called Variational Graph Decoupling Auto-Encoder (VGDAE). It uses node labels as interpretable and objectively existing natural semantics for decoupling and allocates embedding space based on node influence, addressing the issues of manually setting the number of factors in traditional graph decoupling and the mismatch between node information size and embedding space. On the Cora, Citeseer, and fb-CMU datasets, VGDAE shows the impact of different node classes as decoupling targets on classification tasks. Furthermore, we perform visualization of the representations, VGDAE exhibits performance improvements of 2% in classification tasks and 12% in clustering tasks when compared with baseline models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Graph embeddings

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Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

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IEEE Transactions on knowledge and data engineering 2022年第3期34卷 1427-1445页

作者： Qiang, Jipeng Qian, Zhenyu Li, Yun Yuan, Yunhao Wu, Xindong Department of Computer Science Yangzhou University Jiangsu225009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230026 China Mininglamp Academy of Sciences Minininglamp Beijing China

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm. © 2020 IEEE.

关键词： Surveys

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Measurement of Absorption Spectroscopy Based on a Dispersion-Managed Dual-Comb Laser

Measurement of Absorption Spectroscopy Based on a Dispersion...

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2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Jing, Xiuhan Zhang, Chenghong Zhang, He Lyu, Wenhao Fu, Bo Xu, Lijun School of Instrumentation and Optoelectronic Engineering Beihang University Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry Beijing China School of Engineering Medicine Beihang University Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology Beijing China

ISBN: (纸本)9798350378214

Dual-comb spectroscopy is a competitive means of laser absorption spectroscopy owing to broad spectral range and high spectral resolution. Single-cavity dual-comb systems based on mode-locked lasers hold promising potential for further simplifying dual-comb spectroscopy. Two combs possess natural coherence because of common mode noise suppression. Herein, we proposed an all-fiber dispersion-managed dual-comb laser to broaden the spectra of dual combs. The output laser with a 3-dB bandwidth as broad as 9 nm at 1.5 μm was experimentally obtained. Dual combs were realized by the walk-off of orthogonal polarization modes and directly employed to absorption spec-troscopy due to their overlapped spectra. In order to improve the signal-to-noise ratio of dual-comb measurement, interferogram averaging was conducted, where the signal-to-noise ratio was generally proportional to the square root of averaging times. Besides, phase and frequency jitter was corrected by cross-correlation method. High resolution absorption lines of hydrogen cyanide from P18 to P26 were measured. Positions of measured absorption lines were in line with ones in HITRAN database, with an average error of approximate 1 pm. To the best of our knowledge, the 9-line result was the broadest bandwidth of polarization-multiplexed single-cavity dual combs without additional spectral spreading operation. The results demonstrate that the compact dual-comb spectrometer can be considered a potential candidate for quantitative measurements in practical applications. © 2024 IEEE.

关键词： Spectral resolution

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Applying Kumaraswamy distribution on stick-breaking process: a Dirichlet neural topic model approach

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Neural Computing and Applications 2024年第22期36卷 13731-13744页

作者： Ouyang, Jihong Wang, Teng Cao, Jingyue Wang, Yiming College of Computer Science and Technology Jilin University Qianjin Street Jilin Changchun130015 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Street Jilin Changchun130015 China

In recent years, neural topic modeling has increasingly raised extensive attention due to its capacity on generating coherent topics and flexible deep neural structures. However, the widely used Dirichlet distribution in shallow topic models is difficult to reparameterize. Therefore, most existing neural topic models assume the Gaussian as the prior of topic proportions for reparameterization. Gaussian distribution does not have the sparsity like Dirichlet distribution, which limits the model’s topic extraction ability. To address this issue, we propose a novel neural topic model approximating the Dirichlet prior with the reparameterizable Kumaraswamy distribution, namely Kumaraswamy Neural Topic Model (KNTM). Specifically, we adopted the stick-breaking process for posterior inference with the Kumaraswamy distribution as the base distribution. Besides, to capture the dependencies among topics, we propose a Kumaraswamy Recurrent Neural Topic Model (KRNTM) based on the recurrent stick-breaking construction to ensure that the model can still generate coherent topical words in high-dimensional topic space. We examined our method on five prevalent benchmark datasets over six Dirichlet-approximating neural topic models, among which KNTM has the lowest perplexity and KRNTM performance best on topic coherence and topic uniqueness. Qualitative analysis of the top topical words verifies that our proposed models can extract more semantically coherent topics compared with state-of-the-art models, further demonstrating our method’s effectiveness. This work contributes to the broader application of VAEs with Dirichlet priors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Benchmarking

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Fine-Grained Access Control Proxy Re-encryption with HRA Security from Lattice 18th

Fine-Grained Access Control Proxy Re-encryption with HRA Se...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Hou, Jinqiu Peng, Changgen Tan, Weijie Zhong, Chongyi Niu, Kun Li, Hu State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang550025 China School of Mathematics and Big data Guizhou Education University Guiyang550025 China

ISBN: (纸本)9789819998951

With the gradual formation of the cloud computing ecosystem, the value of cloud computing is becoming increasingly evident, and the accompanying information security issues have become core elements. The challenge lies in securely transmitting, computing, and sharing data in a cloud computing environment while keeping privacy. In this paper, we propose an access control proxy re-encryption scheme that supports inner product operations based on lattices in the standard model. First, the ciphertext is linked to the attributes, and condition for transition is associated with the re-encryption key. The proxy may re-encrypt ciphertext that meets attribute conditions instead of all ciphertext, thereby limiting the proxy’s conversion permissions. Furthermore, the user outputs the inner product value after decryption. Finally, honestly re-encryption attacks (HRA) security with increased CPA security is employed to better capture the target of proxy re-encryption. In addition, we propose a new method of hiding access policy, which uses differential privacy technology to perturb the attribute values to better safeguard the privacy of users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Access control

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