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arXiv 2023年

作者： Chen, Jianer Huang, Qin Kanj, Iyad Li, Qian Xia, Ge Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States School of Computing DePaul University ChicagoIL60604 United States Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Computer Science Lafayette College EastonPA18042 United States

We present streaming algorithms for the graph k-matching problem in both the insert-only and dynamic models. Our algorithms, with space complexity matching the best upper bounds, have optimal or near-optimal update time, significantly improving on previous results. More specifically, for the insert-only streaming model, we present a one-pass algorithm with optimal space complexity O(k2) and optimal update time O(1), that w.h.p. (with high probability) computes a maximum weighted k-matching of a given weighted graph. The update time of our algorithm significantly improves the previous upper bound of O(log k), which was derived only for k-matching on unweighted graphs. For the dynamic streaming model, we present a one-pass algorithm that w.h.p. computes a maximum weighted k-matching in O(W k2 · polylog(n)) space1 and with O(polylog(n)) update time, where W is the number of distinct edge weights. Again the update time of our algorithm improves the previous upper bound of O(k2 · polylog(n)). This algorithm, when applied to unweighted graphs, gives a streaming algorithm on the dynamic model whose space and update time complexities are both near-optimal. Our results also imply a streaming approximation algorithm for maximum weighted k-matching whose space complexity matches the best known upper bound with a significantly improved update time. © 2023, CC BY.

关键词： Dynamic models

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Submodular Maximization with Bounded Marginal Values 21st

Submodular Maximization with Bounded Marginal Values

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21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Yang, Ruiqi Gao, Suixiang Wang, Changjun Zhang, Dongmei School of Mathematical Sciences University of Chinese Academy Sciences Beijing100049 China Department of Operations Research and Information Engineering Beijing University of Technology Beijing100124 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China

ISBN: (纸本)9783030692438

We study the problem of maximizing non-monotone submodular functions subject to a p-independence system constraint. Although the submodularity ratio has been well-studied in maximizing set functions under monotonic scenario, the defined parameter may bring hardness of approximation for the maximization of set functions in the non-monotonic case. In this work, utilizing a lower bound for the marginal values, we investigate the Repeated Greedy introduced by (Feldman et al. 2017) and obtain a parameterized performance guarantee for the above constrained submodular maximization problem. © 2021, Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Predicting circRNA-disease associations using similarity assessing graph convolution from multi-source information networks

Predicting circRNA-disease associations using similarity ass...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Li Xue-Gang Hu Pei-Pei Li Lei Wang Zhu-Hong You School of Computer Science and Information Engineering Hefei University of Technology Hefei China Big Data and Intelligent Computing Research Center Guangxi Academy of Sciences) Nanning China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Circular RNA (circRNA), a novel endogenous noncoding RNA molecule with a closed-loop structure, can be used as a biomarker for many complex human diseases. Determining the relationship between circRNAs and diseases helps us to understand the diagnosis, treatment, and pathogenesis of complex diseases, which plays a critical role in clinical research. Nevertheless, the discovery of new circRNA-disease associations by wet-lab methods is not only time-consuming and costly but also randomized and blinded, which is also limited to small-scale studies. Thus, there is an urgent need to establish efficient and reliable computational methods to infer potential circRNA-disease associations on a large scale to effectively reduce costs and save time, and avoid high false-positive rates. In this paper, we propose a novel computational method for predicting circRNA-disease association based on the Similarity Assessing Graph Convolution Network (SAGCN) algorithm, which combines the multi-source similarity network constructed by circRNA and disease. Firstly, we fuse the multi-source similarity information of circRNAs and diseases and construct the multi-source similarity network respectively. Then we use the SAGCN algorithm to extract the hidden feature representations of circRNAs and diseases efficiently and objectively in the way of measuring the similarity between different nodes in the network. Finally, the obtained high-level features of circRNAs and diseases are fed to the multilayer perceptron (MLP) classifier for accurate prediction. Using the 5-fold cross-validation method, the AUC scores of the four SAGCN algorithms, on the benchmark circR2Disease dataset are 93.30%, 92.98%, 92.22% and 91.94%, respectively. Furthermore, case studies further validated that the proposed model was supported by biological experiments, and 25 of the top 30 circRNA-disease associations with the highest scores were confirmed by recent literature. Based on these reliable results, it can be anticip

关键词： Convolution Computational modeling Biological system modeling RNA Predictive models Prediction algorithms Feature extraction

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3D polarimetry of laser induced speckle fields for phase detection thyroid gland polycrystalline structure and traumatic necrosis of human internal organs

3D polarimetry of laser induced speckle fields for phase det...

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Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024

作者： Ushenko, Olexander Bilookyi, Olexander Ushenko, Yuriy Dubolazov, Alexander Soltys, Irina Olar, Olexander Mikirin, Ivan Skliarchuk, Valeriy Salega, Olexander Karas, Oleksandr Rakhimbayeva, Gulbarshin Rogovyi, Yuriy Kozan, Nataliia Zheng, Jun Alimbayeva, Zhadyra Omiotek, Zbigniew Taizhou Institute of Zhejiang University Taizhou China Optics and Publishing Department Chernivtsi National University Chernivtsi Ukraine Department of Surgery Bucovinian State Medical University Chernivtsi Ukraine Computer Science Department Chernivtsi National University Chernivtsi Ukraine Biomedical Engineering and Optic-Electronic Systems Department Vinnitsia National Technical University Vinnytsia Ukraine Department of Information Communication Technologies Kazakh National Medical University named after S.D. Asfendiyarova Almaty Kazakhstan Department of Pathological Physiology Bucovinian State Medical University Chernivtsi Ukraine Department of Forensic Medicine Medical and Pharmaceutical Law State Medical Academy Ivano-Frankivsk Ukraine Kazakh National Women's Teacher Training University Almaty Kazakhstan Faculty of Electrical Engineering and Computer Science Lublin University of Technology Lublin Poland

ISBN: (数字)9781510685796

ISBN: (纸本)9781510685789

A short theory of the processes of formation of the laser speckle fields polarization structure of biological tissues with optically anisotropic architectonics is presented. A method of polarization interference mapping of speckle fields with layer-by-layer phase scanning of holographically reconstructed distributions of complex amplitudes of biological preparations of various morphological structures and physiological states has been proposed and experimentally tested - biopsies of the thyroid gland and blood films of rats with different severity of sepsis. Layer-by-layer maps of polarization ellipticity of native histological sections of thyroid biopsies from donors and sick patients were studied. As part of the statistical analysis of phase coordinate distributions of polarization ellipticity, objective markers for the differential diagnosis of thyroid pathology were found - nodular goiter, autoimmune thyroiditis and papillary cancer. Very good (~90%) and excellent (≥90%) accuracy of differential diagnosis was achieved. Statistical markers for temporal monitoring of septic changes in polarization azimuth maps of polycrystalline blood films of rats were found. An example of further research using laser-induced autofluorescence polarimetry methods is presented. © 2024 SPIE.

关键词： Optical anisotropy

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NCG-LS: Named Entity Recognition Model Specializing for Analyzing Product Titles

NCG-LS: Named Entity Recognition Model Specializing for Anal...

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2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent technology (WI-IAT)

作者： Shiqi Sun Kun Zhang Jingyuan Li Jianhe Cen Yuanzhuo Wang Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beijing Technology and Business University Beijing China

Entity recognition of product titles is essential for retrieving and recommending product information, where product title text has the characteristics of high entity density and fine type granularity. Most of the current studies focus on only one of the two features instead of considering the two challenges together. Our approach, named NCG-LS, proposes NEZHA-CNN-GlobalPointer architecture with the addition of label semantic networks, and uses multi-granularity contextual and label semantic information to fully capture the internal structure and category information of words and texts to improve the entity recognition accuracy. Through a series of experiments, we proved the efficiency of NCG-LS over a dataset of Chinese product titles from JD, improving the F1 value by 5.98%, when compared to the BERT-LSTM-CRF model on the product title corpus.

关键词： Analytical models Text recognition Annotations Semantics Text categorization Feature extraction Character recognition

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Ontology Construction of Fault Diagnosis Knowledge Graph for Civil Aircraft Maintenance

Ontology Construction of Fault Diagnosis Knowledge Graph for...

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Prognostics and System Health Management Conference (PHM-Qingdao)

作者： Huanchun Peng Kong Sun Qingqian Zhou Yini Zhang Weidong Yang Yuanxiang Li Academy for Engineering and Technology Fudan University Shanghai China School of Aeronautics and Astronautics Shanghai Jiao Tong University Shanghai China COMAC Shanghai Aircraft Customer Service Co. Ltd. Shanghai China School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

Maintenance of civil aircraft systems is crucial for ensuring air travel safety. However, managing a large volume of maintenance data can lead to inefficiencies in operation support work. Knowledge graphs are utilized to visualize maintenance manuals and historical fault diagnosis records. Therefore, this paper presents a novel ontology construction approach for fault diagnosis knowledge graphs in civil aircraft maintenance, focusing on event domain, fault domain, and product domain. The ontology construction process follows the “ontology seven-step method.” Finally, several applications, including information retrieval, intelligent question answering, and optimal route recommendation, are implemented using this knowledge graph.

关键词： Fault diagnosis Knowledge graphs Ontologies Information retrieval Question answering (information retrieval) Maintenance Safety Aircraft manufacture Aircraft Prognostics and health management

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Extracting and Classifying 3D Medical Image Texture Characteristics using GLCM and LBP-Based Techniques

Extracting and Classifying 3D Medical Image Texture Characte...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Srinath R. Sharmila N. Raghavendran M. C. Babu K. Ramanan R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Date Science RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Recent advances in 3D sensor technology have advanced 3D imaging systems. This sparks interest in 3D picture feature extraction and categorization. According to the literature, textural content is essential to visuals. In this case, we extract texture characteristics from three-dimensional photos to improve feature differentiation. Category of volumetric data with texture is possible using this method. We employ feature vectors from Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix methods to achieve this. The visual pattern and volumetric data contrast, homogeneity, and local anisotropy are described. The proposed approach was tested using a publicly accessible dataset of modified volumetric textured pictures. Generative Adversarial Networks (GAN) and recurrent Neural Networks (RNN) are classified. Our solution outperforms manually constructed techniques for extracting texture characteristics from 3D and 2D data and standard deep-learning networks. Even with few photographs per class, the recommended method improves class distinction and yields good results. Show exceptional accuracy. Integrating the Gray Level Co-occurrence Matrix (GLCM) with Local Binary Patterns (LBP) improved classification performance. The GLCM approach, which captures spatial relationships, and the LBP method, which depicts local texture, was advantageous. This hybrid method improved feature extraction and classification, notably for complex 3D medical pictures with subtle texture changes.

关键词： Deep learning Visualization Three-dimensional displays Machine learning algorithms Recurrent neural networks Liver Feature extraction Generative adversarial networks Classification algorithms Tumors

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Fast Latent Factor Analysis via a Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm

arXiv

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arXiv 2023年

作者： Li, Jinli Yuan, Ye School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

A high-dimensional and incomplete (HDI) matrix can describe the complex interactions among numerous nodes in various big data-related applications. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm learns a latent factor relying on the stochastic gradient of current instance error only without considering past update information. To address this critical issue, this paper innovatively proposes a Fuzzy PID-incorporated SGD (FPS) algorithm with two-fold ideas: 1) rebuilding the instance learning error by considering the past update information in an efficient way following the principle of PID, and 2) implementing hyper-parameters and gain parameters adaptation following the fuzzy rules. With it, an FPS-incorporated LFA model is further achieved for fast processing an HDI matrix. Empirical studies on six HDI datasets demonstrate that the proposed FPS-incorporated LFA model significantly outperforms the state-of-the-art LFA models in terms of computational efficiency for predicting the missing data of an HDI matrix with competitive accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network

arXiv

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arXiv 2022年

作者： Nie, Yuxiang Huang, Heyan Wei, Wei Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Beijing Institute of Technology Southeast Academy of Information Technology China Huazhong University of Science and Technology China

Long document question answering is a challenging task due to its demands for complex reasoning over long text. Previous works usually take long documents as non-structured flat texts or only consider the local structure in long documents. However, these methods usually ignore the global structure of the long document, which is essential for long-range understanding. To tackle this problem, we propose Compressive Graph Selector Network (CGSN) to capture the global structure in a compressive and iterative manner. Specifically, the proposed model consists of three modules: local graph network, global graph network and evidence memory network. Firstly, the local graph network builds the graph structure of the chunked segment in token, sentence, paragraph and segment levels to capture the short-term dependency of the text. Secondly, the global graph network selectively receives the information of each level from the local graph, compresses them into the global graph nodes and applies graph attention into the global graph nodes to build the long-range reasoning over the entire text in an iterative way. Thirdly, the evidence memory network is designed to alleviate the redundancy problem in the evidence selection via saving the selected result in the previous steps. Extensive experiments show that the proposed model outperforms previous methods on two datasets.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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