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8th IEEE International Conference on Big Data Security on Cloud, 8th IEEE International Conference on High Performance and Smart Computing, and 8th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Liang, Chenjun Deng, Li Zhu, Jincan Cao, Zhen Li, Chao College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Hubei University Department of Information Development and Management Wuhan China

ISBN: (纸本)9781665480697

With the rapid development of cloud storage, the number of disks has increased dramatically, and the absolute number of failed disks has also continued to increase. Since the impact of these failures on the quality of cloud services cannot be ignored, the demand for disk failure prediction is also increasing. In addition, the accuracy of the disk failure prediction algorithm largely depends on the input data set. Therefore, according to the large difference in the feature morphology in the disk tracking dataset, we analyze the change trend of features in different dimensions, and propose a new method. The SW-Disk feature engineering method for disk failure prediction constructs features from both horizontal and vertical dimensions. Including feature crossover between indicators and indicators to construct higher-order features. In the time dimension, the time series features under the multi-scale sliding window are constructed. We use the random forest prediction model to predict whether the disk will fail in the next 30 days, so as to reserve more time to take measures on the disk to achieve the purpose of improving the reliability of the cloud storage system. We validated our SW-Disk feature engineering method and prediction model on the Backblaze2019 disk dataset, and the experimental results demonstrate the effectiveness and superiority of our proposed method. © 2022 IEEE.

关键词： Decision trees

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Quasiperiodic photonic crystal fiber[Invited]

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Chinese Optics Letters 2023年第6期21卷 17-35页

作者：刘娥贤刘建军 College of Computer and Information Engineering Central South University of Forestry and TechnologyChangsha 410004China Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education&Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices School of Physics and ElectronicsHunan UniversityChangsha 410082China

In the fields of light manipulation and localization,quasiperiodic photonic crystals,or photonic quasicrystals[PQs],are causing an upsurge in research because of their rotational symmetry and long-range orientation of transverse lattice arrays,as they lack translational *** allows for the optimization of well-established light propagation properties and has introduced new guiding ***,as a class,quasiperiodic photonic crystal fibers,or photonic quasicrystal fibers[PQFs],are considered to add flexibility and richness to the optical properties of fibers and are expected to offer significant potential applications to optical fiber *** this review,the fundamental concept,working mechanisms,and invention history of PQFs are *** progress in optical property improvement and its novel applications in fields such as dispersion control,polarization-maintenance,supercontinuum generation,orbital angular momentum transmission,plasmon-based sensors and filters,and high nonlinearity and topological mode transmission,are then reviewed in ***-type air-guiding PQFs supporting low attenuation propagation and regulation of photonic density states of quasiperiodic cladding and in which light guidance is achieved by coherent Bragg scattering are also ***,current challenges encountered in the guiding mechanisms and practical preparation techniques,as well as the prospects and research trends of PQFs,are also presented.

关键词： photonic quasicrystal fiber guiding mechanism guiding features and applications challenges and prospects

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Constant-Weight Group Coded Bloom Filter for Multiple-Set Membership Queries 10th

Constant-Weight Group Coded Bloom Filter for Multiple-Set Me...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Tian, Xiaomei Zhao, Huihuang College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

Multiple-set membership query problem plays a very important role in many network systems, including packet forwarding architectures in big data centers, routing switching devices and internet firewall systems. Many solutions to multiple-set membership query problem are based on Bloom filters. And each of these solutions has its own merits. Some have high query speed and some have good accuracy. But generally, they cannot have both at the same time. In this paper, we propose a probabilistic data structure named constant-weight group coded Bloom Filter (CWGCBF) for fast and accurate multi-set membership query. The key technique in CWGCBF is encoding each elements set ID to constant weight code, and directly recording this constant-weight code in the bloom filter vector. Experimental results show that in terms of false positive ratio, CWGCBF, Shifting Bloom Filter and Combinatorial Bloom Filter have great advantages to the other compared Bloom filters, and in terms of time efficiency, CWGCBF and ID Bloom Filter are several times faster than the others. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data structures

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Research on Task Scheduling in Distributed Vulnerability Scanning System 10th

Research on Task Scheduling in Distributed Vulnerability Sca...

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10th International Conference on computer Engineering and Networks, CENet 2020

作者： Jiang, Jie Tang, Sixin College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811584619

Network threats caused by system vulnerabilities is increasing gradually, the distributed vulnerability scanning system can scan large-scale and complex networks and report the vulnerability information. Task scheduling is one of the core components in a distributed system. In this paper, we use dynamic optimization algorithms to improve the task scheduling efficiency of distributed vulnerability scanning system, we propose a PSO-based task scheduling scheme and improves the search ability of particles by adjusting algorithm parameters. We compared the time consume when using existing ‘Resource Aware Scheduling algorithm (RASA) with the basic particle swarm optimization (PSO) algorithm and the improved particle swarm optimization (IPSO) algorithm. Our results show that IPSO has better performance than other scheduling methods. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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GuideNER: Annotation Guidelines Are Better than Examples for In-Context Named Entity Recognition 39

GuideNER: Annotation Guidelines Are Better than Examples for...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Shizhou Xu, Bo Yu, Yang Li, Changqun Lin, Xin Alex School of Computer Science and Technology East China Normal University Shanghai China School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing China

ISBN: (纸本)157735897X

Large language models (LLMs) demonstrate impressive performance on downstream tasks through in-context learning (ICL). However, there is a significant gap between their performance in Named Entity Recognition (NER) and in fine-tuning methods. We believe this discrepancy is due to inconsistencies in labeling definitions in NER. In addition, recent research indicates that LLMs do not learn the specific input-label mappings from the demonstrations. Therefore, we argue that using examples to implicitly capture the mapping between inputs and labels in in-context learning is not suitable for NER. Instead, it requires explicitly informing the model of the range of entities contained in the labels, such as annotation guidelines. In this paper, we propose GuideNER, which uses LLMs to summarize concise annotation guidelines as contextual information in ICL. We have conducted experiments on widely used NER datasets, and the experimental results indicate that our method can consistently and significantly outperform state-of-the-art methods, while using shorter prompts. Especially on the GENIA dataset, our model outperforms the previous state-of-the-art model by 12.63 F1 scores. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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computer Science and Blockchain (CCSB), International Conference on

作者： Guimin Huang Canjun Shi Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance.

关键词： Computational modeling Semantics Knowledge based systems Syntactics Predictive models Feature extraction Vectors Natural language processing Convolutional neural networks Mutual information

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Knowledge tracking model based on recurrent neural network and transformer 8

Knowledge tracking model based on recurrent neural network a...

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8th International Symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2023

作者： Cheng, Yan Zhao, Songhua Hu, Jiansheng Zou, Haifeng Luo, Pin Fu, Yan Zhong, Linhui Liu, Chunlei School of Computer and Information Engineering Jiangxi Normal University Nanchang330022 China Jiangxi Province Science and Technology Innovation Base-Provincial Key Laboratory "Jiangxi Province Key Laboratory of Intelligent Education" Nangchang330022 China Jiangxi Ruanyun Technology Corporation Limited Nanchang330096 China Jiangxi Heyi Keji Youxian Gongsi Nanchang330096 China

ISBN: (纸本)9781510666290

With the continuous development of online education platform, knowledge tracking (KT) has become a key technology to help online education platform provide personalized education. However, the existing knowledge tracking model based on recurrent neural network is difficult to be used for the input of long sequence, and has the problem of long-term dependence. Secondly, although the knowledge tracking model based on Transformer does not have the problem of long-term dependence, it is difficult to capture the input sequence information. Therefore, this paper proposes a knowledge tracking model based on recurrent neural network and transformer. A new position coding method is designed, and LSTM is used to replace the position coding method of Transformer to encode sequence features, so that the model in this paper can not only capture the input sequence information, but also get rid of the long-term dependency problem based on the recurrent neural network, and use GRU network to capture the context information. In addition, an adaptive fusion gate is designed to fuse the global features and context features obtained by Transformer, and use the fused features to predict the students' answers to the next question. In addition, an adaptive fusion gate is designed to fuse the global features and context features obtained by Transformer, and use the fused features to predict the students' answers to the next question. © 2023 SPIE. All rights reserved.

关键词： Students

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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A Tibetan Ancient Uchen Text Line Dataset for OCR

A Tibetan Ancient Uchen Text Line Dataset for OCR

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Image processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Zhuome Gongqu Peng Luo Dongzhou Jiayang Jia Cairang Jiacuo Cizhen Dongzhu Renqing The College of Computer Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining China The College of Information Science and Technology Tibet University Lhasa China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Characters written by hand that represent distinct writing styles are referred to as handwritten text; these are especially common in ancient texts. In the field of digitizing ancient literature, Tibetan ancient document image recognition is a focus of both domestic and international research. However, technological advancement in this field has been hampered by the absence of a specific recognition dataset for image recognition systems of Tibetan ancient documents. This paper addresses this by proposing a dataset for Uchen style text line recognition in Tibetan ancient documents. This dataset includes basic synthetic images of multi-style Uchen text lines as well as real-world images of multi-format Tibetan Uchen text lines that have been manually annotated. Cross-Validation and several rounds of annotation by various people guarantee the quality of the data. Additionally, three popular text line recognition models are used to assess the dataset and validate its usability.

关键词： Hands computer vision Image recognition Text recognition Annotations Computational modeling Optical character recognition Machine learning Writing Usability

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