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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jing Xu, Yuanrong Dong, Suyu Wang, Wei Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Harbin Institute of Technology School of Computer Science and Technology Weihai China Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350386226

Faced with an escalating number of fingerprint images, most existing retrieval approachs suffer from a common problem: diminishing computational efficiency. This paper presents a hierarchical retrieval system tailored for high-resolution fingerprint images that utilizes abundant pore features and robust recognizability to improve retrieval performance. The framework comprises two core components. Firstly, a CNN-based feature extraction network is established, incorporating an attention mechanism to capture pore features in fingerprint images comprehensively. Subsequently, a hierarchical fingerprint retrieval approach is introduced, involving connection graph construction and a hierarchy of jump table structures for efficient retrieval of query pores. Empirical experiments conducted on high-resolution fingerprint image datasets underscore the system's effectiveness. Compared with other advanced pore-based fingerprint retrieval methods, the proposed method exhibits a notable rise in the hit rate with reduced penetration rates, significantly reducing the retrieval time. © 2024 IEEE.

关键词： Image enhancement

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IndoCL: Benchmarking Indonesian Language Development Assessment

IndoCL: Benchmarking Indonesian Language Development Assessm...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lin, Nankai Wu, Hongyan Zheng, Weixiong Liao, Xingming Jiang, Shengyi Yang, Aimin Xiao, Lixian School of Computer Science and Technology Guangdong University of Technology China College of Computer National University of Defense Technology China School of Information Technology and Engineering Guangzhou College of Commerce China School of Computer Science and Intelligence Education Lingnan Normal University China Faculty of Asian Languages and Cultures Guangdong University of Foreign Studies China

ISBN: (纸本)9798891761681

Recently, the field of language acquisition (LA) has significantly benefited from natural language processing technologies. A crucial task in LA involves tracking the evolution of language learners' competence, namely language development assessment (LDA). However, the majority of LDA research focuses on high-resource languages, with limited attention directed toward low-resource languages. Moreover, existing methodologies primarily depend on linguistic rules and language characteristics, with a limited exploration of exploiting pre-trained language models (PLMs) for LDA. In this paper, we construct the IndoCL corpus (Indonesian Corpus of L2 Learners), which comprises compositions written by undergraduate students majoring in Indonesian language. Moreover, we propose a model for LDA tasks, which automatically extracts language-independent features, relieving laborious computation and reliance on specific language. The proposed model uses sequential information attention and similarity representation learning to capture the differences and common information from the first-written and second-written essays, respectively. It has demonstrated remarkable performance on both our self-constructed corpus and publicly available corpora. Our work could serve as a novel benchmark for Indonesian LDA tasks. We also explore the feasibility of using existing large-scale language models (LLMs) for LDA tasks. The results show significant potential for improving LLM performance in LDA tasks. © 2024 Association for Computational Linguistics.

关键词： Students

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An Exploration of Data Augmentation Techniques in Ensemble Learning for Medical Image Segmentation with Transfer Learning

An Exploration of Data Augmentation Techniques in Ensemble L...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Swati Gupta, Namit Prakash, Febin Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

this paper examines using data augmentation strategies in the ensemble, getting to know medical photo segmentation with transfer learning. Various transfer-gaining knowledge of techniques, namely pretrained models, unsupervised function mastering, and multitasking studying, are explored. Pre-skilled models are skilled in one area and further high-quality-tuned using information from any other area to enhance segmentation overall performance. Unsupervised characteristic learning creates a common characteristic space that encodes the shared styles between numerous datasets. Multitask mastering combines challenge-particular multitasking getting to know, and feature-particular studying into a single, more accurate version. Records augmentation strategies unique to scientific photos, such as random cropping, random flipping, random rotation, and affine transformation, are mentioned. The effectiveness of different records augmentation strategies is evaluated on several scientific datasets, such as liver and lung datasets. Effects show combining statistics augmentation techniques with ensemble learning can drastically enhance segmentation accuracy. The look presents further evidence that information augmentation strategies can correctly be used for the clinical image segmentation venture. © 2024 IEEE.

关键词： augmentation effectively learning suggests transformation

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Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey

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ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Xu, Huali Zhi, Shuaifeng Sun, Shuzhou Patel, Vishal Liu, Li College of Computer Science Nankai University Tianjin China College of Electronic Science and Technology National University of Defense Technology Changsha China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Johns Hopkins University Baltimore United States

While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this, Few-shot learning (FSL) enables models to perform the target tasks with very few labeled examples by leveraging prior knowledge from related tasks. However, traditional FSL assumes that both the related and target tasks come from the same domain, which is a restrictive assumption in many real-world scenarios where domain differences are common. To overcome this limitation, Cross-domain few-shot learning (CDFSL) has gained attention, as it allows source and target data to come from different domains and label spaces. This article presents the first comprehensive review of Cross-domain Few-shot Learning (CDFSL), a field that has received less attention compared to traditional FSL due to its unique challenges. We aim at providing both a position article and a tutorial for researchers, covering key problems, existing methods, and future research directions. The review begins with a formal definition of CDFSL, outlining its core challenges, followed by a systematic analysis of current approaches, organized under a clear taxonomy. Finally, we discuss promising future directions in terms of problem setups, applications, and theoretical advancements. © 2025 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Cross-modal image text retrieval based on Cross-optimization and Joint strategy

Cross-modal image text retrieval based on Cross-optimization...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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A fault recognition method based on clustering linear regression

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High technology Letters 2023年第4期29卷 406-415页

作者：陈雷 SHI Jiaqi ZHANG Ting Computer School Beijing Information Science and Technology UniversityBeijing 100101P.R.China Faculty of Information Technology Beijing University of TechnologyBeijing 100124P.R.China

Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clustering linear regression is ***,Hough transform is used to detect the line segment of the enhanced image obtained by the coherence cube ***,the endpoint of the line segment detected by Hough transform is taken as the key point,and the adaptive clustering linear regression algorithm is used to cluster the key points adaptively according to the lin-ear relationship between ***,a fault is generated from each category of key points based on least squares curve fitting method to realize fault *** verify the feasibility and pro-gressiveness of the proposed method,it is compared with the traditional method and the latest meth-od on the actual seismic data through experiments,and the effectiveness of the proposed method is verified by the experimental results on the actual seismic data.

关键词： fault recognition clustering linear regression curve fitting seismic interpreta-tion

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Generating Markov Logic Networks Rulebase Based on Probabilistic Latent Semantics Analysis

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Tsinghua science and technology 2023年第5期28卷 952-964页

作者： Shan Cui Tao Zhu Xiao Zhang Liming Chen Lingfeng Mao Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Computer School University of South ChinaHengyang 421001China School of Computing Ulster UniversityBelfastBT151APUK

Human Activity Recognition(HAR)has become a subject of concern and plays an important role in daily *** uses sensor devices to collect user behavior data,obtain human activity information and identify *** Logic Networks(MLN)are widely used in HAR as an effective combination of knowledge and *** can solve the problems of complexity and uncertainty,and has good knowledge expression ***,MLN structure learning is relatively weak and requires a lot of computing and storage ***,the MLN structure is derived from sensor data in the current *** that the sensor data can be effectively sliced and the sliced data can be converted into semantic rules,MLN structure can be *** this end,we propose a rulebase building scheme based on probabilistic latent semantic analysis to provide a semantic rulebase for MLN *** a rulebase can reduce the time required for MLN structure *** apply the rulebase building scheme to single-person indoor activity recognition and prove that the scheme can effectively reduce the MLN learning *** addition,we evaluate the parameters of the rulebase building scheme to check its stability.

关键词： Markov Logic Network(MLN) structure learning rulebase construction probabilistic latent semantics

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Web Threat Recognition Model Based on Anomaly Detection and Attention Mechanism Seq2Seq Network

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1742-1759页

作者： Zhang, Guang-Hua Zhang, Kai-Di Yan, Jia School of Information Science and Engineering Hebei University of Science Technology Shijiazhuang050018 China Finance Department Hebei Youth Management Cadre College Shijiazhuang050031 China

With the rapid development of the Internet, Web threat identification is crucial. However, the existing Web threat recognition models have the following problems: inability to identify untrained attacks, poor overall performance, and limited generalization ability. To address these problems, this paper proposes a web threat detection model based on anomaly detection and the attention mechanism of Seq2Seq networks. Firstly, a vocabulary based on ASCII codes is established for data processing, and the Seq2Seq network is utilized for encoding and decoding operations to enhance the model’s generalization ability. Secondly, an anomaly detection approach is employed to train the model only on normal requests, thereby detecting attacks that have not been encountered during training. Finally, the attention mechanism is introduced to allocate different weights and scores to each time step, enhancing the model’s *** results demonstrate that the proposed model performs better than existing models. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Cyber attacks

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Universal Adversarial Attack for Trustworthy LiDAR based Object Detection in Embedded Applications 4

Universal Adversarial Attack for Trustworthy LiDAR based Obj...

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4th International Conference on Intelligent technology and Embedded Systems, ICITES 2024

作者： Cai, Mumuxin Wang, Xupeng Sohel, Ferdous Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Information Technology Murdoch University Murdoch Australia

ISBN: (纸本)9798331506292

3D object detection based on deep neural networks (DNNs) has widely been adopted in the field of embedded applications, such as autonomous driving. Nonetheless, recent studies have demonstrated that LiDAR data tends to exhibit intense corruptions, resulting in the failure of 3D object detection tasks. Considering the vulnerability of existing DNNs and the wide applications of 3D object detection to safety-critical scenarios, the robustness of deep 3D detection models under adversarial attacks is investigated in this work. The proposed universal adversarial attack is encoded into a perturbation voxel, which adds point-wise perturbations to benign LiDAR scenes. The detector-level perturbation voxel is generated by suppressing the detector's predictions on training samples, which covers the entire perceptual range of the detector. The designed perturbation voxels can be applied to the entire scene to simulate the global perturbation inherent in LiDAR. And it can adapt to other detectors with various point cloud representations, which makes attack universal. To test the effectiveness of the proposed attack, it was launched against a number of deep 3D detectors using several datasets. The results demonstrate its superiority over existing art. © 2024 IEEE.

关键词： 3D object detection LiDAR sensors Point cloud Security of embedded applications Universal adversarial attack

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PillarTsAE: A High-Performance Pillar-based 3D Object Detection Network in LIDAR Point Clouds 8

PillarTsAE: A High-Performance Pillar-based 3D Object Detect...

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Liu, Xinzhe Li, Wenju Cui, Liu He, Liuqin Shanghai Institute of Technology Shanghai of Computer Science and Information Engineering Shanghai China Shanghai Institute of Technology Shanghai of Computer Science and Information Shanghai China

ISBN: (纸本)9798350369564

3D object detection plays a crucial role in many fields such as autonomous driving, robot perception and other fields. Current methods encounter limitations when dealing with intricate point cloud data, such as poor performance in detecting small objects and the high computational overhead they exhibit. This study proposes a high-performance network dubbed PillarTsAE, addressing problems above effectively. Specifically, it consists of two novel modules: TsAFE and PPFE, enhancing the expression of point cloud features and examining both inter-pillar and intra-pillar relational features, respectively. Finally, PillarTsAE is equipped with BiFPN in neck module. Sufficient experiments demonstrate that our method outperforms other models, achieving state-of-the-art performance on KITTI with mAP, showcasing its powerful and robust ability. © 2023 IEEE.

关键词： Feature extraction

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