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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： He, Haibin Ye, Maoyuan Zhang, Jing Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University China College of Computing & Data Science Nanyang Technological University Singapore

Beyond the text detection and recognition tasks in image text spotting, video text spotting presents an augmented challenge with the inclusion of tracking. While advanced end-to-end trainable methods have shown commendable performance, the pursuit of multi-task optimization may pose the risk of producing sub-optimal outcomes for individual tasks. In this paper, we identify a main bottleneck in the state-of-the-art video text spotter: the limited recognition capability. In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance. To adapt the image text spotter to video datasets, we add a rescoring head to rescore each detected instance's confidence via efficient tuning, leading to a better tracking candidate pool. Additionally, we design a long-short term matching module, termed LST-Matcher, to enhance the spotter's tracking capability by integrating both long- and short-term matching results via Transformer. Based on the above simple designs, GoMatching delivers new records on ICDAR15-video, DSText, BOVText, and our proposed novel test set with arbitrary-shaped text termed ArTVideo, which demonstrates GoMatching's capability to accommodate general, dense, small, arbitrary-shaped, Chinese and English text scenarios while saving considerable training budgets. © 2024 Neural information processing systems foundation. All rights reserved.

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Secure Formation Control of Multi-Agent System against FDI Attack Using Fixed-Time Convergent Reinforcement Learning

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IEEE Transactions on Control of Network Systems 2025年第2期12卷 1203-1214页

作者： Gong, Zhenyu Yang, Feisheng Yuan, Yuan Ma, Qian Zheng, Wei Xing Northwestern Polytechnical University School of Automation and Innovation Center NPU Chongqing Shaanxi Xi'an710129 China Northwestern Polytechnical University School of Astronautics Shaanxi Xi'an710072 China Nanjing University of Science and Technology School of Automation Jiangsu Nanjing210094 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

In this paper, a fixed-time convergent reinforcement learning (RL) algorithm is proposed to accomplish the secure formation control of a second-order multi-agent system (MAS) under the false data injection (FDI) attack. To alleviate the FDI attack on the control signal, a zero-sum graphical game is introduced to analyze the attack-defense process, in which the secure formation controller intends to minimize the common performance index function, whereas the purpose of the attacker is the opposite. Attaining the optimal secure formation control policy located at the Nash equilibrium depends on solving the game-associated coupled Hamilton-Jacobi-Isaacs equation. Taking into account fixed-time convergence, a critic-only online RL algorithm with the experience replay technique is designed. Meanwhile, the corresponding convergence and stability proofs are provided. A simulation example is presented to show the effectiveness of the devised scheme. © 2014 IEEE.

关键词： Adversarial machine learning

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Cross-Layer Optimization Strategies for Energy-Efficient and Reliable Machine-Type Communication in LPWANs 2

Cross-Layer Optimization Strategies for Energy-Efficient and...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Amanullah, M. Manoranjini, J. Sathiya, V. Helen Prabbu Sankar, P. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering Department of Artificial Intelligence and Data Science Chennai India Panimalar Engineering College Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331534950

In the massive Machine-Type Communication (mMTC), the exponential growth of Internet of Things (IoT) devices over Low Power Wide Area Networks (LPWANs) presents substantial issues regarding energy efficiency and stability. The existing frameworks frequently fails in the the intricate relationships among the MAC-layer and physical parameters. To enhanced cross-layer evaluation methodology that integrates adaptive data rate (ADR) optimization, real-time energy consumption monitoring, and dynamic downlink message handling to address the problems. To maintain stability and performance with the network situations, the framework makes use of a Control-Lyapunov function. To enable effective resource allocation uses the Successive Convex Approximation that improve the effects of various transmission systems under quasi-static and dynamic channel conditions. To verify the cross-layer interactions and it impacts the energy efficiency, the proposed method begins with the data collection and it moves to payload size and packet length, are regularly changed to examines the affect throughput and energy consumption. The findings show the high-density node situations, energy consumption that reduced while retaining reliable communication. Furthermore, the results show the interdependencies among network parameters, highlighting the necessity of an integrated strategy. This framework helps to ensure the mMTC scenarios that optimized by improving the long-term viability and dependability of IoT networks. © 2024 IEEE.

关键词： Resource allocation

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Comparison of multiclass classification techniques using dry bean dataset

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International Journal of Cognitive Computing in Engineering 2023年第1期4卷 6-20页

作者： Salauddin Khan, Md Nath, Tushar Deb Murad Hossain, Md Mukherjee, Arnab Bin Hasnath, Hafiz Manhaz Meem, Tahera Khan, Umama Statistics Discipline Science Engineering and Technology School Khulna University Khulna-9208 Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Modeling and Data Science University of Turin Italy Mathematics Discipline Science Engineering and Technology School Khulna University Khulna-9208 Bangladesh Biotechnology and Geneetic Engineering Discipline Life Science School Khulna University Khulna-9208 Bangladesh

Background: The application of classsification methods through multivariate and machine learning techniques has enormous significance in agricultural sector. It is vital to classify various types of seeds as well as identify the quality of seeds which has a great impact on the production of crops. There is a wide range of genetic variations in dry beans all over the world. Many studies have been conducted previously on various dataset to indentify the sorts of dry beans, however most of them focused on machine learning techniques with binary classification. Objective: The aim of this study is to investigate a reliable classifier which has the lowest noise implications and establish an algorithm for dry bean classification effectively. This paper focuses on outlier removals, oversampling with Adaptive Synthetic (ADASYN) algorithm and finding the best classifier to guarantee the highest possible accuracy. Methods: The raw dataset for this study was accessed from UCI Machine Learning Repository. The dataset contained grains having 16 features, 12 dimensions, and 4 distinct shapes. For the purpose of eliminating missing values from the dataset, interquartile range (IQR) with python programming was utilized. Eight most popular classifiers were used in this study which are Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Multilayer Perception (MLP) with balanced and imbalanced classes. The authors utilized frequency tables, bar diagrams, boxplots, analysis of variance for descriptive analysis as well as data preprocessing. Results: The XGB classifier preferably outperformed than other classifiers with balanced and imbalanced distribution of dry beans within each class. It has acquired accuracy (ACC) 93.0% and 95.4% in imbalanced and balanced classes respectively. In case of balanced dataset, after application of ADASYN algorithm both KNN and RF t

关键词： Classification (of information)

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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computer Modeling in Engineering & sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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Integrated Simulation Research of Multi-natural Energy-driven Unmanned Surface Vehicle

Integrated Simulation Research of Multi-natural Energy-drive...

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2023 International Conference on Applied Mathematics and Digital Simulation, AMDS 2023

作者： Chen, Ao Chen, Tianding He, Hong Wang, Hui Fan, Yimei Yu, Xin School of Physics and Information Engineering Minnan Normal University Fujian Province China School of Computing and Information Science Fuzhou Institute of Technology Fujian Province China School of Data and Computer Science Xiamen Institute of Technology Fujian Province China

In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV's equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations. © Published under licence by IOP Publishing Ltd.

关键词： DC-DC converters

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Automatic ICD Coding Based on Segmented ClinicalBERT with Hierarchical Tree Structure Learning 28th

Automatic ICD Coding Based on Segmented ClinicalBERT with ...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Kang, Beichen Wang, Xiaosu Xiong, Yun Zhang, Yao Zhou, Chaofan Zhu, Yangyong Zhang, Jiawei Tang, Chunlei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China IFM Lab Department of Computer Science University of California DavisCA United States Harvard Medical School BostonMA United States

ISBN: (纸本)9783031306778

Automatic ICD coding aims at assigning the international classification of disease (ICD) codes to clinical notes documented by clinicians, which is crucial for saving human resources and has attracted much research attention in recent years. However, facing the challenges brought by the complex long textual narratives in clinical notes and the long-tailed data distribution in ICD codes, existing studies are ineffectual in the struggle to extract key information from the clinical notes and handle large amounts of small-data learning problems on the tail codes, which makes it hard to achieve satisfactory performance. In this paper, we present a ClinicalBERT-based model for automatic ICD coding, which can effectively cope with complex long clinical narratives via a segmentation learning mechanism and take advantage of the tree-like structure of ICD codes to transmit information among code nodes. Specifically, a novel hierarchical tree structure learning module is proposed to enable each code to utilize information both from upper and lower nodes of the tree, so that better code classifiers are learned for both head and tail codes. Experiments on MIMIC-III dataset show that our model outperforms current state-of-the-art (SOTA) ICD coding methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clinical research

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VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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A Novel Deep Learning Model Architecture Design that Improves Skin Lesion Diagnosis 24

A Novel Deep Learning Model Architecture Design that Improve...

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8th International Conference on Electronic Information Technology and computer Engineering, EITCE 2024

作者： Lei, Gaoming Zhao, Jian Li, Ning Mei, Fengcheng Qin, Wei Liang, Lixin College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen China School of Computer Science and Software Shenzhen Institute of Information Technology Guangdong Shenzhen China

ISBN: (纸本)9798400710094

Mainstream deep learning models do not perform well in skin lesion image classification for skin disease diagnosis due to multiple factors, such as noise and illumination intensity variations of skin lesion images, similarity of lesion features, morphological diversity, small volume of datasets and unbalanced image samples. In order to improve the diagnosis performance, in this paper, we first preprocess skin lesion images through a new color constant algorithm to mitigate their illumination intensity variations. Then, we apply a novel deep learning model, which combines multi-scale convolution and attention mechanisms. In the model, stacked multi-scale convolution are used to obtain local and global features of lesion images. Both local and global features are fused as input of the flatten transformer shift window. This approach expands local receptive field of the shift window, focuses more on local and global lesion feature areas of a skin lesion and enhances relationship among local and global features. Extensive experiments demonstrate the efficacy of our method. Compared with other models, our proposed model pays more attention to features in the skin lesion areas. The overall accuracy rate, specificity, and sensitivity have increased by 2.91%, 0.58%, and 4.02% respectively. The overall F1-Score has reached the state-of-the-art value of 0.8841. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

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Journal of computer science & Technology 2023年第1期38卷 196-210页

作者：张照博钟芷漫袁平鹏金海 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

关键词： natural language processing(NLP) domain entity linking computational linguistics word sense disambiguation knowledge graph

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