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KeyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

Big Data Mining and Analytics 2024年第2期7卷 547-560页

作者： Junwen Duan Xincheng Liao Ying An Jianxin Wang Hunan Key Laboratory of Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

关键词： natural language processing Event Extraction(EE) Multi-Prompt Learning(MPL) low-resource

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On the parameterized complexity of minimum/maximum degree vertex deletion on several special graphs

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Frontiers of computer science 2023年第4期17卷 97-107页

作者： Jia LI Wenjun LI Yongjie YANG Xueying YANG School of Information Engineering Hunan Industry PolytechnicChangsha 410036China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410015China Chair of Economic Theory Saarland UniversitySaarbrücken 66123Germany

In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the distinguished vertex has the unique minimum *** maximum degree vertex deletion problem is defined analogously but here we want the distinguished vertex to have the unique maximum *** is known that both problems areΨ-hard and fixed-parameter intractable with respect to some natural *** this paper,we study the(parameterized)complexity of these two problems restricted to split graphs,p-degenerate graphs,and planar *** study provides a comprehensive complexity landscape of the two problems restricted to these special graphs.

关键词： minimum degree maximum degree vertex deletion split graphs planar graphs parameterized complexity

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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LV-auth: Lip Motion Fusion for Voiceprint Authentication 18th

LV-auth: Lip Motion Fusion for Voiceprint Authentication

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Liu, Wei Zhu, Xiaojing Liu, Qin Li, Peng Zhou, Man School of Cyber Science and Engineering Wuhan University Wuhan430072 China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031714634

Voiceprint authentication is a growing field of biometric authentication. However, voiceprint authentication is vulnerable to spoofing attacks and suffers significant degradation in noisy environments. Employing lip motion detection is an innovative solution to enhance security. By strategically selecting a clean channel, such as the ultrasonic frequencies generated by smartphones’ inherent audio capability, lip motion detection can be performed without disturbance from ambient noise. In this paper, we propose LV-auth, a smartphone-based multimodal biometric authentication system that integrates lip motion and voiceprint. LV-auth captures both the speech signals and the ultrasonic echo signals modulated by lip motion, enabling liveness detection while enhancing robustness. We employ a contrastive learning-based feature extractor to extract reliable and effective fused features from both modalities for authentication. Extensive experiments show that LV-auth achieves an authentication accuracy of 99.37% and an equal error rate (EER) of 0.73% on the smartphone. In particular, LV-auth demonstrates excellent robustness in noisy environments and effectively withstands spoofing attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smartphones

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Modeling of Complex Traffic Interaction Under Uncontrolled Intersection Scenario

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Li, Jiangkun Deng, Weiwen Wang, Ying Wang, Jian Ding, Juan School of Transportation Science and Engineering Beihang University Beijing China College of Computer Science and Technology Jilin Univerisity Changchun China PanoSim Technology Company Limited Jiaxing China

Complex traffic scenarios at uncontrolled intersections are crucial for the test validation of autonomous driving systems. The core of the test scenario construction lies in the accurate modeling of the complex interaction behaviors between vehicles in dynamic traffic. Data-driven models are difficult to support long-term simulation due to the existence of cumulative errors. In addition, existing mechanistic models usually assume rational driver behavior and focus mainly on improving efficiency and safety, thus simplifying vehicle interactions. To overcome the limitations of existing studies, we construct a complex traffic interaction model based on social force theory. This model captures the intricate interactions among vehicles at uncontrolled intersections by introducing the concepts of driving and repulsive forces. In particular, we propose a novel concept of segmented conflicting repulsion, an approach that can accurately model high-risk scrambling interactions between vehicles at intersections. Validation of the model using real data sets demonstrates its ability to accurately reproduce complex interaction behaviors at real-world intersections. Further, simulation analysis and application results reveal that our model-generated scenarios significantly outperform those created by SUMO in terms of complexity, thereby effectively enhancing the safety assessment of the autonomous driving system, Apollo. IEEE

关键词： Safety testing

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ESR: Optimizing Gene Feature Selection for scRNA-seq Data 10

ESR: Optimizing Gene Feature Selection for scRNA-seq Data

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Huang, Tao Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis. © 2023 IEEE.

关键词： Entropy

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CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework

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Multimedia Tools and Applications 2025年第15期84卷 15501-15526页

作者： Agrawal, Rohit Singh, Navneet Pratap Shelke, Nitin Arvind Tripathi, Kuldeep Narayan Singh, Ranjeet Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science Race Course Road MP Gwalior474005 India School of AI Bennett University Uttar Pradesh Greater Noida201310 India School of Computer Science and Engineering Bennett University TechZone 2 UP Greater Noida201310 India

Breast cancer is a major health concern for women worldwide, and early detection is vital to improve treatment outcomes. While existing techniques in mammogram classification have demonstrated promising results, their limitations become apparent when applied to larger datasets. The decline in performance with increased dataset size highlights the need for further research and advancements in the field to enhance the scalability and generalizability of these techniques. In this study, we propose a framework to classify breast cancer from mammograms using techniques such as mammogram enhancement, discrete cosine transform (DCT) dimensionality reduction, and deep convolutional neural network (DCNN). The first step is to improve the mammogram display to improve the visibility of key features and reduce noise. For this, we use 2-stage Contrast Limited Adaptive Histogram Equalization (CLAHE). DCT is then used to enhance mammograms to reduce residual data. It can provide effective reduction while preserving important diagnostic information. In this way, we reduce the computational complexity and increase the results of subsequent classification algorithms. Finally, DCNN is used on size-reduced DCT coefficients to learn feature discrimination and classification of mammograms. DCNN architectures have been optimized with various techniques to improve their performance, including regularization and hyperparameter tuning. We perform experiments on the DDSM dataset, a large dataset containing approximately 55,000 mammogram images, and demonstrate the effectiveness of the proposed method. We assess the proposed model’s performance by computing the precision, recall, accuracy, F1-Score, and area under the receiver operating characteristic curve (AUC). We achieve Precision and Recall values of 0.929 and 0.963, respectively. The classification accuracy of the proposed models is 0.963. Moreover, the F1-Score and AUC values are 0.962 and 0.987, respectively. These results are better a

关键词： Discrete cosine transforms

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Business Process Remaining Time Prediction Based on Incremental Event Logs

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IEEE Transactions on Services Computing 2025年第3期18卷 1308-1320页

作者： Guo, Na Liu, Cong Mo, Qi Cao, Jian Ouyang, Chun Lu, Xixi Zeng, Qingtian Shandong University of Technology School of Electrical and Electronic Engineering Zibo255000 China Nova University of Lisbon NOVA Information Management School Lisbon1070-312 Portugal Shandong University of Technology School of Computer Science and Technology Zibo255000 China Yunnan University School of Software Kunming650091 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200030 China Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Utrecht University Department of Information and Computing Sciences CS Utrecht3584 Netherlands Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China

Predictive Process Monitoring (PPM) aims to predict the future state of running process instances to enable timely interventions to mitigate potential risks. As one of the most fundamental tasks in PPM, process remaining time prediction focuses on preventing timeout occurrences. While various deep learning-based approaches have been developed for this purpose, they often rely on pre-established static prediction models and struggle to maintain accurate predictions when the process undergoes dynamic changes, such as an expanding sales channels. To tackle this challenge, this paper proposes an incremental process remaining time prediction framework by continuously updating the prediction model based on an incremental event log. Specifically, a feature selection strategy is first introduced to extract effective features from event logs. Leveraging effective features can significantly improve the prediction quality by capturing the changes in process information. Then, three incremental log-based updating mechanisms, including period-based updating, quantity-based updating, and concept-drift-based updating, along with a reconstruction strategy, are proposed to dynamically adjust the prediction model in response to business changes. Finally, LSTM, Transformer, and Auto-encoder models are adapted and integrated into the proposed framework. The approach has been implemented and publicly released. Experimental evaluation using nine real-life event logs demonstrate that the proposed framework and its three instantiations (i.e., LSTM-based, Transformer-based, and Auto-encoder-based ones) outperform state-of-the-art techniques in terms of prediction accuracy. © 2008-2012 IEEE.

关键词： Predictive Models Feature Extraction Business Adaptation Models Accuracy Long Short Term Memory Transformers Process Control Computational Modeling Process Monitoring Predictive Process Monitoring Remaining Time Prediction Process Mining Incremental Event Log Business Processes Event Log Prediction Model Prediction Accuracy Dynamic Changes Effects Of Characteristics Long Short Term Memory Static Model Prediction Framework Continuous Updating Incremental Process Reconstruction Scheme Neural Network Training Set Convolutional Neural Network Change Process Hidden Layer Combination Of Features Recurrent Neural Network Flow Control Neural Units Continuous Bag Of Words Mean Absolute Error Values Concept Drift Improve Prediction Accuracy Construction Of Prediction Model Dynamic Nature Of Processes Real Life Scenarios Prediction Task Incremental Learning

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Heterogeneous Device Collaboration Based Federated Learning for Big Data Applications

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IEEE Transactions on Big Data 2024年 1-11页

作者： Wang, Wenhua Yang, Quan Liang, Yuzhu Xu, Yang Liu, Qin Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing China College of Computer Science and Technology Huaqiao University Xiamen China College of Computer Science and Electronic Engineering Hunan University Changsha China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China

In the era of Big Data, artificial intelligence and information science are the key technologies to extract the value of data and enhance the competitiveness of enterprises. The characteristics of distributed, small-scale, and sparse lead to the isolated data island problem. To solve these problems, Federated Learning is proposed. However, a large number of terminal models need to be uploaded to the server in Federated Learning, especially for the actual scenario of Internet of Things. Therefore, huge communication costs are required which dramatically increases the pressure on the backbone network. Furthermore, the low quality of the local model will lead to decreased accuracy and convergence rates of the model. To overcome the above limitations, we propose heterogeneous device collaboration based federated learning (HDCFL), which constructs a three-layer structure for Federated Learning by leveraging edge computing and designs a heterogeneous device collaboration method that groups the terminals based on their computing power, communication time, and data volume to train the model. Then, we conduct a theoretical analysis of the proposed algorithm which verifies its advantage. At last, the experimental result demonstrates that the proposed algorithm consistently achieves superior performance in terms of both convergence speed and accuracy compared with state-of-the-art baselines. IEEE

关键词： Competition

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TransEffiVisNet – an image captioning architecture for auditory assistance for the visually impaired

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Multimedia Tools and Applications 2024年 1-20页

作者： R, Harshitha B, Lakshmipriya Krishnamurthy, Vallidevi School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Engineering Jawaharlal Nehru University New Delhi India

Insights derived out of image captioning systems have potential applications in real life, including providing auditory assistance for the visually impaired. This paper proposes TransEffiVisNet, a novel image captioning system embedded with text-to-speech generation, which can be deployed to aid individuals with visual impairments. The proposed model combines a convolutional neural network (CNN) and a transformer-based encoder and decoder to generate descriptive captions for the visual content provided as an image input. A comparative study was conducted to assess the performance of the proposed model formulated using five prominent CNN models—VGG16, ResNet50, EfficientNetB0, EfficientNetB1, and Inception V3, while maintaining the same transformer architecture. The image captioning model with EfficientNetB1 showcased phenomenal results compared to four other architectures and hence preferred to be used in TransEffiVisNet. Integration of Google Text-to-Speech technology to convert captions into speech further enhances the functionality of the model which in future can be deployed for visually impaired assistance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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