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A topical VAEGAN-IHMM approach for automatic story segmentation

Mathematical Biosciences and engineering 2024年第7期21卷 6608-6630页

作者： Yu, Jia Peng, Huiling Wang, Guoqiang Shi, Nianfeng School of Computer and Information Engineering Luoyang Institute of Science and Technology China Software Research Institute Technological University of Shannon Ireland

Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance. © 2024 the Author(s).

关键词： Hidden Markov models

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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ER-Net:Efficient Recalibration Network for Multi-ViewMulti-Person 3D Pose Estimation

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computer Modeling in engineering & Sciences 2023年第8期136卷 2093-2109页

作者： Mi Zhou Rui Liu Pengfei Yi Dongsheng Zhou National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian116622China School of Computer Science and Technology Dalian University of TechnologyDalian116024China

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application *** the introduction of end-to-end direct regression methods,the field has entered a new stage of ***,the regression results of joints that are more heavily influenced by external factors are not accurate enough even for the optimal *** this paper,we propose an effective feature recalibration module based on the channel attention mechanism and a relative optimal calibration strategy,which is applied to themulti-viewmulti-person 3D human pose estimation task to achieve improved detection accuracy for joints that are more severely affected by external ***,it achieves relative optimal weight adjustment of joint feature information through the recalibration module and strategy,which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding *** call this method as the Efficient Recalibration Network(ER-Net).Finally,experiments were conducted on two benchmark datasets for this task,Campus and Shelf,in which the PCP reached 97.3% and 98.3%,respectively.

关键词： Multi-view multi-person pose estimation attention mechanism computer vision

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Fault diagnosis of chemical process based on CNN-BiGRU 24

Fault diagnosis of chemical process based on CNN-BiGRU

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Sun, Chengfu Ni, Yang Du, Rui Huaiyin Institute of Technology School of Computer and Software Engineering Huaian China

ISBN: (纸本)9798400718199

targeting issues where the fault features in the chemical production process are hard to discern and the fault diagnosis method's accuracy is low. This paper proposes an improved CNN-BiGRU (Convolutional Neural Networks and Bidirectional Gated Recurrent Unit) model, which is different from the usual serial structure, and adopts a parallel structure to combine the improved CNN and Bidirectional Gated Recurrent Unit. First, CNN is used to extract local features adaptively, and a batch normalization (BN) layer is used in CNN to avoid internal covariate shift and accelerate network training. In order to prevent overfitting and minimize network calculations, the global pooling layer is introduced. Second, BiGRU is used to extract global features from the data, and by incorporating a linear layer into the conventional BiGRU network, the model's accuracy is increased. Eventually, the fusion layer fuses the features that were extracted by the two networks. The experiment's outcomes demonstrate how well the model can raise fault detection accuracy. Taking the Eastman Process of Tennessee process (TE) as the research object, experiments show that the proposed method has excellent performance. © 2024 ACM.

关键词： Failure analysis

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A New Approach for an Introduction to computer Networking Course: A Case Study 5

A New Approach for an Introduction to Computer Networking Co...

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5th International Conference on Information technology and Education technology, ITET 2024

作者： Vogel, Joyce Bouhnik, Dan Jerusalem College of Technology Computer Science and Software Engineering Jerusalem Israel

ISBN: (纸本)9798331540425

The course Introduction to computer Networks (ICN) has become one of the most vital courses in computer Science and software engineering degrees and clearly is an imperative course for a degree in computer networking. However, despite its importance, the course suffers from many issues which lead to a high failure rate and low success. Even students who pass the course have problems understanding key concepts in data communication. One of the key issues is the theoretical nature of the material of the course. Many of the concepts are abstract and dry, which the students find hard to understand and boring. At the Jerusalem College of technology (JCT), we encountered such problems with our networking course. We therefore decided to suspend the course for a year, research successful networking courses, and apply our findings to a revised networking course. One of the strategic changes that was made was to introduce more practical material into the course. Top-down approach to networking replaced a bottom-up approach, hands-on lab were added to the course, and online quizzes were included. This paper presents some of our findings and changes that were made to the networking course. The problems that were present in our original version of the ICN course are not unique to the JCT but are common to networking courses throughout higher education institutions. We therefore believe that the findings of our research can benefit other faculty who wish to improve their networking courses. © 2024 IEEE.

关键词： Students

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Road Defect Detection Model Based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第4期52卷 986-994页

作者： Cong, Shengqiang Zhang, Chunna Yu, Yang Yue, Xiaoping Zhao, Jinchi Shen, Yuming School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China University of Science and Technology Liaoning Liaoning Anshan114051 China

The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and insufficient feature representation in complex scenarios, slow bounding box regression speed. To address these challenges, we propose the YOLOv8-DSW model. Firstly, we incorporate the Dilation-wise Residual (DWR) module into the C2f module in the neck, improving the detection performance of multi-scale feature maps. Secondly, We incorporate the SENetv2 attention mechanism into the neck of the YOLOv8 model to augment feature expression capabilities. Finally, we introduce Wise-IoU (WIoU) to enhance the speed of bounding box regression. Experimental results indicate that YOLOv8-DSW enhances the mean Average Precision at 50% (mAP50) by 2.5% in comparison to the YOLOv8n, significantly improving detection accuracy and provide valuable method for the development of road defect detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Deep learning

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Competition for gradient-free tuning of large language models:approaches, results, current challenges and future directions

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National Science Review 2023年第6期10卷 16-19页

作者： Tingfeng Cao Liang Chen Dixiang Zhang Tianxiang Sun Zhengfu He Xipeng Qiu Xing Xu Hai Zhang School of Software Engineering South China University of Technology School of Computer Science Fudan University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

PROBLEM Recent years have witnessed the rapid progress of self-supervised language models (LMs)[1],especially large language models (LLMs)[2].LLMs not only achieved state-of-the-art performance on many natural language processing tasks,but also captured widespread attention from the public due to their great potential in a variety of real-world applications (***,search engines,writing assistants,etc.)through providing general-purpose intelligent services.A few of the LLMs are becoming foundation models,an analogy to infrastructure,that empower hundreds of downstream applications.

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Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic Science and technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China College of Computer Science&Software Engineering Shenzhen UniversityShenzhen518060China School of Computer Science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network Multi-head attention mechanism Spatio-temporal dependency Traffic flow prediction

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HybridGAD: Identification of AI-Generated Radiology Abstracts Based on a Novel Hybrid Model with Attention Mechanism

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computers, Materials & Continua 2024年第8期80卷 3351-3377页

作者： TugbaÇelikten Aytug Onan Department of Software Engineering Faculty of TechnologyManisa Celal Bayar UniversityManisa45140Turkey Department of Computer Engineering Faculty of Engineering and ArchitectureIzmir KatipÇelebi UniversityIzmir35620Turkey

Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the *** and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed *** recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis ***:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical ***:Radiological imaging is an extremely important tool in modern medicine to assist in medical *** work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.

关键词： Generative artificial intelligence AI-generated text detection attention mechanism hybrid model for text classification

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Adaptive Data Forwarding Scheme for Diverse Infotainment Services in Vehicular Named Data Networking

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IEEE Transactions on Vehicular technology 2024年第5期74卷 8012-8022页

作者： Khan, Sajjad Ahmad Lim, Huhnkuk Chaudhry, Shehzad Ashraf Kocaeli University Software Engineering Department Izmit41000 Turkey Hoseo University Computer Engineering Department Korea Republic of Abu Dhabi University Department of Computer Science and Information Technology Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering & Architecture Nisantasi University Istanbul Turkey

Providing diverse infotainment services is crucial for enhancing road safety and improving the overall driving experience in vehicular networks. However, delivering these services within Vehicular Named Data Networking (VNDN) environments presents significant challenges, primarily due to the data broadcast phenomenon and the varying Quality of Service (QoS) requirements of different applications. This paper introduces an adaptive data forwarding scheme designed to effectively manage these challenges and optimize the delivery of infotainment services in VNDN. Our proposed scheme enhances the data packet format by incorporating additional attributes that prioritize data based on its criticality, specifically categorizing it into critical, non-critical, and entertainment data. Furthermore, we introduce an Enhanced Weighted Fair Queuing (EWFQ) policy to optimize the scheduling of data packets according to their importance. Two innovative data forwarding schemes, namely Advanced Push-based Data Forwarding (APDF) and Enhanced Pull-based Data Forwarding (EPDF), are developed to address the needs of critical and non-critical information and entertainment data, respectively. Simulation results demonstrate that our adaptive data forwarding scheme significantly outperforms legacy fragment schemes in delivering infotainment data within dynamic VNDN environments, ensuring reliability and efficiency. This paper contributes to advancing data dissemination strategies in vehicular networks and lays the groundwork for future research in adaptive QoS management. © 1967-2012 IEEE.

关键词： Entertainment industry Quality of service Adaptive systems Delays Data dissemination Vehicle dynamics Scalability Road safety Real-time systems Vehicles

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