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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Li Hu, Jingyang Fu, Shihao Xing, Xin Zhou, Wei School of Big Data and Software Engineering Chongqing University Chongqing400030 China School of Computer Science and Mathematics Anyang University Henan Province 455000 China Chongqing Academy of Metrology and Quality Inspection China

ISBN: (纸本)9781665410205

Existing rumor detection methods have not ade-quately considered which features should be addressed at the macroscopic or micro levels. Furthermore, the fundamental manifestation of news dissemination-public opinion-has received insufficient attention. We propose a macro-micro based public opinion (MMPO) modeling method to address these gaps. This approach enables a better understanding of news dissemination on social media platforms. Firstly, we incorporate post sentiment as a crucial feature. Secondly, we examine how the propagation at the micro-level influences sub-posts. Next, we investigate the temporal variations in public opinion from a macro perspective. Lastly, we integrate the acquired general propagation information with textual data to obtain more distinct representations of news propagation. By leveraging the macro perspective, the proposed model effectively captures the fine-grained characteristics of news propagation and helps smooth out the noise in user comments. The experimental evaluation showcases the superiority of our model in rumor detection compared to existing methods. © 2024 IEEE.

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Low-dose CT reconstruction using dataset-free learning

arXiv

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arXiv 2024年

作者： Wang, Feng Wang, Renfang Qiu, Hong College of Big Data and Software Engineering Zhejiang Wanli University Zhejiang Ningbo China

Low-Dose computer tomography (LDCT) is an ideal alternative to reduce radiation risk in clinical applications. Although supervised-deep-learning-based reconstruction methods have demonstrated superior performance compared to conventional model-driven reconstruction algorithms, they require collecting massive pairs of low-dose and norm-dose CT images for neural network training, which limits their practical application in LDCT imaging. In this paper, we propose an unsupervised and training data-free learning reconstruction method for LDCT imaging that avoids the requirement for training data. The proposed method is a post-processing technique that aims to enhance the initial low-quality reconstruction results, and it reconstructs the high-quality images by neural work training that minimizes the 1-norm distance between the CT measurements and their corresponding simulated sinogram data, as well as the total variation (TV) value of the reconstructed image. Moreover, the proposed method does not require to set the weights for both the data fidelity term and the plenty term. Experimental results on the AAPM challenge data and LoDoPab-CT data demonstrate that the proposed method is able to effectively suppress the noise and preserve the tiny structures. Also, these results demonstrate the rapid convergence and low computational cost of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer Science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Guiding ChatGPT for Better Code Generation: An Empirical Study

Guiding ChatGPT for Better Code Generation: An Empirical Stu...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Chao Liu Xuanlin Bao Hongyu Zhang Neng Zhang Haibo Hu Xiaohong Zhang Meng Yan School of Big Data and Software Engineering Chongqing University China School of Software Engineering Sun Yat-sen University China

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Automated code generation is a powerful technique for software development, which can significantly reduce developers' effort and time for writing code. Recently, OpenAI's large language model ChatGPT has emerged as a powerful tool for generating human-like responses to a wide range of textual inputs (i.e., prompts), including those related to code generation. However, the effectiveness of ChatGPT in code generation is still not well understood. The code generation performance could also be heavily influenced by the choice of prompts, which should be further explored. In this paper, we report an empirical study on ChatGPT's capabilities for two types of code generation tasks, namely text-to-code and code-to-code generation. We investigate different types of prompts by leveraging the chain-of-thought strategy with multi-step optimizations. Our empirical results show that by carefully designing prompts to guide ChatGPT, the code generation performance can be improved substantially. We also analyze the factors that influence the prompt design and provide insights that could guide future research.

关键词： Codes Large language models Writing Chatbots software Task analysis Optimization

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ConvNeXt-Mask2Former: A Semantic Segmentation Model for Land Classification in Remote Sensing Images

ConvNeXt-Mask2Former: A Semantic Segmentation Model for Land...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Jing Zheng Big Data and Software Engineering Department Zhejiang Wanli University ZJWL Ningbo China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

With the rapid development of remote sensing technology and deep learning, significant progress has been made in land classification of remote sensing images using semantic segmentation models. However, it still faces some deficiencies and challenges in terms of multi-scale features of images, the computational burden of high resolution images, and long-range dependencies. Therefore, in this paper, I design a semantic segmentation network based on encoder and decoder (Encoder-Decoder) architecture, using ConvNeXt as the encoder, i.e., the backbone network, and Mask2Former as the decoder, with the addition of an auxiliary segmentation module (ASM), and at the same time, an image enhancement pipeline is used to assist the training in the image preprocessing stage. Mask2Former is an architecture with good flexibility and generalization ability, which can be easily used in different visual tasks. At the same time, as a convolutional network without the addition of an attention mechanism, ConvNeXt has excellent feature extraction capabilities. I evaluate my model on the widely used high resolution image datasets Potsdam and Vaihinge and compare the results with the current mainstream semantic segmentation models, and the experimental results show that my model can be optimized for the accurate segmentation of land categories. Specifically, my model has an optimal mPA of $\mathbf{9 3 . 7 9 \%}$ and mIoU of $86.80 \%$ on the Potsdam dataset, and an optimal mPA of $82.32 \%$ and mIoU of $\mathbf{9 0 . 0 5 \%}$ on the Vaihinge dataset, which is both significantly higher than the existing models, and provide important references for land classification tasks and land use.

关键词： Training Image resolution Semantic segmentation Computational modeling Land surface Computer architecture Decoding

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Next POI Recommendation Based on Spatial and Temporal Disentanglement Representation

Next POI Recommendation Based on Spatial and Temporal Disent...

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IEEE International Conference on Web Services (ICWS)

作者： Hongjin Tao Jun Zeng Ziwei Wang Min Gao Junhao Wen School of big data and software engineering Chongqing University China

The next Point-of-interest (POI) recommendation task is to predict the next POI that users may be interested in. POI check-in sequence implicitly reflects the user’s location transition patterns, and the sequence modeling relies on the engineering of multiple independent features. Even though traditional POI recommendation models can fulfill the predicting task via entangled features, those black box models fail to mine the intrinsic check-in intention. The disentanglement representation learning method enables models to disentangle targeting intentions and provide interpretability for recommended results. However, existing disentanglement representation studies focus on disentangling user preference but neglect the entangled location characteristic. Besides, unrecorded check-ins result in inconsecutive transition sequences, which may influence their disentanglement qualities. Therefore, we proposed CrossDR, a Cross-sequence Location Disentanglement Representation method for the next POI recommendation, to explore how spatial and temporal factors influence check-in behaviors by a global view of location transitions. We apply disentanglement learning along with the time-masked data augmentation method and frequency-domain learning technique to further alleviate the short trajectory cold start problem caused by consecutive sequence generation. Experiments on two real-world datasets show our model has competitive capability compared to strong baselines.

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MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interaction Sites Prediction Using Squeeze-and-Excitation Capsule Networks

MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interact...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Weipeng Jiang, Changkun Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781665468190

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task. © 2022 IEEE.

关键词： Forecasting

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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A framework for accurate and efficient classification of manufacturing resources in the industrial internet platform

A framework for accurate and efficient classification of man...

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2022 International Symposium on Intelligent Robotics and Systems, ISoIRS 2022

作者： Zhang, Zhe Wang, Xu Wu, Yingbo Chongqing University College of Mechanical and Vehicle Engineering Chongqing China Chongqing University School of Big Data & Software Engineering Chongqing China

ISBN: (纸本)9781665455213

Manufacturing Resource Classification (MRC) is a prerequisite for optimal manufacturing resource allocation in the Industrial Internet Platform (IIP). Some key challenges are the inaccurate description of digital manufacturing resources (MRs) and capabilities, the large scope of resource search, and the slow response time of the cloud platform. To address these issues, this paper proposes a framework for classifying resources based on Video Semantic Description (VSD) and Event Description (ED) and related implementations to support the optimal allocation of MRs in the IIP. Firstly, we propose a framework for accurately and efficiently classifying MRs, the structure of which mainly consists of real-time digital descriptions of MRs, construction of manufacturing event clouds, and similar classification of manufacturing events. In addition, we propose a real-time numerical description method for MRs to address the real-time transmission and accurate description of IIP platforms, an event cloud construction method to shorten the search scope of resource allocation, and an event cloud similarity classification method to reduce the associated computational redundancy, which can support the accurate and efficient classification of MRs. © 2022 IEEE.

关键词： Semantics

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Adaptive Embedding Using Contrastive Learning with Median Absolute Deviation and Variance for Recommendation

Adaptive Embedding Using Contrastive Learning with Median Ab...

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Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Qinxin Xu Haofeng Li Wei Zhou Zhiyong Yang School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing China Tongji University Shanghai China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Recommendation systems have increasingly relied on embedding techniques to capture user preferences and item features. Adaptive temperature techniques have rapidly developed to provide more accurate recommendations. However, these methods often struggle with popularity bias and a lack of adaptability in dynamic environments, which will end in a decline of the robustness and the bias in temperature prediction. To address these challenges, we propose integrating noise-based data augmentation and InfoNCE loss from the XSimGCL framework into the adaptively modulating embedding magnitude model. Our method enhances traditional adaptive temperature adjustment by incorporating statistical measures like median absolute deviation and variance of embedding norms, aiming to create more robust and adaptive embeddings. This approach eliminates the necessity for adapting temperature by loss, instead focusing on incorporating statistical measures and adding noise to enhance robustness. Extensive evaluations on several real-world datasets demonstrate that our approach effectively mitigates popularity bias and significantly improves overall recommendation quality. This suggests that the nuanced integration of contrastive learning with adaptive embedding techniques can lead to more personalized and equitable recommendations.

关键词： Temperature measurement Adaptation models Temperature distribution Accuracy Contrastive learning data augmentation data models Robustness Loss measurement Recommender systems

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