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检索条件"任意字段=12th International Conference on Information Systems and Computing Technology"

共 47372 条记录，以下是91-100 订阅

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Multi View Learning-Based Graph Neural Network for Rumor Detection 12

Multi View Learning-Based Graph Neural Network for Rumor Det...

引用

12th international conference on information systems and computing technology

作者： Li, Linen Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China

ISBN: (数字)9798350379860

ISBN: (纸本)9798350379860;9798350379877

While existing graph neural network methods have integrated users' social behaviors and text content to some extent for rumor detection, they primarily suffer from two limitations. First, the propagation patterns of rumors are often described by static graphs, which presuppose that the structure of the rumor propagation network is fixed before the algorithm learns, overlooking the fact that rumor dissemination is a dynamic process. Second, current methods tend to neglect the in-depth exploration of complex interactions and dynamic changes between different information dimensions. Some approaches merely integrate personal user information as model inputs, others rely solely on time series analysis to track the changes in rumors, or fail to effectively differentiate the impact of various user features on rumor spread. response to these issues, this study introduces a new model framework named Multi-View Graph Neural Network (MV-GNN), which utilizes different perspectives to represent rumors, capturing more comprehensive information. We first abstract the process involved in rumor propagation into three perspectives: the user profile perspective, the user comment perspective, and the rumor diffusion perspective. then, we integrate these different views to predict the credibility of given information. On two real-world datasets, MV-GNN achieved the best detection performance, demonstrating the efficacy of the model.

关键词： rumor detection graph neural network multi view learning social media dynamic graph

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Remote Sensing Ship Detection Network with Auxiliary Global Receptive Field Convolution 12

Remote Sensing Ship Detection Network with Auxiliary Global ...

引用

12th international conference on information systems and computing technology

作者： Chen, Kejie Liu, Jin Liu, Yuxin Wei, Lai Shanghai Maritime Univ Coll Informat Engn Shanghai Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Currently, remote sensing images face issues such as low image resolution, image noise, small target sizes, and similar shapes between targets and non-targets. Detecting small targets in such images can lead to missed detections and recognition errors. Furthermore, in offshore navigation under nearshore conditions, these problems are further amplified. Traditional convolutional networks and newer Transformer-based image target detection techniques each have their own challenges. Traditional convolutional networks suffer from the limitation of lack of global receptive field as the convolution depth increases, which can lead to incorrect identification of land noise and port equipment as positive samples in near-shore conditions. the original Transformer structure has global context thanks to the self-attention mechanism, but the low image resolution and inherent convergence difficulties of this architecture make training challenging and the results are not ideal. this paper proposes a new convolutional structure, called Convolutional Addition Transformer Encoder (CATE), which combines global encoding information with convolution operations to improve recognition accuracy and stability. In the loss function part, simple classification and weight assignment are performed on the samples in the dataset, which further improves the training efficiency of the network.

关键词： AIS Remote sensing images Low image resolution deep learning Global receptive field Attention mechanisms

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A Deep Reinforcement Learning Model with Multi-Dimensional Resources Management for Routing Optimization 12

A Deep Reinforcement Learning Model with Multi-Dimensional R...

引用

12th international conference on information systems and computing technology

作者： Chen, Ailin Song, Jian Liu, Jiali Gu, Yebo Shen, Tao Wang, Qingwang Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming Yunnan Peoples R China Beijing Inst Mech Equipment Beijing Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Routing optimization enhances network performance and efficiency by optimizing path selection and resource allocation, making it a crucial technology for meeting diverse communication demands. Optimizing a single metric, specifically bandwidth, can enhance performance. However, other critical parameters are often overlooked. this oversight results in an incomplete capture of the overall network state. Such an approach poses limitations, especially in complex network environments. To address this issue, we propose a multidimensional network resource joint optimization method that considers four key resources: bandwidth, end-to-end delay, power consumption, and communication distance. By constructing a Resource Tensor, this method balances multiple performance metrics during the optimization process, thereby improving overall system performance. Simulation results demonstrate that this approach outperforms existing methods in reducing end-to-end delay, lowering bit error rates, and enhancing packet transmission efficiency.

关键词： Routing Optimization Deep Reinforcement Learning Graph Neural Network Resource Matrix

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Sentiment Analysis of Beauty Product Reviews Using the IndoBERT Method and Naive Bayes Classification 12

Sentiment Analysis of Beauty Product Reviews Using the IndoB...

引用

12th international conference on information and Communication technology, ICoICT 2024

作者： Ramdhan, Haidaruddin Muhammad Dwifebri Purbolaksono, Mahendra Bunyamin, Bunyamin School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350363432

this paper presents an integrated approach to sentiment analysis of beauty product reviews using the IndoBERT model combined with Naive Bayes classification, which specifically addresses the challenge of accurately analyzing mixed-language reviews. this research outlines the problem of mixed language sentiment analysis and introduces a methodology that includes data preprocessing, feature extraction with IndoBERT, and sentiment classification using Naive Bayes. the results, presented later in this paper, demonstrate that the Gaussian Naive Bayes classifier, in combination with IndoBERT, achieves a Mean Macro F1 Score of 0.569 (56.9%) in the Price category. this integrated approach significantly improves computational efficiency and accuracy, offering valuable insights for natural language processing (NLP) and digital marketing applications. © 2024 IEEE.

关键词： Natural language processing systems

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An Analysis of Factors Triggering Abnormal Cognitive States in Cooperation between Manned and Unmanned Aerial systems 12

An Analysis of Factors Triggering Abnormal Cognitive States ...

引用

12th international conference on information systems and computing technology

作者： Wang, Huan Zhang, Xia Yan, Chuang China Aeronaut Radio Elect Res Inst State Key Lab Av Integrat & Aviat Syst Of Syst Sy Shanghai Peoples R China

ISBN: (纸本)9798350379860;9798350379877

For operators, manned-unmanned teaming raises the possibility of abnormal cognitive states which put burden on task performance. this paper presents an analysis of factors triggering abnormal cognitive states (including cognitive overload, stress, overconcentration and vigilance decrement) in cooperation between manned and unmanned aerial systems through group decision making. Semi-structured interviews were carried out with 20 experts in machine agents designing and air combat training fields. A collection of 56 influencing factors for abnormal cognitive states was constructed from the aspects of human, machine and environment/task. Deep screening was caried out subsequently by two-round scales based on these preliminarily screened factors. Experts made their judgments on each factor from the aspects of impact levels and confidence. Statistical results including concentration, confidence, dispersion, and coordination were analyzed until a consensus was reached among all the experts. Finally, 28 influencing factors were remained with 9 factors for cognitive overload, 9 factors for stress, 6 factors for overconcentration and 4 factors for vigilance decrement. the findings can work as inputs to cognitive experiments and provide design guidelines for human-computer interaction in manned-unmanned teaming systems.

关键词： manned-unmanned teaming cognitive overload stress overconcentration vigilance decrement influencing factor

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Toward Design Perspectives of AI Chatbots 12

Toward Design Perspectives of AI Chatbots

引用

12th international conference on Advanced Cloud and Big Data, CBD 2024

作者： Hezavehi, Sasha Mahdavi Yong, Jianming Shen, Jun Xu, Dongming Yecies, Brian School of Business University of Southern Queensland SpringfieldQLD Australia School of Computing and Information Technology University of Wollongong Australia School of Business University of Queensland BrisbaneQLD Australia School of The Arts English and Media University of Wollongong Australia

ISBN: (纸本)9798331511074

this paper represents a perspective on the design of AI chatbots with respect to the concept of method engineering. Overall, this paper includes the following sections: First, we represent primitive findings based on a set of method fragments extracted from selected papers in the domain of AI chatbot design. then, we discuss our future work and the limitations of our research in information systems. © 2024 IEEE.

关键词： Artificial Intelligence chatbot design method engineering method fragments

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ReAuKGC: Relation-Augmented for Knowledge Graph Completion with Pre-trained Language Models 12

ReAuKGC: Relation-Augmented for Knowledge Graph Completion w...

引用

12th international conference on information systems and computing technology

作者： Chen, Hao Wang, Youcai Sun, Yining Chen, Yanyan USTC Scinece Isl Branch Grad Sch Hefei 230026 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Knowledge graph completion (KGC) infer absent relationships by utilizing the existing facts. the two most prevalent methods for KGC are: structure-based and description-based methods. Structure-based techniques efficiently capture relational information by embedding both entities and relationships. Description-based methods utilize textual information by employing pre-trained language models (PLMs), enhancing the semantic representation of entities. Nevertheless, the previous description-based methods often overlook the essential relational data within triples, concentrating solely on the semantic details of entity descriptions. In order to overcome this issue, we present a novel approach called Relation-Augmented Knowledge Graph Completion using Pre-trained Language Models (ReAuKGC), which utilizes special relation tokens to enrich the relation information without any additional compu-tational cost. To address entity ambiguity, we introduce a re-ranking method based on structure. the experimental findings indicate that ReAuKGC attains sota performance on the WN18RR benchmark. We perform comprehensive ablation analyses to emphasize the contribution of each specific component.

关键词： component knowledge graph completion language models description-based methods

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Electromagnetic Signal Augmentation of Pantograph Arcs Using an Improved DCGAN 12

Electromagnetic Signal Augmentation of Pantograph Arcs Using...

引用

12th international conference on information systems and computing technology

作者： Zhang, Chunhui Liang, Yanyue Gao, Zhiwei Zhao, Li Shijiazhuang Inst Technol Sch Artificial Intelligence Shijiazhuang Hebei Peoples R China Shijiazhuang Inst Technol Sch Internet Applicat Technol Shijiazhuang Hebei Peoples R China Shijiazhuang Tiedao Univ Hebei Prov Key Lab Electromagnet Environm Effecs Shijiazhuang Hebei Peoples R China Shijiazhuang Inst Technol Shijiazhuang Hebei Peoples R China Shijiazhuang Tiedao Univ Sch Traff & Transportat Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9798350379860;9798350379877

In response to the insufficient sample issue in the electromagnetic signal data of high-speed railway pantograph arcs, this study proposes an improved Deep Convolutional Generative Adversarial Network (DCGAN) to augment image datasets. By optimizing the network structure, the proposed method significantly improves the quality of generated images and introduces a lightweight design that reduces the computational demands of the model, making it more suitable for resource-constrained environments. After 5,000 iterations, the proposed method achieves a Frechet Inception Distance (FID) score of 36.33, compared to 45.54 for the original DCGAN and 50.21 for the WGAN. Moreover, the Inception Score (IS) of the proposed method is 1.91 +/- 0.11, outperforming the original DCGAN (1.81 +/- 0.23) and WGAN (1.77 +/- 0.04), further demonstrating its effectiveness. this method provides a new technical reference for research on electromagnetic signals in high-speed railways.

关键词： Electromagnetic signal DCGAN image augmentation lightweight network

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GMD-Linear: Integrating Graph Neural Networks and WaveNet for Enhanced Wind Power Forecasting 12

GMD-Linear: Integrating Graph Neural Networks and WaveNet fo...

引用

12th international conference on information systems and computing technology

作者： Zhang, Yuchen Zhang, Zixuan Hu, Yawen Wan, Yuanhao Nanchang Inst Sci & Technol Nanchang Jiangxi Peoples R China Nanchang Normal Coll Appl Technol Nanchang Jiangxi Peoples R China

ISBN: (纸本)9798350379860;9798350379877

Wind energy, a promising alternative to fossil fuels, faces challenges due to its variability and dependence on weather conditions. Effective integration into the power grid necessitates accurate deep learning models for forecasting wind power generation. While the existing MD-Linear model has shown commendable predictive performance, it struggles with long-term dependencies and complex spatial relationships in wind power data. To overcome these limitations, we propose GMD-Linear, an innovative framework that integrates graph neural networks with WaveNet modules. this approach harnesses the strengths of graph convolutional networks and WaveNet technology, significantly enhancing predictive accuracy and generalization. Empirical results demonstrate that our model excels at capturing long-term dependencies in wind power data using the advanced Spatial Dynamic Wind Power Forecasting (SDWPF) dataset from Longyuan Power Group Corporation Limited, markedly improving prediction reliability. this method not only addresses the volatility of wind energy but also supports the seamless integration of renewable resources into existing electrical grid infrastructures.

关键词： GMD-Linear Graph Neural Networks WaveNet Module Wind Power Generation

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Modulation Recognition Based on Bayesian Neural Networks 12

Modulation Recognition Based on Bayesian Neural Networks

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12th international conference on information systems and computing technology

作者： Meng, Zhuxuan Du, Jing Liu, Weisong Hao, Ruiqi Huang, Qiwang Deng, Jinzhou Acad Mil Sci AMS Consulting Ctr Strateg Assessment Beijing Peoples R China Acad Mil Sci AMS Beijing Peoples R China

ISBN: (纸本)9798350379860;9798350379877

In the domain of wireless communication, modulation recognition technology is crucial for improving spectrum utilization and enhancing the performance of communication systems. However, with the rapid evolution of communication technologies, traditional methods of modulation recognition, which rely on statistical analysis and machine learning, are facing significant challenges. these are particularly pronounced in complex signal environments and during adversarial interference, underscoring the need for robust recognition techniques. To address these issues, a modulation recognition technology based on Bayesian Neural Networks (BNN) is proposed in this paper. BNN incorporates probability distributions over network weights, offering not only estimates of prediction uncertainty but also significantly enhancing the model's generalization and robustness. the fundamental principles and architectural design of BNN are presented initially, followed by a detailed discussion of the BNN training process using variational Bayesian methods. the open-source RML2016_04C dataset is applied in the experimental section, encompassing a variety of modulation types, to evaluate model performance across different signal-to-noise ratio (SNR) conditions. the results demonstrate that BNNs achieve superior accuracy and robustness in modulation recognition tasks, especially under low SNR conditions, outperforming traditional neural networks. Moreover, the advantages of BNNs in handling adversarial attacks are also discussed in this paper.

关键词： Bayesian neural networks modulation recognition uncertainty quantification robustness

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