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检索条件"任意字段=Conference on Visual Information Processing and Communication II"

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Design and Implementation of Overseas communication Path for Chinese Corporate Image Based on Multimodal Discourse Analysis with Machine Learning Algorithm 24

Design and Implementation of Overseas Communication Path for...

引用

3rd International conference on Artificial Intelligence and Intelligent information processing, AiiIP 2024

作者： Lin, Jiling Lei, Fei Liu, Weiliang School of Foreign languages Guangdong University of Science & Technology Guangdong Dongguan China School of Finance and Economics Guangdong University of Science & Technology Guangdong Dongguan China Huizhou Modern Vocational Technology School Guangdong Huizhou China

ISBN: (纸本)9798400707308

In an increasingly globalized economy, the communication of corporate image plays a pivotal role in shaping perceptions and fostering relationships with international stakeholders. This study explores the design and implementation of effective overseas communication strategies for Chinese corporations, leveraging multimodal discourse analysis and machine learning algorithms. By synthesizing quantitative and qualitative data, we analyze various communication modalities—including visual, textual, and auditory elements—utilized in corporate branding across different cultural contexts. Employing machine learning techniques, we identify patterns and insights from vast datasets, enabling a nuanced understanding of how multimodal elements influence audience reception and engagement. Our findings reveal key factors that contribute to successful corporate image dissemination and highlight the importance of cultural sensitivity in communication strategies. The research provides a framework for Chinese corporations to enhance their global presence by integrating data-driven insights into their communication practices, ultimately fostering a more favorable corporate image and stronger international relationships. This study not only contributes to the field of corporate communication but also offers practical implications for practitioners seeking to navigate the complexities of multicultural environments in an era of rapid technological advancement. © 2024 Copyright held by the owner/author(s).

关键词： Machine Learning Algorithms

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Revolutionizing Water Management: Digital Twins and AI for Predictive Maintenance and Sustainability 5th

Revolutionizing Water Management: Digital Twins and AI for P...

引用

5th International conference on Protection of Historical Constructions, PROHITECH 2025

作者： Syed, Toqeer Ali Naqash, Muhammad Tayyab Siddiqui, Muhammad Shoaib Alqahtany, Saad Said Faizullah, Safiullah Formisano, Antonio Faculty of Computer and Information System Islamic University of Madinah Medina Saudi Arabia Civil Engineering Department Faculty of Engineering Islamic University of Madinah Medina Saudi Arabia University of Naples Federico II Piazzale Tecchio 80 Naples80125 Italy

ISBN: (纸本)9783031873119

The innovative investigation integrates digital and physical water networks to improve their management and predictive capabilities. The used approach applies advanced sensors for detecting leaks and smart meters that monitor water usage, send alerts, usage data and visual information to a digital platform. At the heart of this framework is a digital twin, a virtual replica that continuously reflects the real-time state of the physical network by processing incoming sensor data. A multimodal transformer model is employed to integrate and analyze diverse data streams, including numerical and visual inputs, enabling highly accurate leakage detection, future water usage forecasting, and contextual insights with a prediction accuracy of about 98%. This approach advances water management and provides valuable insights for preserving historical buildings that demonstrates optimize operational efficiency and ensure sustainable water use in smart water networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： information leakage

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GRU Based Multiview Summarisation to obtain fast effective Key-frame

GRU Based Multiview Summarisation to obtain fast effective K...

引用

2025 International conference on Computer, Electrical and communication Engineering, ICCECE 2025

作者： Kandaswamy, Vijay Anand Joshua Kumaresan, S. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331513894

This paper proposes a novel approach to video summarisation by introducing a feature variance model that enhances video frame extraction. In typical video record aggregation systems, a variety of image processing tasks, such as acquisition, preprocessing, segmentation, and feature extraction, should be carried out with high precision to guarantee seamless system performance. Among these, feature extraction is essential since it breaks down large amounts of visual data into more manageable, informative vectors, and the success of the system is directly impacted by how effectively this process functions. Widely Recurrent Neural Networks (RNNs) are used in supervised video summarisation processes to obtain a model for the temporal dependencies between video frames. RNN's ability to learn long-range memory in the video is limited, and CNN often focusses on local information while losing long-term knowledge when the input sequence is lengthy. Therefore, a Gated Recurrent Unit based Neural Network (GRU-NN) for video summarisation is proposed, to classify them into keyframes and non-keyframes. Keyframes are selected for summarisation, while non keyframes are discarded or reduced. We explore various keyframe extraction techniques, analyse the performance of the proposed summarisation model, and discuss the results. Finally, we offer insights into the model's potential improvements and its broader applications in video summarisation tasks. © 2025 IEEE.

关键词： Image segmentation

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Centinela: An Intelligent System Based on an Integrated Architecture for Supporting Scholars 12th

Centinela: An Intelligent System Based on an Integrated Arch...

引用

12th Ecuadorian conference on information and communication Technologies

作者： Recalde, Lorena Suntaxi, Gabriela Martinez-Mosquera, Diana Masabanda, Rommel Cabrera, Danny Escuela Politec Nacl Dept Informat & Comp Sci Ladron Guevara E11-253 Quito Ecuador

ISBN: (纸本)9783031754302;9783031754319

Research and discovery are fundamental to the advancement of visionary universities. In the modern era, the impact of scientific research on development underscores the critical need for enhanced collaboration among experts. In Ecuador, both public and private universities are spearheading high-quality research initiatives that engage hundreds of scholars, highlighting an increased momentum in scientific output. This surge necessitates an efficient computational tool to support these academic communities in networking, finding collaborators, and selecting joint research topics. To address these needs, we have developed an architecture underpinning a new application named "Centinela". This architecture is structured into three layers: i. two operational and one analytical databases that forms the data layer, which by combining multiple databases, optimizes the retrieval, processing, and presentation of research data;ii. Django for the back-end to handle business logic;and iii. Angular for the front-end that supports the user interaction. Centinela integrates data from the Scopus API, focusing on the contributions of Ecuadorian researchers, and builds networks of co-authorship, acts as a search engine for academic publications, allows researchers to form groups, provides recommendations on research topics and facilitates group decision-making processes. This integration showcases the application's robustness in streamlining the management of researchers' collective expertise and fostering in-depth knowledge across both foundational and advanced subjects.

关键词： System architecture Docker Neo4j information retrieval Group recommender system Co-authorship network C4 Model

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Research on fine recognition method of electric power equipment safety hazards based on visual processing

Research on fine recognition method of electric power equipm...

引用

2024 International conference on Computer Vision and Image processing, CVIP 2024

作者： Huo, Yongbo Pang, Bingyao Yang, Xizai Yang, Le Lin, Yiming State Grid Shaanxi Electric Power Co. Ltd. Information and Communication Company Xi’an710048 China School of Electrical Engineering Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9781510688384

With the further research of deep learning, power companies have gradually eliminated the prevention and control by manual inspection, and have adopted deep learning to identify the safety hazards of power equipment, and to detect the defects of power equipment more accurately and quickly. Firstly, this paper introduces the significant drawbacks of traditional power equipment identification methods in the field of power equipment identification, and then illuminates the advantages of deep learning in the field of power equipment, which leads to the YOLOv7 algorithm used in this study, and then provides a detailed introduction of the theory and concepts of the algorithm. Second, this paper constructs a dataset applicable to the identification of safety hazards in electrical equipment, and uses this dataset to train and test the YOLOv7 algorithm. Finally, by analyzing the experimental results, it is concluded that the algorithm has a high accuracy character and robustness in the method of identifying safety hazards of electrical equipment. This study provides a practical and feasible technical route and theoretical foundation for solving the problem of safety hidden hazard detection of electrical equipment using YOLOv7 algorithm. © 2025 SPIE.

关键词： Power transmission lines

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Fourth International conference on Computer Vision, Application, and Algorithm, CVAA 2024

Fourth International Conference on Computer Vision, Applicat...

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4th International conference on Computer Vision, Application, and Algorithm, CVAA 2024

ISBN: (纸本)9781510687615

The proceedings contain 122 papers. The topics discussed include: DFrFT-ES model for emotion recognition based on fractional Fourier transform of EEG signals;research on traffic sign recognition under complex meteorological conditions;diffusion-augmented learning for long-tail recognition;apple leaf scab recognition using CNN and transfer learning;container image management in cloud-edge environments: an image deletion method based on layer affinity;computer graphics and image processing techniques based on visual communication design;dynamic fusion and non-negative matrix factorization-based multi-view clustering method;convolutional recurrent neural network-based EEG signal classification in motor imagery;and sentiment classification of MOOC courses by merging local context focus and bi-directional gated recurrent unit.

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Corresponding an Audio-processing Transformer to EEG Brain Activity Induced by Naturalistic Audio-visual Video Stimuli

Corresponding an Audio-Processing Transformer to EEG Brain A...

引用

International conference on Artificial Intelligence in information and communication (ICAiiC)

作者： Hiroki Kurashige Jun Kaneko School of Information Telecommunication Engineering Research & Information Center (TRIC) Institute of Advanced Biosciences Tokai University Tokyo Japan School of Information Telecommunication Engineering Tokai University Tokyo Japan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

We attempted to relate EEG brain activities evoked by naturalistic audio-visual video stimuli to the outputs of an audio-processing Transformer induced by audio inputs extracted from the same stimuli. We found a good correspondence, especially in low-frequency brain activity. This is complementary to a previous study that showed good correspondence between high-frequency brain activity and a movie-processing Transformer. These suggest that combining audio- and movie-processing Transformers and using them as a brain simulator is promising. That is, by utilizing this, it should be possible to synthesize audio-visual video stimuli that can intervene in a variety of brain activities and functions.

关键词： Brain Artificial neural networks Transformers Electroencephalography Artificial intelligence

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I Hear, See, Speak & Do: Bringing Multimodal information processing to Intelligent Virtual Agents for Natural Human-AI communication

I Hear, See, Speak & Do: Bringing Multimodal Information Pro...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE conference on

作者： Ke Li Fariba Mostajeran Sebastian Rings Lucie Kruse Susanne Schmidt Michael Arz Erik Wolf Frank Steinicke Human-Computer Interaction University of Hamburg HIT Lab NZ University of Canterbury

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

In this demo paper, we present an Extended Reality (XR) framework providing a streamlined workflow for creating and interacting with intelligent virtual agents (IVAs) with multimodal information processing capabilities using commercially available artificial intelligence (AI) tools and cloud services such as large language and vision models. The system supports (i) the integration of high-quality, customizable virtual 3D human models for visual representations of IVAs and (ii) multimodal communication with generative AI-driven IVAs in immersive XR, featuring realistic human behavior simulations. Our demo showcases the enormous potential and vast design space of embodied IVAs for various XR applications.

关键词： Solid modeling visualization Three-dimensional displays Extended reality Metaverse information processing User interfaces Systems support Haptic interfaces Artificial intelligence

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An Optimized Tennis Ball Picking Method Combining YOLOv5 and K-Means++ 24

An Optimized Tennis Ball Picking Method Combining YOLOv5 and...

引用

8th International conference on Electronic information Technology and Computer Engineering, EITCE 2024

作者： Li, Peixiang Zhou, Haiyan Yang, Xingyu School of Information and Communication Guilin University of Electronic Technology Guangxi Guilin China

ISBN: (纸本)9798400710094

With the rapid development of artificial intelligence and robotics, intelligent picking robots are being increasingly applied across various fields. However, how to efficiently identify and pick up the target object in a complex dynamic environment is still a challenging problem. Aiming at this problem, an optimized visual recognition and path planning method is developed by fusing YOLOv5 target detection algorithm and K-Means++ clustering algorithm to improve the performance of tennis picking robot. YOLOv5 is employed within the ROS framework to detect and locate tennis balls, while K-Means++ is used to cluster and optimize their positions. Field experiments demonstrate that the system achieves mAP@0.5, mAP@0.5:0.95, accuracy, and recall rates of 98.26%, 86.14%, 99.14%, and 96.59%, respectively, in tennis ball recognition. The improved K-Means++ clustering algorithm shows strong performance in optimizing path planning. Compared to the unimproved K-Means algorithm, the average picking time is reduced by 12.14%. When compared to the Ant Colony Optimization, the reduction is 4.281%, and compared to the Genetic Algorithm, the picking time is decreased by 8.076%. Additionally, when compared to manual picking, the time is reduced by 19.589%. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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A Novel visual communication Scenario Based on Image processing Algorithms

A Novel Visual Communication Scenario Based on Image Process...

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Machine Learning and Autonomous Systems (ICMLAS), International conference on

作者： Jinhua Yang Li Hou Xijing University Shaanxi Xian China

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on image processing algorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REVISED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio.

关键词： Training Adaptation models Image coding Machine learning algorithms visual communication Image synthesis Heuristic algorithms Semantics Generative adversarial networks Neural style transfer

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