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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Ma, Jiangtao Jin, Wenduo Chen, Yonggang Zhang, Fan Qian, Shenyi Qiao, Yaqiong College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China The State Information Center China China National Digital Switching System Engineering and Technology R&D Center Zhengzhou China College of Cyber Science Nankai University Tianjin300350 China

ISBN: (纸本)9798331531904

Most existing multimodal named entity recognition (MNER) methods cannot align image and text well, and fail to effectively fuse image-text semantic information, leading to suboptimal MNER accuracy. To this end, this paper proposes a novel named entity recognition model based on Contrastive Learning and Multimodal Semantic Interaction (named CLMSI). Specifically, to narrow the semantic gap between image-text modalities, making semantically similar image-text representations closer and pushing dissimilar ones further apart, we propose a contrastive learning-based cross-modal alignment method. Moreover, to integrate multimodal information effectively and filter out the noise introduced by image features as thoroughly as possible, we propose to incorporate image descriptions as additional features to enrich the multimodal representation and to reduce the semantic bias of the image information. Extensive experiments conducted on two MNER benchmark datasets, Twitter-2015 and Twitter-2017, demonstrate the superiority of the proposed method. Further ablation studies and case analyses also affirm the efficacy of CLMSI. © 2024 IEEE.

关键词： Contrastive Learning

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Mixed Color Channels (MCC): A Universal Module for Mixed Sample Data Augmentation Methods

Mixed Color Channels (MCC): A Universal Module for Mixed Sam...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wei, Yang Ma, Jianfeng Jiang, Zhongyuan Xiao, Bin School of Cyber Engineering Xidian University China Shaanxi Key Laboratory of Network and System Security China School of Computer Science and Technology Chongqing University of Posts and Telecommunication China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Color invariance is critical for computer vision systems since it significantly increases the robustness and effectiveness of the system. MSDA approaches (e.g., FMix and CutMix) have attracted considerable attention in recent years since they are simple, effective, and do not require extra computation con-sumption. By mixing samples, these approaches extend the distribution of training samples. However, the color information of these mixed samples is not changed, which makes it still difficult for trained models to achieve color invariance. To address this issue, we propose a universal module called Mixed Color Channels (MCC) that implements color changes by mixing the sample and its color variants, which enables trained models to achieve color invariance. In the experimen-tal section, we insert MCC into four state-of-the-art MSDA approaches, evaluate its effectiveness, and embed MCC into a non-MSDA method to demonstrate its extensibility. © 2022 IEEE.

关键词： Color

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Accuracy Improvement of CNN MobileNet-V1 and Residual Network 50 Layers Models using Adam setting for Car Type Classification

Accuracy Improvement of CNN MobileNet-V1 and Residual Networ...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Taqiyuddin, Muhammad Windarto, Yudi Eko Syafei, Wahyul Amien Windasari, Ike Pertiwi Prasetijo, Agung Budi Diponegoro University Graduate School of Information System Semarang Indonesia Diponegoro University Department of Computer Engineering Semarang Indonesia Diponegoro University Department of Electrical Engineering Semarang Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

The industry 4.0 era is coming. This causes companies and agencies are competing in building a system that integrates cyber technology and automation technology. One example is the system for recognizing, detecting, and sorting cars according to their type as the number of cars dramatically increases. For the case when calculating certain types of cars passing the road, instant and accurate sorting of car types is required. This certainly influences the development process of related agencies. This paper presents an accuracy improvement of convolutional neural network in MobileNet-V1 and ResNet-50 models using Adam setting for car type classification. Maximum results are obtained by varying different parameters, the use of the optimizer Adam and RMSprop, employing ImageNet as transfer learning, as well as implementing dropout layers and 2 kinds of learning rate. This system is trained using 10,708 pictures of City Car, Pick Up, Sedan, Sport ultra-vehicle, and Van. Accuracy of car type classification reaches 92,1% for MobileNet-V1 and 88,2% for ResNet-50 models. © 2022 IEEE.

关键词： Convolutional neural networks

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Virtual Reality Technology in Peruvian Higher Education: Influence of University Tenure 13

Virtual Reality Technology in Peruvian Higher Education: Inf...

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13th International Conference on Virtual Campus, JICV 2023

作者： Vergara-Rodriguez, Diego Solis, Ronald Melgarejo Anton-Sancho, Alvaro Depaz, Carlos Edmundo Navarro Fernandez-Arias, Pablo Samala, Agariadne Dwinggo Ávila Spain Web Digital School University Department of Virtual Education Lima Peru San Marcos University Dept. System Engineering and Computer Science Lima Peru Universitas Negeri Padang Facuity of Engineering West Sumatera Padang Indonesia

ISBN: (纸本)9798350343441

The use of virtual reality (VR) technologies in higher education has been growing in recent years due to their didactic potential in the most diverse areas of knowledge. However, the implementation of VR in universities is affected by several factors, including the level of digitization of the country or the tenure, private or public, of the universities. In this paper, a quantitative, descriptive and correlational research is carried out on the assessments of a group of professors in Peru about the use of VR in their lectures and on the differences in these perceptions according to the tenure of the university of the participants. For this purpose, a validated questionnaire was used, and its responses were statistically analyzed. The results reveal that professors express high ratings for the use of VR but show insufficient levels of digital skills for its use. In addition, professors from private universities give higher ratings of the didactic and usability aspects of VR. It is suggested that VR equipment and faculty training should be strengthened. © 2023 IEEE.

关键词： Virtual reality

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An efficient memory data organization strategy for application-characteristic graph processing

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Frontiers of computer Science 2022年第1期16卷 191-193页

作者： Peng FANG Fang WANG Zhan SHI Dan FENG Qianxu YI Xianghao XU Yongxuan ZHANG Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage SystemEngineering Research Center of data storage systems and TechnologyMinistry of Education of ChinaSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

1 Introduction Graph processing has received significant attention for its ability to cope with large-scale and complex unstructured data in the ***,most of the graph processing applications exhibit an irregular memor... 详细信息

关键词： application processing pattern

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Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Deng, Songhe Zhuo, Wei Xie, Jinheng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model’s ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://***/CVI-SZU/QA-CLIMS Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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Realizing Global Citizen Solutions for Sustainable Development Goals: A Collaborative engineering Course Between Thai and Japanese Students 23

Realizing Global Citizen Solutions for Sustainable Developme...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Sae-Jia, Thanachot Leelawat, Natt Tang, Jing Murakami, Rie Taoka, Yuki Ota, Eri Punyabukkana, Proadpran Chulalonkorn University Faculty of Engineering Department of Environmental Engineering Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Graduate School of System Design and Management Keio University Yokohama Japan International School of Engineering Chulakongkorn University Faculty of Engineering Bangkok Thailand Global Education Section Center for Entrepreneurship Education Tokyo Institute of Technology Tokyo Japan School of Environment and Society Tokyo Institute of Technology Tokyo Japan Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350353952

One of the problems faced by engineering and science students is focusing solely on technical knowledge while needing more cultural context. In today's interconnected world, addressing global challenges necessitates collaborative efforts from individuals across nations. Accordingly, a joint course between Chulalongkorn University in Thailand and the Tokyo Institute of Technology in Japan has been established. The recent theme of the course, 'Gamification towards Sustainable Development Goals (SDGs),' aimed to foster global citizenship and address SDGs through multicultural collaboration. This study examines the similarities and differences in problem identification between Thai and Japanese student groups participating in the course. Data was collected from discussions, presentations, and group assignments throughout the course. The findings indicate that while both groups share common concerns, such as waste management, language barriers, and transportation issues, their perspectives and proposed solutions are influenced by cultural characteristics. Thai students emphasized infrastructural inadequacies and local traditions, whereas Japanese students focused on efficiency and the impacts of tourism. The study highlights the importance of understanding cultural nuances in collaborative international projects to achieve effective and innovative solutions to global challenges. © 2024 IEEE.

关键词： Sustainable development goals

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An Improved YOLO-v3Algorithm for Ship Detection in SAR Image Based on K-means++ with Focal Loss 3

An Improved YOLO-v3Algorithm for Ship Detection in SAR Image...

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3rd China International SAR Symposium, CISS 2022

作者： Wang, Haonan Wu, Baolong Wu, Yanni Zhang, Shuangxi Mei, Shaohui Liu, Yanyang Northwestern Polytechnical University School of Electronics and Information Xi'an China Shanxi Normal University School of Computer Science Xi'an China Shanghai Institute of Satellite Engineering Department of Radar System Engineering Shanghai China

ISBN: (数字)9798350398717

ISBN: (纸本)9798350398717

In recent years, practical industrial production application have put forward extremely high requirements for its detection accuracy and detection efficiency in the aspect of synthetic aperture radar (SAR) image ship detection. Among the solutions to this problem, the YOLO has received more and more attention due to its advantages such as high speed. In this paper, the K-means++ is used to obtain the Anchor Box, the Focal loss is introduced to balance the proportion of positive and negative samples, and an improved image detection algorithm based on YOLO-v3 is proposed to solve the low detection efficiency and detection accuracy of ship images. The experimental results show that the improved algorithm in this paper can get rid of the local optimum well, shorten the convergence time, improve the training efficiency and the accuracy of ship image detection. © 2022 IEEE.

关键词： Ships

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Packet Format Detection and Modulation Classification of Wireless LAN Using Distributed Convolutional Neural Network

Packet Format Detection and Modulation Classification of Wir...

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2021 International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2021

作者： Putra, Dody Ichwana Pratama, Muhammad Harry Bintang Lanante, Leonardo Grad, Hiroshi Ochi Kyushu Institute of Technology School of Computer Science and System Engineering Iizuka Japan

ISBN: (纸本)9781665419512

This paper proposes a method to detect the packet format and classify signal modulation type of wireless LAN signals using the distributed model of Convolutional Neural Network (CNN). The main advantages of this method compared to the conventional are the high accuracy of the model and the flexibility for lowering the complexity. These are achieved because the CNN models are trained to perform multiple small classification tasks instead of a single big classification task. This method makes retraining much easier. The five-fold cross-validation is applied to assess the performance for training and testing the model. To validate the model, actual Wi-Fi signals are used to test the model using Software Define Radio (SDR). The result shows the proposed method can accurately classify different packet formats and signal modulation types above 90%, even when different timing offsets occur. © 2021 IEEE.

关键词： Orthogonal frequency division multiplexing

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