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Your search keyword '"Mao, Keming"' showing total 38 results
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38 results on '"Mao, Keming"'

1. NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism

Author

Li, Miao, Chen, Ming-Bin, Tang, Bo, Hou, Shengbin, Wang, Pengyu, Deng, Haiying, Li, Zhiyu, Xiong, Feiyu, Mao, Keming, Cheng, Peng, and Luo, Yi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our constructed benchmark dataset is focused on four facets of writing proficiency and six facets of safety adherence, and it comprises manually and carefully designed 1,267 test samples in the types of multiple choice questions and short answer questions for five editorial tasks in 24 news domains. To measure performances, we propose different GPT-4 based automatic evaluation protocols to assess LLM generations for short answer questions in terms of writing proficiency and safety adherence, and both are validated by the high correlations with human evaluations. Based on the systematic evaluation framework, we conduct a comprehensive analysis of ten popular LLMs which can handle Chinese. The experimental results highlight GPT-4 and ERNIE Bot as top performers, yet reveal a relative deficiency in journalistic safety adherence in creative writing tasks. Our findings also underscore the need for enhanced ethical guidance in machine-generated journalistic content, marking a step forward in aligning LLMs with journalistic standards and safety considerations., Comment: Long paper, ACL 2024 Main

Published
2024

3. LCAUnet: A skin lesion segmentation network with enhanced edge and body fusion

Author

Ma, Qisen, Mao, Keming, Wang, Gao, Xu, Lisheng, and Zhao, Yuhai

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Accurate segmentation of skin lesions in dermatoscopic images is crucial for the early diagnosis of skin cancer and improving the survival rate of patients. However, it is still a challenging task due to the irregularity of lesion areas, the fuzziness of boundaries, and other complex interference factors. In this paper, a novel LCAUnet is proposed to improve the ability of complementary representation with fusion of edge and body features, which are often paid little attentions in traditional methods. First, two separate branches are set for edge and body segmentation with CNNs and Transformer based architecture respectively. Then, LCAF module is utilized to fuse feature maps of edge and body of the same level by local cross-attention operation in encoder stage. Furthermore, PGMF module is embedded for feature integration with prior guided multi-scale adaption. Comprehensive experiments on public available dataset ISIC 2017, ISIC 2018, and PH2 demonstrate that LCAUnet outperforms most state-of-the-art methods. The ablation studies also verify the effectiveness of the proposed fusion techniques., Comment: 14 pages, 10 figures

Published
2023

4. SkinDiff: A Novel Data Synthesis Method Based on Latent Diffusion Model for Skin Lesion Segmentation

Author

Jing, Xin, Yang, Shushuo, Zhou, Heyang, Wang, Gao, Mao, Keming, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Chen, Wei, editor, and Pan, Yijie, editor

Published
2024
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8. A Novel Deep Learning Based Method for Doppler Spectral Curve Detection

Author

Mao, Keming, Ren, Yitao, Yin, Liancheng, Jin, Yan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Pimenidis, Elias, editor, Angelov, Plamen, editor, Jayne, Chrisina, editor, Papaleonidas, Antonios, editor, and Aydin, Mehmet, editor

Published
2022
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11. Monitoring Chlorophyll-a Concentration Variation in Fish Ponds from 2013 to 2022 in the Guangdong-Hong Kong-Macao Greater Bay Area, China.

Author

Li, Zikang, Yang, Xiankun, Zhou, Tao, Cai, Shirong, Zhang, Wenxin, Mao, Keming, Ou, Haidong, Ran, Lishan, Yang, Qianqian, and Wang, Yibo

Subjects
FISH ponds, MACHINE learning, SUSTAINABLE aquaculture, BODIES of water, WATER quality, SUMMER, WINTER, CHLOROPHYLL in water
Abstract

Aquaculture plays a vital role in global food production, with fish pond water quality directly impacting aquatic product quality. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a key producer of aquatic products in South China. Monitoring environmental changes in fish ponds serves as an indicator of their health. This study employed the extreme gradient boosting tree (BST) model of machine learning, utilizing Landsat imagery data, to assess Chlorophyll-a (Chl-a) concentration in GBA fish ponds from 2013 to 2022. The study also examined the corresponding spatiotemporal variations in Chl-a concentration. Key findings include: (1) clear seasonal fluctuations in Chl-a concentration, peaking in summer (56.7 μg·L−1) and reaching lows in winter (43.5 μg·L−1); (2) a slight overall increase in Chl-a concentration over the study period, notably in regions with rapid economic development, posing a heightened risk of eutrophication; (3) influence from both human activities and natural factors such as water cycle and climate, with water temperature notably impacting summer Chl-a levels; (4) elevated Chl-a levels in fish ponds compared to surrounding natural water bodies, primarily attributed to human activities, indicating an urgent need to revise breeding practices and address eutrophication. These findings offer a quantitative assessment of fish pond water quality and contribute to sustainable aquaculture management in the GBA. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Design and implementation of clothing fashion style recommendation system using deep learning

Author

Muhammad KHALID, Mao KEMING, and Tariq HUSSAIN

Subjects
T59.5, similarity measure, Automation, cloth recommendation, convolutional neural network, Information technology, General Medicine, T58.5-58.64
Abstract

In recent years, the huge amount of information and users of the internet service, it is hard to know quickly and accurately what the user wants. This phenomenon leads to an extremely low utilization of information, also known as the information overload problem. Traditionally, keywords are used to retrieve images, but such methods require a lot of annotations on the image data, which will lead to serious problems such as inconsistent, inaccurate, and incomplete descriptions, and a huge amount of work. To solve this problem, Content Based Information Retrieval (CBIR) has gradually become a research hotspot. CBIR retrieves picture objects based entirely on the content. The content of an image needs to be represented by features that represent its uniqueness. Basically, any picture object can be represented by its specific shapes, colors, and textures. These visual characteristics of the image are used as input conditions for the query system, and a result the system will recommended nearest images and data set. This research designs and implements two-stage deep learning-based model that recommends a clothing fashion style. This model can use deep learning approach to extract various attributes from images with clothes to learn the user’s clothing style and preferences. These attributes are provided to the correspondence model to retrieve the contiguous related images for recommendation. Based on data-driven, this thesis uses convolutional neural network as a visual extractor of image objects. This experimental model shows and achieves better results than the ones of the previous schemes.

Published
2021

26. Design and implementation of clothing fashion style recommendation system using deep learning.

Author

KHALID, Muhammad, Mao KEMING, and HUSSAIN, Tariq

Subjects
DEEP learning, RECOMMENDER systems, FASHION design, CONVOLUTIONAL neural networks, INFORMATION overload, INFORMATION retrieval
Abstract

In recent years, the huge amount of information and users of the internet service, it is hard to know quickly and accurately what the user wants. This phenomenon leads to an extremely low utilization of information, also known as the information overload problem. Traditionally, keywords are used to retrieve images, but such methods require a lot of annotations on the image data, which will lead to serious problems such as inconsistent, inaccurate, and incomplete descriptions, and a huge amount of work. To solve this problem, Content Based Information Retrieval (CBIR) has gradually become a research hotspot. CBIR retrieves picture objects based entirely on the content. The content of an image needs to be represented by features that represent its uniqueness. Basically, any picture object can be represented by its specific shapes, colors, and textures. These visual characteristics of the image are used as input conditions for the query system, and a result the system will recommended nearest images and data set. This research designs and implements two-stage deep learning-based model that recommends a clothing fashion style. This model can use deep learning approach to extract various attributes from images with clothes to learn the user's clothing style and preferences. These attributes are provided to the correspondence model to retrieve the contiguous related images for recommendation. Based on data-driven, this thesis uses convolutional neural network as a visual extractor of image objects. This experimental model shows and achieves better results than the ones of the previous schemes. [ABSTRACT FROM AUTHOR]

Published
2021
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27. Lung Nodule Image Classification Based on Ensemble Machine Learning

Author

Deng Zhuofu and Mao Keming

Subjects
Nodule (geology), Contextual image classification, business.industry, Computer science, 0206 medical engineering, Health Informatics, Pattern recognition, 02 engineering and technology, engineering.material, Machine learning, computer.software_genre, 020601 biomedical engineering, Ensemble learning, 0202 electrical engineering, electronic engineering, information engineering, engineering, 020201 artificial intelligence & image processing, Radiology, Nuclear Medicine and imaging, Artificial intelligence, business, computer
Published
2016

35. The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016)

Author

Wang, Shaoqing, primary, Yang, Xiancun, additional, Su, Meixia, additional, Liu, Qiang, additional, Gong, Tao, additional, Mao, Qi, additional, Zhao, Shuguang, additional, Han, Fang, additional, Mao, Keming, additional, Liu, Yixian, additional, Zhu, Yanchun, additional, Li, Shuo, additional, Yang, Jie, additional, Fu, Nan, additional, Yu, Shaode, additional, Li, Rongmao, additional, Xiong, Jing, additional, Xie, Yaoqin, additional, Wang, Shuihua, additional, Du, Sidan, additional, Chen, Zhimin, additional, Phillips, Preetha, additional, Chen, Shuwen, additional, Lu, Zeyuan, additional, Sun, Ping, additional, Dong, Zhengchao, additional, Zhang, Yudong, additional, Zhuang, Jingwen, additional, Zheng, Junzheng, additional, Bai, Mei, additional, Mao, Ning, additional, Mu, Xinnuan, additional, Xu, Cong, additional, Song, Yulu, additional, Song, Xiaolei, additional, Wang, Bin, additional, Xie, Haizhu, additional, Gan, Ke, additional, Luo, Daisheng, additional, Deng, Zhuofu, additional, Shi, Zhenghao, additional, Ma, Jiejue, additional, Zhao, Minghua, additional, Liu, Yonghong, additional, Wang, Yongchao, additional, Gao, Song, additional, Wang, Yaping, additional, Liu, Guixue, additional, Li, Wensheng, additional, Tu, Changyu, additional, Li, Lei, additional, Cai, Ailong, additional, Wang, Linyuan, additional, Bu, Haibing, additional, Yan, Bin, additional, Ho, Junghua, additional, Chang, Yin, additional, Manousakas, Ioannis, additional, Wei, Chiehhsuan, additional, Sun, Xiaolong, additional, Park, Juyoung, additional, Kim, Soyeun, additional, Kang, Kyungtae, additional, Zhang, Jingke, additional, Zhao, Feng, additional, Li, Guanyu, additional, Ren, Yijie, additional, Chen, Yupei, additional, Zhang, Xuming, additional, Yu, Zhen, additional, Ni, Dong, additional, Chen, Siping, additional, Li, Shengli, additional, Wang, Tianfu, additional, Lei, Baiying, additional, Li, Y. F., additional, Zhang, Lanhua, additional, Yan, Chengxin, additional, Yang, Huihui, additional, Sun, Baoliang, additional, Ding, Yanhui, additional, Zhang, Yongxin, additional, Zhan, Yafeng, additional, Wu, Yuxiang, additional, Huang, Zhenghua, additional, Zhang, Tianxu, additional, Fang, Hao, additional, Zhang, Yan, additional, Song, Zhijian, additional, Wang, Manning, additional, Li, Wan, additional, Yang, Chunlan, additional, Shi, Feng, additional, Wang, Qun, additional, Wu, Shuicai, additional, Lu, Wangsheng, additional, Li, Shaowu, additional, Farokhian, Farnaz, additional, Nie, Yingnan, additional, Zhang, Xin, additional, Li, Qingchun, additional, Yang, Dongyan, additional, Liang, Yun, additional, Sheng, Shihou, additional, Cheng, Xianbin, additional, Gai, Baodong, additional, Li, Binquan, additional, Hu, Xiaohui, additional, Beheshti, Iman, additional, Demirel, Hasan, additional, Ren, Jiechuan, additional, Lai, Fuwen, additional, Jin, Mingwu, additional, Liu, Yifei, additional, Ding, Mingyue, additional, Zhou, Yanhong, additional, Gong, Huihong, additional, Peng, Wei, additional, Liang, Wenyu, additional, Zhao, Lili, additional, Li, Kuan, additional, Yin, Jianping, additional, Wang, Mao, additional, Liu, Wei, additional, Gao, ZhiJun, additional, Tan, LiSha, additional, Duan, Shaoyin, additional, Lin, Simin, additional, Zhong, Hua, additional, Lv, Shaomao, additional, Lei, Haijun, additional, Zhang, Jian, additional, and Yang, Zhang, additional

Published
2016
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