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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tan, Kaiwen Tian, Jin Zhang, Yongbing Shu, Zhenqiu Yu, Zhengtao Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9798350386226

The interaction between SNPs plays a key role in revealing the genetic mechanisms of complex diseases. However, as the order of SNP interactions increases, the number of SNP combinations increases exponentially, presenting a serious combinatorial explosion problem. Although swarm intelligence algorithms can alleviate the combinatorial explosion problem by optimizing search paths and have been widely used in SNP interaction detection, the performance of swarm intelligence algorithms is greatly affected by initialization and search direction. To mitigate these issues, we propose a high-order SNP interaction detection algorithm based on hierarchical clustering and multitask harmony search (abbreviated as HCMHS). In harmony memory initialization, considering that similar SNPs are more likely to form pathogenic combinations, hierarchical clustering is first used to cluster SNPs into different clusters, and then based on these clusters, harmony memory is initialized. In harmony search, considering the correlation between different orders of SNP interactions, the search directions for SNPs of various orders are optimized through a multi-task framework. To verify the performance of HCMHS, we carried out experiments on 58 simulated datasets and one real dataset, and HCMHS achieved the best results compared to nine advanced algorithms. The code of HCMHS is available at https://***/huoluan17-tian/HCMHS. © 2024 IEEE.

关键词： Hierarchical clustering

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TwinFusion-YOLOv9 for RGB-T Object Detection 5

TwinFusion-YOLOv9 for RGB-T Object Detection

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Fu, Zhiqian Wu, Hua Shen, Shiquan Wang, Qingwang Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Zhaoyang District Government Service Management Bureau Zhaotong China Kunming University of Science and Technology Faculty of Transportation Engineering Kunming China

ISBN: (纸本)9798331533991

Cooperative target detection using infrared and visible images is currently a highly popular approach. This approach is not affected by environmental lighting conditions and can operate around the clock. However, issues such as inaccurate time and spatial synchronization of sensors, target movement, stereo disparity, or other factors leading to positional deviations result in the same target not overlapping completely in different images. This can impact the overall detection performance of the model. To tackle these challenges, this paper introduces a target detection network named TwinFusion-YOLOv9, which integrates features from visible images and infrared images to enhance detection robustness. To ensure the efficiency and accuracy of the solution, a structure suitable for joint target detection based on YOLOv9 was designed. Features from visible and infrared images are separately extracted using a backbone, then fused using a newly designed fusion module, and detection results are obtained using downstream modules. During the training process, an auxiliary detection branch improved from Programmable Gradient information (PGI) proposed in YOLOv9 is incorporated to enhance model accuracy and expedite convergence. Experimental results demonstrate that TwinFusion-YOLOv9 exhibits outstanding performance on public datasets, holding both theoretical and practical significance. © 2024 IEEE.

关键词： Infrared imaging

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Representation-Enhanced Semi-Supervised Learning for Flotation Working Condition Recognition 43

Representation-Enhanced Semi-Supervised Learning for Flotati...

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43rd Chinese Control Conference, CCC 2024

作者： Ai, Mingxi Li, Peng Zhang, Jin Xie, Yongfang Tang, Zhaohui Yunnan University School of Information Science and Engineering Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650504 China Central South University School of Automation Changsha410083 China

ISBN: (纸本)9789887581581

Timely and accurate recognition of flotation working conditions plays a crucial role in stabilizing process indicators and formulating production operation strategies. Soft sensing methods based on deep learning allow for automatic learning of features from input froth images directly, and is considered a promising flotation monitoring approach. However, the high demand for a large number of labeled samples severely hinders the application of such methods because acquiring froth videos is easily in flotation plants, but manually adding labels to video frames is often expensive. Therefore, a representation-enhanced semi-supervised learning method for flotation working condition recognition is proposed in this paper. Firstly, based on the consistency assumption, a student-teacher network is developed. It leverages massive unlabeled froth image data by ensuring the consistency of model outputs under variations in the input space. Secondly, to guide the student-teacher network learn class-aware cluster representation from unlabeled data, a novel prototypical contrastive learning method with hard negative mixing is proposed. Comparative and ablation experiments on industrial flotation dataset show the effectiveness of the proposed method. © 2024 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Contrastive Learning

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Research on Garbled Circuits-Based Identity Authentication and Ciphertext Hidden Access Control in Vehicular Networks 24

Research on Garbled Circuits-Based Identity Authentication a...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Jiao, Zhilong Bai, Fenhua Zhang, Chi Zhang, Xiaohui Gong, Bei Faculty of Information Engineering and Automation Kunming University of Science and Technology China Kunming China Department of Computing Beijing University of Technology China Beijing China

ISBN: (纸本)9798400717161

Since most communications in vehicular networks rely on wireless transmissions, information broadcast by vehicles is highly susceptible to interception and eavesdropping by third parties, making it vulnerable to various attacks. This can result in communication messages being tampered with and the leakage of vehicle locations and identity privacy. From a security perspective, this paper introduces Yao's garbled circuit mechanism, combining random bytes with the solutions of linear equations as identity information assigned to vehicle nodes. By employing dual authentication, the proposed approach addresses issues such as privacy protection, anonymity, replay attacks, and collusion attacks in vehicular networks, thereby enhancing security. Moreover, considering the post-authentication access control scenario, this paper proposes a novel LSSS matrix transformation algorithm for ciphertext hidden access control strategy. It realizes a partially hidden policy, which makes matrix generation more efficient by supporting both AND and OR gates, thus improving the access control efficiency while ensuring security. Performance analysis indicates that in terms of identity authentication, the scheme reduces computational costs by an average of 96.25%, and in access control, the scheme reduces key generation time by an average of 80%. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Ciphertext

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Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

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Research in Astronomy and Astrophysics 2023年第6期23卷 86-97页

作者： Zhen-Hong Shang Si-Yu Mu Kai-Fan Ji Zhen-Ping Qiang Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China Yunnan Observatories Chinese Academy of SciencesKunming 650216China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming 650224China

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on *** method transforms the transverse velocity field measurements into an optical flow field prediction *** evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope *** experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning *** the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 *** optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar *** code implementing the proposed method is available on https://***/lygmsy123/transverse-velocity-field-measurement.

关键词： methods:data analysis techniques:image processing Sun:fundamental parameters

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A Weighted Graph-based Method with Mapping of Material Composition to Target Performance 11

A Weighted Graph-based Method with Mapping of Material Compo...

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11th International Conference on information Systems and Computing technology, ISCTech 2023

作者： Wang, Chunxiu Yin, Jiancheng Shen, Tao Liu, Yingli Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650093 China Kunming University of Science and Technology Faculty of Materials Science and Engineering Kunming650093 China

ISBN: (纸本)9798350342406

Materials informatics is a method that combines statistics, machine learning techniques, and high-throughput computing to promote materials discovery and design. In this study, we propose a novel framework based on weighted graph for mapping material compositions to properties. The framework utilizes distributed representations of elemental compositions and incorporates soft attention mechanisms to capture the importance of different elements in the materials. By leveraging attention mechanism and residual network, the model can effectively extract features and generate fixed-length representations for materials, enabling efficient information aggregation. Experimental results on diverse datasets from the Material Project database demonstrate the effectiveness of our proposed method, achieving less errors compared to other methods that do not consider the material structure. This data-driven deep learning method showcases the potential of materials informatics in providing accurate predictions for various material properties. © 2023 IEEE.

关键词： Mapping

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Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Zhenguo Liu, Jianjian Li, Ying Yunnan Key Laboratory of Artificial Intelligence Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (纸本)9780000000002

Pre-trained language representation-based dependency parsing models have achieved obvious improvements in rich-resource languages. However, these model performances depend on the quality and scale of training data significantly. Compared with Chinese and English, the scale of Vietnamese Dependency treebank is scarcity. Considering human annotation is labor-intensive and time-consuming, we propose a multi-features enhanced multi-task learning framework to convert all heterogeneous Vietnamese Treebanks to a unified one. On the one hand, we exploit Tree BiLSTM and pattern embedding to extract global and local dependency tree features from the source Treebank. On the other hand, we propose to integrate these features into a multi-task learning framework to use the source dependency parsing to assist the conversion processing. Experiments on the benchmark datasets show that our proposed model can effectively convert heterogeneous treebanks, thus further improving the Vietnamese dependency parsing accuracy by about 7.12 points in LAS. © 2024 China National Conference on Computational Linguistics.

关键词： Multi-task learning

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DialectMoE: An End-to-End Multi-dialect Speech Recognition Model with Mixture-of-Experts 23rd

DialectMoE: An End-to-End Multi-dialect Speech Recognition M...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhou, Jie Gao, Shengxiang Yu, Zhengtao Dong, Ling Wang, Wenjun Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (纸本)9789819783663

Dialect speech recognition has always been one of the challenges in Automatic Speech Recognition (ASR) systems. While lots of ASR systems perform well in Mandarin, their performance significantly drops when handling dialect speech. This is mainly due to the obvious differences between dialects and Mandarin in pronunciation and the data scarcity of dialect speech. In this paper, we propose DialectMoE, a Chinese multi-dialects speech recognition model based on Mixture-of-Experts (MoE) in a low-resource conditions. Specifically, DialectMoE assigns input sequences to a set of experts using a dynamic routing algorithm, with each expert potentially trained for a specific dialect. Subsequently, the outputs of these experts are combined to derive the final output. Due to the similarities among dialects, distinct experts may offer assistance in recognizing other dialects as well. Experimental results on the Datatang dialect public dataset show that, compared with the baseline model, DialectMoE reduces Character Error Rate (CER) for Sichuan, Yunnan, Hubei and Henan dialects by 23.6%, 32.6%, 39.2% and 35.09% respectively. The proposed DialectMoE model demonstrates outstanding performance in multi-dialects speech recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dynamic routing algorithms

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Chinese Grammatical Error Correction via Large Language Model Guided Optimization Training 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Liu, Xiao Li, Ying Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9780000000002

Pre-trained language model-based methods for Chinese Grammatical Error Correction (CGEC) are categorized into Seq2Seq and Seq2Edit types. However, both Seq2Seq and Seq2Edit models depend on high-quality training data significantly. Considering the strong generation and inference ability of large language models (LLMs), we propose a large language model-guided optimization training method to exploit LLMs to extract error knowledge to optimize the traditional CGEC model training process. On the one hand, we use error types and confusion sets as extra knowledge to guide LLMs to generate diverse pseudo data, thus extending the error distribution of our training data. On the other hand, LLMs are utilized to infer the predicted results from our CGEC models and obtain the re-training data, thus iteratively optimizing our pre-trained CGEC models. Experiments on two benchmark datasets show that our LLMs-guided optimization method with small-scale training data can achieve comparable results with baseline models with large-scale training data. Detailed comparison experiments demonstrate that both the early deviser pseudo data and the later re-training data are extremely useful for traditional CGEC model optimization training, and can benefit from each other. We will release our code and prompts at https://***/SakuraAcedia/llm-cgec-got to facilitate future work. © 2024 China National Conference on Computational Linguistics.

关键词： Digital elevation model

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Multi-features Enhanced Multi-task Learning for Vietnamese Treebank Conversion 23rd

Multi-features Enhanced Multi-task Learning for Vietnamese ...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Zhenguo Liu, Jianjian Li, Ying Yunnan Key Laboratory of Artificial Intelligence Kunming Kunming650500 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9789819783663

Pre-trained language representation-based dependency parsing models have achieved obvious improvements in rich-resource languages. However, these model performances depend on the quality and scale of training data significantly. Compared with Chinese and English, the scale of Vietnamese Dependency treebank is scarcity. Considering human annotation is labor-intensive and time-consuming, we propose a multi-features enhanced multi-task learning framework to convert all heterogeneous Vietnamese Treebanks to a unified one. On the one hand, we exploit Tree BiLSTM and pattern embedding to extract global and local dependency tree features from the source Treebank. On the other hand, we propose to integrate these features into a multi-task learning framework to use the source dependency parsing to assist the conversion processing. Experiments on the benchmark datasets show that our proposed model can effectively convert heterogeneous treebanks, thus further improving the Vietnamese dependency parsing accuracy by about 7.12 points in LAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi-task learning

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