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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Kun Wang, Lihui Yang, Menglong Xu, Jingwen Cheng, Xinyu Zhang, Jian Zhu, Yuemin Wei, Hongjiang Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity. © 2024 IEEE.

关键词： Image registration

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Target Hierarchy-Guided Knowledge Tracing: Fine-Grained Knowledge State Modeling

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SSRN 2024年

作者： Sun, Xinjie Zhang, Kai Shen, Shuanghong Wang, Fei Guo, Yuxiang Liu, Qi School of Computer Science and Technology University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei China

Knowledge Tracing (KT) focuses on modeling the exercise process of students, assessing their knowledge state changes during the exercise process, and further providing targeted guidance for teaching and learning. Current KT models have made significant progress by utilizing deep learning technologies to model relevant attributes of the questions. However, in current knowledge tracing strategies, we have yet to exhaustively explore how to precisely evaluate the hierarchies of students’ knowledge mastery (encompassing Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating) to better discern their current target hierarchy. To address the limitations of the KT model in accurately identifying and positioning target hierarchy, our research focuses on two key areas: first, we dynamically track students’ target hierarchies by evaluating their feedback on questions at different hierarchies;second, we assess their actual question-solving abilities within these target hierarchies, thereby optimizing our evaluation of their knowledge status. Based on these insights, we propose the Target Hierarchy-guided Knowledge Tracing (THKT) model. In this model, we first incorporate the identification of question hierarchy into the model representation. Then, to avoid homogenization of the target hierarchy, we dynamically track the appropriate hierarchy for students based on their varying feedback to questions of the same hierarchy, thereby pinpointing their learning target hierarchies. Simultaneously, we develop a KCs applied ability learning module that works in conjunction with the target hierarchies to generate interpretable prediction results. The proposed THKT model has been tested and evaluated using three public, real-world educational datasets. The findings clearly demonstrate that our approach shines in the realm of KT prediction tasks, providing significantly interpretability. For broader research, we plan to provide the source code at: https://***/xinj

关键词： Students

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LGMD-based Neural Network for Detecting Abnormal Velocity Targets in Moving Crowd 9

LGMD-based Neural Network for Detecting Abnormal Velocity Ta...

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9th International Symposium on Computer and Information Processing technology, ISCIPT 2024

作者： Zhao, Zhongxiang Hu, Bin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University College of Computer Science & Technology Department of Computer Science Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (纸本)9798350388404

Detecting abnormal velocity targets is critical especially in public surveillance for crowd activity monitoring. Although there are some explorations on one such issue, traditional methods still perform poorly in complex scenes. In this paper, a novel visual neural network is investigated to perceive abnormal velocity targets in moving crowd, based on the latest neurophysiological achievements revealed in locusts' vision systems. The proposed neural network contains two neural counterparts, i.e., the presynaptic and the postsynaptic networks. The former one receives visual signals and processes them to capture the motion cues of different targets, and the latter one filters the salience energies to perceive abnormal velocity targets in the field of view. Numerical experiments carried out show that the proposed neural network can effectively detect abnormal velocity targets in moving crowd. This study is an important step towards dynamic visual information processing in crowd behavior analysis. © 2024 IEEE.

关键词： Visual servoing

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Named Entity Recognition Based on Boundary Enhanced for Chinese Electronic Medical Records 12

Named Entity Recognition Based on Boundary Enhanced for Chin...

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12th International Conference of Information and Communication technology, ICTech 2023

作者： Chen, Xueliang Liu, Tianming Tang, Gongzheng Liu, Yunjiang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shandong University Laboratory of Big Data and Artificial Intelligence Technology Beijing China

ISBN: (纸本)9798350332919

With the continuous popularization of medical informatization, the number of medical documents, medical guides, electronic medical records and other medical texts shows a trend of rapid growth and huge scale. Medical texts contain great social value. However, most medical texts exist in an unstructured form and cannot be directly applied to products. Therefore, how to fully explore the potential valuable information has become a hot research direction in the field of natural language processing. Named entity recognition is the basic task of natural language processing, and also the key task of medical data mining and analysis. It also lays the foundation for downstream tasks such as medical term relationship extraction and medical term standardization. Named entity recognition aims to find some key phrases in a large number of texts, such as people's names, place names, diseases and drugs. At present, named entity recognition has made great achievements in the open field, but there are still many problems in the research of named entity recognition in the medical field, such as the strong professionalism of entities, the long length of some types of entities, and the existence of complex entities mixed in Chinese and English. Aiming at the problem that the length of named entities in existing medical electronic medical records is longer than that in other fields, resulting in low extraction accuracy, a named entity recognition algorithm based on boundary enhancement is proposed. The model adopts the method of multi-task learning, which can effectively alleviate the boundary problem caused by the length of medical terms. We have tried various models on our medical electronic medical record data set. Experimental results show that this method is simple and effective in medical named entity recognition. © 2023 IEEE.

关键词： data mining

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Network configuration approach based on the reliability service 12

Network configuration approach based on the reliability serv...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Fengfei, Kuang Yaojm, Lin Xiehua, Yu Meilong, Chen University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Fujian Quanzhou362332 China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350310153

Wireless communication network plays an important role in various industries. One of the key concern in wireless communication is network security. This paper proposes an optimal transmission lines assignment with maximal reliabilities in the wireless communication. Firstly, a reliability evaluation metric with a transmission line assignment is proposed for calculating the reliability service under some constraints. Secondly, based on the reliability, non-dominated sorting Genetic algorithm is used for working out the optimal solution via considering the network security. Some simulated experiments are conducted that the proposed approach is able to obtain the reasonable solution under the time constraints. © 2022 IEEE.

关键词： Reliability

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Semi-supervised Learning for Fine-Grained Entity Typing with Mixed Label Smoothing and Pseudo Labeling 28th

Semi-supervised Learning for Fine-Grained Entity Typing wit...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Bo Zhang, Zhengqi Du, Ming Wang, Hongya Song, Hui Xiao, Yanghua School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Fudan-Aishu Cognitive Intelligence Joint Research Center Shanghai China

ISBN: (纸本)9783031306747

Distant supervision (DS) has been proposed to automatically annotate data and achieved significant success in fine-grained entity typing(FET). Despite its efficiency, distant supervision often suffers from the noisy labeling problem. To solve the noisy labeling problem, existing approaches assume the existence of "clean" and "noisy" sets in the training data and use different types of methods to utilize them. However, they still suffer from the confirmation bias problem in the "noisy" set and the false positive problem in the "clean" set. To address these issues, we propose a novel semi-supervised learning method with mixed label smoothing and pseudo labeling for distantly supervised fine-grained entity typing. Specifically, to solve the false positive problem on the "clean" set, we propose a mixed label smoothing method to smooth the labels of the "clean" set to train the FET model. To solve the confirmation bias problem on the "noisy" set, we do not consider the labels in the "noisy" set and use a pseudo labeling technique to deal with the "noisy" set. Extensive experiments conducted on three widely used FET datasets show the effectiveness of our proposed approach. The source code is publicly available at https://***/xubodhu/NFETC-SSL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing

Uniform Attention Maps: Boosting Image Fidelity in Reconstru...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenyi Mo Tianyu Zhang Yalong Bai Bing Su Ji-Rong Wen Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Du Xiaoman Technology

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Text-guided image generation and editing using diffusion models have achieved remarkable advancements. Among these, tuning-free methods have gained attention for their ability to perform edits without extensive model adjustments, offering simplicity and efficiency. However, existing tuning-free approaches often struggle with balancing fidelity and editing precision. Reconstruction errors in DDIM Inversion are partly attributed to the cross-attention mechanism in U-Net, which introduces misalignments during the inversion and reconstruction process. To address this, we analyze reconstruction from a structural perspective and propose a novel approach that replaces traditional cross-attention with uniform attention maps, significantly enhancing image reconstruction fidelity. Our method effectively minimizes distortions caused by varying text conditions during noise prediction. To complement this improvement, we introduce an adaptive mask-guided editing technique that integrates seamlessly with our reconstruction approach, ensuring consistency and accuracy in editing tasks. Experimental results demonstrate that our approach not only excels in achieving high-fidelity image reconstruction but also performs robustly in real image composition and editing scenarios. This study underscores the potential of uniform attention maps to enhance the fidelity and versatility of diffusion-based image processing methods. Code is available at https://***/Mowenyii/Uniform-Attention-Maps.

关键词： Computer vision Accuracy Codes Image synthesis Noise Reconstruction algorithms Distortion Diffusion models Robustness Image reconstruction

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Generalization error bounds for two-stage recommender systems with tree structure 24

Generalization error bounds for two-stage recommender system...

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

作者： Jin Zhang Ze Liu Defu Lian Enhong Chen School of Artificial Intelligence and Data Science University of Science and Technology of China School of Computer Science and Technology University of Science and Technology of China School of Artificial Intelligence and Data Science and School of Computer Science and Technology University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Hefei Anhui China

ISBN: (纸本)9798331314385

Two-stage recommender systems play a crucial role in efficiently identifying relevant items and personalizing recommendations from a vast array of options. This paper, based on an error decomposition framework, analyzes the generalization error for two-stage recommender systems with a tree structure, which consist of an efficient tree-based retriever and a more precise yet time-consuming ranker. We use the Rademacher complexity to establish the generalization upper bound for various tree-based retrievers using beam search, as well as for different ranker models under a shifted training distribution. Both theoretical insights and practical experiments on real-world datasets indicate that increasing the branches in tree-based retrievers and harmonizing distributions across stages can enhance the generalization performance of two-stage recommender systems.

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A topic detection method based on KM-LSH Fusion algorithm and improved BTM model

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Soft Computing 2024年第19期28卷 11421-11438页

作者： Liu, Wenjun Guo, Huan Gan, Jiaxin Wang, Hai Wang, Hailan Zhang, Chao Peng, Qingcheng Sun, Yuyan Yu, Bao Hou, Mengshu Li, Bo Li, Xiaolei School of Computer and Software Engineering XiHua University Chengdu610039 China Chengdu611731 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Sichuan Provincial Engineering Research Center of Hydroelectric Energy Power Equipment Technology Chengdu610039 China

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： K-means clustering

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Research on Metaverse Multi-person Linkage Using Mobile Edge Computing Based on Extended Reality Under the Immersive Experience of Zhijiang Peace Culture Memorial Hall 16th

Research on Metaverse Multi-person Linkage Using Mobile Edge...

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Liu, Yiwen Fu, Jinrong Yan, Haobo Gao, Yanxia Peng, Ling Qu, Taiguo School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China

ISBN: (纸本)9783031580529

This study aims to deeply explore the impact of mobile edge computing based on extended reality technology on the multi-person linkage of the Zhijiang Peace Culture Memorial Hall under the immersive experience. In the part of research background and significance, it discusses the rise of the application of XR technology in the field of culture and education, emphasizing the importance of education on the history of revolution and its value in the social sense. Then, it expounds the core topic of this research aimed at integrating XR technology with education on the history of revolution, and presenting visitors with more realistic and immersive historical scenes by creating an immersive experience of multi-person linkage. The use of these technologies enables visitors to transcend time and space barriers, directly participate in historical events, and deeply appreciate the intrinsic value of red culture. In order to achieve multi-person linkage, this study adopts mobile edge computing technology to ensure that multiple visitors can realize real-time interaction in the virtual scene and jointly build a collective experience of the Metaverse. This research has achieved positive results in the fields of XR technology application, education on the history of revolution, and mobile edge computing. It provides a useful reference for the modernization and upgrading of the Zhijiang Peace Culture Memorial Hall, and also provides a new paradigm for the cultural promotion of multi-person linkage experience in Metaverse. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Mobile edge computing

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