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2023 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2023

作者： Sun, Xinhao An, Siqi Gao, Xiaoting Cui, Enchang College of Light Industry Liaoning University Shenyang China Jilin University Key Laboratory of Symbolic Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

ISBN: (纸本)9798350309003

With the complexity of the functions of modern buildings, the problem of vertical traffic in buildings is becoming more and more prominent. As the only vertical transportation, the elevator is a necessary prerequisite for the development of modern buildings to solve its group control distribution problem. In this paper, a vertical traffic scheduling control method is proposed based on dual fuzzy neural network, a recognition strategy is designed to identify the passenger flow models. The dual fuzzy neural network is used to first identify the traffic network model in which the elevator is located, and then combine the corresponding weights and the confidence of the group control allocation to complete the group control scheduling. Finally, the proposed method is verified through a semi-physical simulation platform, proving the correctness and effectivness for traffic pattern recognition and scheduling strategy optimization of group controllers. © 2023 IEEE.

关键词： Fuzzy neural networks

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SSRMamba: Efficient Visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Baisong Wang, Xingwang Xu, Haixiao College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Krowledge Engineering Ministry of Education Jilin University China

ISBN: (纸本)9798350368741

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba. © 2025 IEEE.

关键词： hyperspectral image Mamba spectral super-resolution state space model

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Local feature aggregation algorithm based on graph convolutional network

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Frontiers of Computer Science 2022年第3期16卷 203-205页

作者： Hao WANG Liyan DONG Minghui SUN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China

1Introduction and main contributions In the field of social networks and knowledge graphs,semi-supervised learning models based on graph convolutional networks have achieved great success in node classification[1],inductive node embedding[2],link prediction[3],and *** semi-supervised models based on graph convolutional network(GCN)[4]expect to obtain more feature information of a graph or accelerate the training.

关键词： convolution aggregation semi

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More Flexible Proximity Wildcards Path Planning with Compressed Path Databases 34

More Flexible Proximity Wildcards Path Planning with Compres...

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34th International Conference on Automated Planning and Scheduling, ICAPS 2024

作者： Chen, Xi Zhang, Yue Zhang, Yonggang College of Software Jilin University China College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

ISBN: (纸本)9781577358893

Grid-based path planning is one of the classic problems in AI, and a popular topic in application areas such as computer games and robotics. Compressed Path Databases (CPDs) are recognized as a state-of-the-art method for grid-based path planning. It is able to find an optimal path extremely fast without state-space search. In recent years, researchers have tended to focus on improving CPDs by reducing CPD size or improving search performance. Among various methods, proximity wildcards are one of the most proven improvements in reducing the size of CPD. However, its proximity area is significantly restricted by complex terrain, which significantly affects the pathfinding efficiency and causes more additional costs. In this paper, we enhance CPDs from the perspective of improving search efficiency and reducing search costs. Our work focuses on using more flexible methods to obtain larger proximity areas, so that more heuristic information can be used to improve search performance. Experiments conducted on the Grid-Based Path Planning Competition (GPPC) benchmarks demonstrate that the two proposed methods can effectively improve search efficiency and reduce search costs by up to 3 orders of magnitude. Remarkably, our methods can further reduce the storage cost, and improve the compression capability of CPDs simultaneously. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Motion planning

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In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought 41

In-Context Decision Transformer: Reinforcement Learning via ...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Sili Hu, Jifeng Chen, Hechang Sun, Lichao Yang, Bo School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Lehigh University BethlehemPA United States

In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-improvement not requiring gradient updates, current works still suffer from high computational costs when the across-episodic sequence increases with task horizons. To this end, we propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. Specifically, IDT is inspired by the efficient hierarchical structure of human decision-making and thus reconstructs the sequence to consist of high-level decisions instead of low-level actions that interact with environments. As one high-level decision can guide multi-step low-level actions, IDT naturally avoids excessively long sequences and solves online tasks more efficiently. Experimental results show that IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods. In particular, the online evaluation time of our IDT is 36× times faster than baselines in the D4RL benchmark and 27× times faster in the Grid World benchmark. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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A Simple but Effective Approach for Unsupervised Few-Shot Graph Classification 24

A Simple but Effective Approach for Unsupervised Few-Shot Gr...

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33rd ACM Web Conference, WWW 2024

作者： Liu, Yonghao Huang, Lan Cao, Bowen Li, Ximing Giunchiglia, Fausto Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education China

ISBN: (纸本)9798400701719

Graphs, as a fundamental data structure, have proven efficacy in modeling complex relationships between objects and are therefore found in wide web applications. Graph classification is an essential task in graph data analysis, which can effectively assist in extracting information and mining content from the web. Recently, few-shot graph classification, a more realistic and challenging task, has garnered great research interest. Existing few-shot graph classification models are all supervised, assuming abundant labeled data in base classes for meta-training. However, sufficient annotation is often challenging to obtain in practice due to high costs or demand for expertise. Moreover, they commonly adopt complicated meta-learning algorithms via episodic training to transfer prior knowledge from base classes. To break free from these constraints, in this paper, we propose a simple yet effective approach named SMART for unsupervised few-shot graph classification without using any labeled data. SMART employs transfer learning philosophy instead of the previously prevailing meta-learning paradigm, avoiding the need for sophisticated meta-learning algorithms. Additionally, we adopt a novel mixup strategy to augment the original graph data and leverage unsupervised pretraining on these data to obtain the expressive graph encoder. We also utilize the prompt tuning technique to alleviate the overfitting and low fine-tuning efficiency caused by the limited support samples of novel classes. Extensive experimental results demonstrate the superiority of our proposed approach, significantly surpassing even leading supervised few-shot graph classification models. Our code is available here. © 2024 ACM.

关键词： Graph neural networks

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A Method for Extracting Electronic Medical Record Entities by Fusing Multichannel Self-Attention Mechanism with Location Relationship Features 9th

A Method for Extracting Electronic Medical Record Entities b...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Xu, Hongyan Wang, Hong Feng, Yong Wang, Rongbing Zhang, Yonggang College of Information Liaoning University Shenyang110036 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819959709

With the implementation of the "Internet+" strategy, electronic medical records are generally applied in the medical field. Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients’ states, but the existing methods for extracting entities from electronic medical records have problems of redundant information, overlapping entities, and low accuracy rates. Therefore, this paper proposes an entity extraction method for electronic medical records based on the network framework of BERT-BiLSTM, which incorporates a multichannel self-attention mechanism and location relationship features. First, the text input sequence was encoded using the BERT-BiLSTM network framework, and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mechanism. Then, the position relation characteristic was used to extract the local semantic message of the text, and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained. Next, the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix. Finally, the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus, and the experimental results indicate that the proposed method surpasses existing methods in terms of precision, recall, F1 value and training time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Location

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Enhancing Nested Named Entity Recognition in Chinese EMRs: A Collaborative Approach

Enhancing Nested Named Entity Recognition in Chinese EMRs: A...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yue, Hongtao Feng, Xiaoyue Xu, Hanchi Chu, Haoxuan Wang, Yushi Guan, Renchu Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Changchun China First Hospital Jilin University Cardiovascular Center Changchun China

ISBN: (纸本)9798350386226

Named entity recognition (NER) in electronic medical records (EMRs) is critical for identifying medical entities, constructing medical knowledge graphs, and supporting clinical decision-making. However, the scarcity of EMR datasets and the challenges posed by the complexity of Chinese medical texts hinder progress in this area. To address these issues, we introduce CMR-NER, a nested named entity recognition model that enhances entity prediction by integrating multiple features and considering a global view of entity boundaries. Leveraging the robust generalization capabilities of the large language model, CMR-NER involves collaboration with medical experts to ensure accuracy and reduce annotation costs. Additionally, we present HF-NER, a high-quality dataset specifically curated for Chinese EMR entity recognition focusing on heart failure. This dataset, constructed through a methodology combining ChatGPT's capabilities and expert validation, is a significant contribution to the field. It facilitates a comprehensive evaluation of CMR-NER. Experimental results demonstrate that our approach achieves comparable or superior performance to existing methods, underscoring its effectiveness in this challenging domain. © 2024 IEEE.

关键词： Electronic health record

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A frequency and two-hop configuration checking-driven local search algorithm for the minimum weakly connected dominating set problem

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Neural Computing and Applications 2024年第22期36卷 13833-13852页

作者： Li, Ruizhi He, Jintao Lin, Cuisong Liu, Ying Hu, Shuli Yin, Minghao Jilin University of Finance and Economics Changchun130117 China Northeast Normal University Changchun130117 China Business Big Data Research Center of Jilin Province Changchun130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

The minimum weakly connected dominating set problem is a typical NP-hard problem with a wide range of applications. To solve this problem, we propose a frequency property and two-hop configuration checking strategy-driven local search algorithm (FCC2LS). In this algorithm, we first propose a lock-vertex-based initial solution construction procedure. This procedure guarantees that certain vertices, which must be included in the optimal solution, are added to the solution. Second, we propose a two-hop configuration checking strategy and a frequency property. The two-hop configuration checking strategy is a new variant of the original configuration checking strategy, which prohibits vertices with unchanged configuration from being added to the candidate solution. The frequency property is used to record the number of times for each vertex is added to the solution, which can increase the diversity of selected vertices. Third, we combine two scoring functions, Dscore and Nscore, with the above strategies and propose effective vertex selection methods to help the algorithm select suitable vertices to add into or remove from the candidate solutions. Finally, we evaluate the proposed algorithm FCC2LS with four state-of-the-art algorithms on four groups of benchmark instances. Experimental results show that our algorithm performs better on four classical benchmark instances. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Combinatorial optimization

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A Morphology Focused Cell Detection Model for Histopathology Images

A Morphology Focused Cell Detection Model for Histopathology...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Zhe Wei, Fangyue Guo, Shuyu Che, Xiaoting Bai, Tian Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China

ISBN: (纸本)9798350337488

The accurate automatic recognition of cell locations is of great significance for downstream tasks in pathology. Due to the various size and distribution of different cell types, previous cell detection methods applied fixed circles as labels to localize cell position by default, which makes less precise and more difficult to adapt various cell morphology. In this paper, we integrate adaptive areas of interests based on morphology to address the limitation in detecting cells with irregular shapes in pathological images. Moreover, a EGSI module is proposed to extract global distribution of cells in slices, which enables model to acquire more rich semantic information. We exhibit favorable F1 score of our method on four public datasets. Experimental results demonstrate that the proposed method could detect cells more accurately in those images with different cell morphologies and dense cell distribution. © 2023 IEEE.

关键词： cell detection cellular morphology digital pathology medical image analysis

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