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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Chen, Yulin Luo, Jing Tu, Xinhui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China College of Computer Science Central China Normal University Hubei Wuhan China

ISBN: (纸本)9798400717826

Recent advancements in large language models have led to significant improvements in various natural language processing tasks, including automated question answering. However, these models still struggle with providing accurate responses to complex biology questions, such as those found in the National College Entrance Examination. To address this issue, researchers have explored retrieval-augmented generation techniques, which incorporate external knowledge sources to enhance answer accuracy. While these methods have shown promise in improving efficiency, they often lack the ability to perform in-depth analysis of complex questions. In response to these limitations, the paper proposes a novel ReAct agent-based question answering method. This method combines biology textbook document libraries with Google search engine capabilities, leveraging the strengths of large language models. By integrating these components, this method can engage in autonomous decision-making and multi-step reasoning, allowing for a more thorough analysis of complex biology questions. Experimental results demonstrate the effectiveness of this new approach. When compared to responses generated directly by ChatGPT and GPT-4o models, the method showed significant improvements in accuracy for biology questions from the National College Entrance Examination. Specifically, the new method achieved accuracy increases of 11.1% and 4.64% over ChatGPT and GPT-4o, respectively. © 2024 Copyright held by the owner/author(s).

关键词： Question answering

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Leveraging Static Relationships for Intra-Type and Inter-Type Message Passing in Video Question Answering

arXiv

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arXiv 2025年

作者： Liang, Lili Sun, Guanglu School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Intelligent Information Processing and Application Harbin150080 China

Video Question Answering (VideoQA) is an important research direction in the field of artificial intelligence, enabling machines to understand video content and perform reasoning and answering based on natural language questions. Although methods based on static relationship reasoning have made certain progress, there are still deficiencies in the accuracy of static relationship recognition and representation, and they have not fully utilized the static relationship information in videos for in-depth reasoning and analysis. Therefore, this paper proposes a reasoning method for intra-type and inter-type message passing based on static relationships. This method constructs a dual graph for intra-type message passing reasoning and builds a heterogeneous graph based on static relationships for inter-type message passing reasoning. The intra-type message passing reasoning model captures the neighborhood information of targets and relationships related to the question in the dual graph, updating the dual graph to obtain intra-type clues for answering the question. The inter-type message passing reasoning model captures the neighborhood information of targets and relationships from different categories related to the question in the heterogeneous graph, updating the heterogeneous graph to obtain inter-type clues for answering the question. Finally, the answers are inferred by combining the intra-type and inter-type clues based on static relationships. Experimental results on the ANetQA and Next-QA datasets demonstrate the effectiveness of this method. © 2025, CC BY.

关键词： Question answering

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Semantic Data Augmentation for Few-Shot Biomedical Named Entity Recognition

Semantic Data Augmentation for Few-Shot Biomedical Named Ent...

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

作者： Zhang, Ying Wang, Weihua College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798350368741

Biomedical Named Entity Recognition (BioNER) aims to identify and classify entities in biomedical text. This task struggles with data scarcity due to limited annotated data. Although data augmentation is effective, existing methods fail to handle the complex semantic mappings on biomedical terminology, which results in the generated samples lacking semantic diversity. In this paper, we propose a data augmentation method to create semantically diverse and coherent training samples for few-shot BioNER. The method utilizes the Unified Medical Language System (UMLS) for entity replacement and combines with a Masked Language Model (MLM) to generate contextually relevant words. Experimental results show that our method improves BioNER performance in few-shot scenarios. Compared to all baseline models, our method achieves an average F1 score improvement of 7.6% and 11.6% on the NCBI and JNLPBA datasets, respectively. Our source code and data are available at https://***/ABC8184/SDA. © 2025 IEEE.

关键词： BioNER data augmentation few-shot learning masked language model UMLS

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The GECAM ground search system for gamma-ray transients

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Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 161-174页

作者： Ce Cai Yan-Qiu Zhang Shao-Lin Xiong Ping Wang Jian-Hui Li Xiao-Bo Li Cheng-Kui Li Yue Huang Shi-Jie Zheng Li-Ming Song Shuo Xiao Qi-Bin Yi Yi Zhao Sheng-Lun Xie Rui Qiao Yan-Qi Du Zhi-Wei Guo Wang-Chen Xue Chao Zheng Jia-Cong Liu Chen-Wei Wang Wen-Jun Tan Yue Wang Jin-Peng Zhang Chao-Yang Li Guo-Ying Zhao Xiao-Yun Zhao Xiao-Lu Zhang Zhen Zhang Wen-Xi Peng Xiang Ma Jing-Yan Shi Dong-Ya Guo Jin Wang Xin-Qiao Li Xiang-Yang Wen Zheng-Hua An Fan Zhang College of Physics and Hebei Key Laboratory of Photophysics Research and Application Hebei Normal UniversityShijiazhuang050024China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing100049China University of Chinese Academy of Sciences Beijing100049China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang550001China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang550001China School of Physics and Optoelectronics Xiangtan UniversityXiangtan411105China School of Computer and Information Dezhou UniversityDezhou253023China Institute of Astrophysics Central China Normal UniversityWuhan430079China

In the era of time-domain,multi-messenger astronomy,the detection of transient events on the high-energy electromagnetic sky has become more important than *** gravitational wave high-energy electromagnetic counterpart all-sky monitor(GECAM)is a dedicated mission to monitor gamma-ray transients,launched in December,2020.A real-time on-board trigger and location software,using the traditional signal-to-noise ratio(SNR)method for blind search,is constrained to relatively bright signals due to the limitations in on-board computing resources and the need for real-time *** this work,we developed a ground-based pipeline for GECAM to search for various transients,especially for weak bursts missed by on-board *** pipeline includes both automatic and manual mode,offering options for blind search and targeted *** targeted search is specifically designed to search for interesting weak bursts,such as gravitational wave-associated gamma-ray bursts(GRBs).From the ground search of the data in the first year,GECAM has been triggered by 54 GRBs and other transients,including soft gamma-ray repeaters,X-ray binaries,solar flares,terrestrial gamma-ray *** report the properties of each type of triggers,such as trigger time and light *** this search pipeline and assuming a soft Band spectrum,the GRB detection sensitivity of GECAM is increased to about 1.1×10^(-8)erg cm^(-2)s^(-1)(10-1000 ke V,burst duration of 20 s).These results demonstrate that the GECAM ground search system(both blind search and targeted search)is a versatile pipeline to recover true astrophysical signals which were too weak to be found in the on-board search.

关键词： gamma-ray sources gravitational waves data analysis gamma-ray telescopes

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Unifying Dual-Space Embedding for Entity Alignment via Contrastive Learning 31

Unifying Dual-Space Embedding for Entity Alignment via Contr...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Cunda Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Entity alignment (EA) aims to match identical entities across different knowledge graphs (KGs). Graph neural network-based entity alignment methods have achieved promising results in Euclidean space. However, KGs often contain complex local and hierarchical structures, which are hard to represent in a single space. In this paper, we propose a novel method named as UniEA, which unifies dual-space embedding to preserve the intrinsic structure of KGs. Specifically, we simultaneously learn graph structure embeddings in both Euclidean and hyperbolic spaces to maximize the consistency between embeddings in the two spaces. Moreover, we employ contrastive learning to mitigate the misalignment issues caused by similar entities, where embeddings of similar neighboring entities become too close. Extensive experiments on benchmark datasets demonstrate that our method achieves state-of-the-art performance in structure-based EA methods. Our code is available at https://***/wonderCS1213/UniEA. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Sentence Ordering 31

C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Se...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Xiaotao Li, Jiang Su, Xiangdong Zhang, Fujun College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Sentence ordering is the task of rearranging a set of unordered sentences into a coherent and logically consistent sequence. Recent work has primarily used pre-trained language models, achieving significant success in the task. However, existing sentence ordering corpora are predominantly in English, and comprehensive benchmark datasets for non-English languages are unavailable. Meanwhile, current datasets often insert specific markers into paragraphs, inadvertently making the logical sequence between sentences more apparent and reducing the models' ability to handle genuinely unordered sentences in real applications. To address these limitations, we develop C3LRSO, a high-quality Chinese sentence ordering dataset that overcomes the aforementioned shortcomings by providing genuinely unordered sentences without artificial segmentation cues. Furthermore, given the outstanding performance of large language models on NLP tasks, we evaluate these models on our dataset for this task. Additionally, we propose a simple yet effective parameter-free approach that outperforms existing methods on this task. Experiments demonstrate the challenging nature of the dataset and the strong performance of our proposed method. These findings highlight the potential for further research in sentence ordering and the development of more robust language models. Our dataset is freely available at https://***/JasonGuo1/C3LRSO. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Optical polarized orthogonal matrix

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Photonics Research 2025年第2期13卷 373-381页

作者： SHUJUN ZHENG JIAREN TAN XIANMIAO XU HONGJIE LIU YI YANG XIAO LIN XIAODI TAN Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal University Department of Electrical and Computer Engineering Duke University College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal University

Multiplexing technology serves as an effective approach to increase both information storage and transmission capability. However, when exploring multiplexing methods across various dimensions, the polarization dimension encounters limitations stemming from the finite orthogonal combinations. Given that only two mutually orthogonal polarizations are identifiable on the basic Poincaré sphere, this poses a hindrance to polarization modulation. To overcome this challenge, we propose a construction method for the optical polarized orthogonal matrix(OPOM), which is not constrained by the number of orthogonal combinations. Furthermore, we experimentally validate its application in high-dimensional multiplexing of polarization holography. We explore polarization holography technology, capable of recording amplitude, phase, and polarization, for the purpose of recording and selective reconstruction of polarization multi-channels. Our research reveals that, despite identical polarization states, multiple images can be independently manipulated within distinct polarization channels through orthogonal polarization combinations, owing to the orthogonal selectivity among information.

关键词： Multiplex holography Optical data storage Optical encryption Orbital angular momentum multiplexing Signal transmission Three dimensional displays

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Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation 31

Distance-Adaptive Quaternion Knowledge Graph Embedding with ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet through either semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with the geometric distance of entities to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. We then utilize distance-adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://***/llqy123/DaBR. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Data-Driven Group Formation Control of Cyber-Physical Systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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A priority-based blockchain transaction packaging algorithm in a cloud-edge-end collaboration computing environment

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Knowledge and information Systems 2025年 1-34页

作者： Li, Chunlin Zeng, Haibo Jiang, Kun Yang, Kaijun Yang, Shaohua Jia, Qingren Ministry of Industry and Information Technology Beijing China The Second Surveying and Mapping Institute of Hunan Province Hunan Changsha China Department of Computer Science Wuhan University of Technology Wuhan430063 China Anhui Provincial Key Laboratory of Philosophy and Social Sciences for Digital Rural Construction and Governance & School of Management Anhui Science and Technology University Anhui Chuzhou China College of Electronic Science and Technology National University of Defense Technology Hunan Changsha China

With the rapid development of mobile internet, intelligent IoT, and 5G communication technologies, many IoT devices connect to the Industrial Internet of Things, generating significant data. Blockchain is widely used in identity authentication and trust management due to its reliability. In blockchain technology, transaction packaging is a crucial component of the consensus mechanism and is critical to enhancing fairness and service quality in request processing. However, the flat structure of the blockchain, the necessity for multi-party consensus, and the profit-seeking nature of nodes lead to issues such as unfair transaction processing and prolonged response times in cloud-edge-end architectures, which are critical for empowering intelligent edge applications. To address these challenges, we have refined the blockchain consensus mechanism and introduced a novel packaging algorithm, ITFPA (Improved time-fee packaging algorithm), within the cloud-edge-end environment. This algorithm models the transaction packaging problem as a 0–1 knapsack problem and employs a branch-and-bound method to find the optimal solution. The proposed model considers both the transaction waiting time and transaction fee, using the weighted result of these factors as the priority for the transaction. We compared the proposed algorithm with the WaitTime and TxFee algorithms across four metrics: system fairness, transaction response time, and block priority. The experimental results demonstrate that the proposed algorithm enhances system fairness, reduces transaction response times, and improves service quality to a significant degree. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Industrial internet of things (IIoT)

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