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3rd International Conference on Environmental Remote Sensing and Geographic information Technology, ERSGIT 2024

作者： Zeng, Hao Wang, Shun Xun, Lina The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9781510690219

One of the primary air pollutants that has a detrimental effect on both the environment and human health is sulfur dioxide. The ecosystem and climate greatly depend on the high-precision monitoring of sulfur dioxide. In this study, SO2 ground-based data in Xi'an and Hangzhou were calculated using the UV continuous spectral inversion method, and the accuracy was confirmed using OMI satellite data that had undergone bilinear spatial interpolation. Although there are still some discrepancies in the comparative analysis results, the results indicate a good correlation between the ground-based measurement results of the UV hyperspectral solar radiometer and OMI data products;the correlation numbers are 0.938 and 0.941, respectively, and the relative errors are roughly 10%. The ultraviolet hyperspectral solar radiometer can complement this study's findings with accuracy and is a valuable tool for environmental monitoring. © 2025 SPIE.

关键词： accuracy verification correlation sulfur dioxide distribution ultraviolet hyperspectral solar radiometer

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Latency optimization for task offloading in Space-Terrestrial collaborative networks based on deep reinforcement learning

Latency optimization for task offloading in Space-Terrestria...

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2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Song, Chuanlong Zhao, Yuxuan He, Jian School of Communication and Information Engineering Shanghai University Shanghai China Shanghai Aerospace Electronic Technology Institute Key Laboratory of Intelligent Computing for Spatial Heterogeneous Network Shanghai China

ISBN: (纸本)9781510690585

Satellite networks, as a crucial component of space communication systems, effectively mitigate the limitations of terrestrial communication networks in edge computing, such as insufficient ground station coverage and susceptibility to natural disasters. Therefore, this paper introduces satellite networks into the context of terrestrial edge computing, constructing a space-terrestrial collaborative network edge computing framework and investigating the latency issues in task offloading. Due to factors such as long communication distances in space-terrestrial networks, limited onboard resources, and rapid relative positional changes between nodes, system latency may increase. This study models the problem as a Markov Decision Process (MDP) with the objective of minimizing system latency. A Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is proposed for the space-terrestrial collaborative network edge computing scenario. Experimental results demonstrate that, compared to local task processing and the Deep Q-Network (DQN) algorithm, the proposed algorithm reduces the overall system overhead by an average of 28.12% and 13.29%, respectively, while also exhibiting better stability and convergence. © 2025 SPIE.

关键词： deep reinforcement learning edge computing satellite-ground collaborative network task offloading

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Obtaining Electromagnetic Properties of Multi-Type Media in Realistic Environments:State-of-the-Art and Prospects

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China Communications 2025年第1期22卷 25-40页

作者： Guo Lantu Guan Ke Liu Ting He Danping Zhang Haixia Zhu Qiuming Lu Jun Zhang Minggao China Research Institute of Radiowave Propagation Qingdao 266107China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Control Science and Engineering Shandong UniversityJinan 250061China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Jinan 250061China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Jiaxing University Jiaxing 314001China

To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current ***,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic *** a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic ***,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.

关键词： electromagnetic properties of media multi-type media parameter inversion ray tracing realistic environment

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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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Graph attention based on contextual reasoning and emotion-shift awareness for emotion recognition in conversations

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Complex and intelligent Systems 2025年第7期11卷 1-20页

作者： Yang, Juan Wei, Puling Du, Xu Shen, Jun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Hubei Province Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan 430081 China National Engineering Research Center for E‑Learning Central China Normal University Wuhan 430079 China School of Computing and Information Technology University of Wollongong 2522 NSW Australia

Emotion recognition in conversations has recently emerged as a hot research topic owing to its increasingly important role in developing intelligent empathy services. Thoroughly exploring the conversational context and accurately capturing emotion-shift information are highly crucial for accurate emotion recognition in conversations. However, existing studies generally failed to fully understand the complex conversational context due to their insufficient capabilities in extracting and integrating emotional cues. Moreover, they mainly focused on the speaker’s emotion inertia while paying less attention to explore multi-perspective emotion-shift patterns. To address these limitations, this study proposes a novel multimodal approach, namely, GAT-CRESA (Graph ATtention based on Contextual Reasoning and Emotion-Shift Awareness). Specifically, the multi-turn global contextual reasoning module iteratively performs contextual perception and cognitive reasoning for efficiently understanding the global conversational context. Then, GAT-CRESA explores emotion-shift information among utterances from both the speaker-dependent and the global context-based perspectives. Next, the emotion-shift awareness graphs are constructed for extracting significant local-level conversational context, where edge relations are determined by the learnt emotion-shift labels. Finally, the outputs of graphs are concatenated for final emotion recognition. The loss of emotion prediction task is combined together with those of two perspective’s emotion-shift learning for guiding the training process. Experimental results show that our GAT-CRESA achieves new state-of-art records with 72.77% ACC and 72.81% wa-F1 on IEMOCAP, and 65.44% ACC and 65.04% wa-F1 on MELD, respectively. The ablation results also indicate the effectiveness and rationality of each component in our approach. © The Author(s) 2025.

关键词： Contextual reasoning Emotion recognition in conversations (ERC) Emotion-shift Graph attention

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BEVUDA++: Geometric-Aware Unsupervised Domain Adaptation for Multi-View 3D Object Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 5109-5122页

作者： Zhang, Rongyu Liu, Jiaming Li, Xiaoqi Chi, Xiaowei Wang, Dan Du, Li Du, Yuan Zhang, Shanghang Nanjing University School of Electronic Science and Engineering Nanjing210093 China The Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science Beijing100871 China The Hong Kong University of Science and Technology Academy of Interdisciplinary Studies Hung Hom Hong Kong

Vision-centric Bird’s Eye View (BEV) perception holds considerable promise for autonomous driving. Recent studies have prioritized efficiency or accuracy enhancements, yet the issue of domain shift has been overlooked, leading to substantial performance degradation upon transfer. We identify major domain gaps in real-world cross-domain scenarios and initiate the first effort to address the Domain Adaptation (DA) challenge in multi-view 3D object detection for BEV perception. Given the complexity of BEV perception approaches with their multiple components, domain shift accumulation across multi-geometric spaces (e.g., 2D, 3D Voxel, BEV) poses a significant challenge for BEV domain adaptation. In this paper, we introduce an innovative geometric-aware teacher-student framework, BEVUDA++, to diminish this issue, comprising a Reliable Depth Teacher (RDT) and a Geometric Consistent Student (GCS) model. Specifically, RDT effectively blends target LiDAR with dependable depth predictions to generate depth-aware information based on uncertainty estimation, enhancing the extraction of Voxel and BEV features that are essential for understanding the target domain. To collaboratively reduce the domain shift, GCS maps features from multiple spaces into a unified geometric embedding space, thereby narrowing the gap in data distribution between the two domains. Additionally, we introduce a novel Uncertainty-guided Exponential Moving Average (UEMA) to further reduce error accumulation due to domain shifts informed by previously obtained uncertainty guidance. To demonstrate the superiority of our proposed method, we execute comprehensive experiments in four cross-domain scenarios, securing state-of-the-art performance in BEV 3D object detection tasks, e.g., 12.9% NDS and 9.5% mAP enhancement on Day-Night adaptation. © 1991-2012 IEEE.

关键词： Teaching

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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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FoodSky: A food-oriented large language model that can pass the chef and dietetic examinations

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Patterns 2025年第5期6卷

作者： Zhou, Pengfei Min, Weiqing Fu, Chaoran Jin, Ying Huang, Mingyu Li, Xiangyang Mei, Shuhuan Jiang, Shuqiang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Intelligent Computing Technology Chinese Academy of Sciences Suzhou China Zhongke Shenjian Technology Co. Ltd. Suzhou China

Food is the cornerstone of both survival and social life. With the increasing complexity of global dietary cultures, there is a growing demand for food intelligence to enable tasks like recipe recommendations and diet-disease correlation discovery. To address this, we introduce the food-oriented large language model (LLM) FoodSky, which offers fine-grained perception and reasoning on food data. We constructed a food corpus, FoodEarth, from various authoritative sources to enhance FoodSky's knowledge. We also developed the topic-based selective state space model and hierarchical topic retrieval augmented generation algorithms to improve FoodSky's ability to capture fine-grained food semantics and generate context-aware food-relevant text. Extensive experiments show that FoodSky significantly outperforms general-purpose LLMs on the Chinese National Chef Examination and Dietetic Examination, achieving an accuracy of 83.3% and 91.2%, respectively. Beyond enhancing culinary creativity and promoting healthier eating patterns, FoodSky aims to establish a new benchmark for domain-specific LLMs in addressing real-world food-related challenges. © 2025 The Author(s)

关键词： Food ingredients

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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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