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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhang, Ming Huang, Caishuang Wu, Yilong Liu, Shichun Zheng, Huiyuan Dong, Yurui Shen, Yujiong Dou, Shihan Zhao, Jun Ye, Junjie Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Institute of Modern Languages and Linguistics Fudan University China

ISBN: (纸本)9798891761643

Task-oriented dialogue (TOD) systems aim to efficiently handle task-oriented conversations, including information collection. How to utilize TOD accurately, efficiently and effectively for information collection has always been a critical and challenging task. Recent studies have demonstrated that Large Language Models (LLMs) excel in dialogue, instruction generation, and reasoning, and can significantly enhance the performance of TOD through fine-tuning. However, current datasets primarily cater to user-led systems and are limited to predefined specific scenarios and slots, thereby necessitating improvements in the proactiveness, diversity, and capabilities of TOD. In this study, we present a detailed multi-domain task-oriented data construction process for conversations, and a Chinese dialogue dataset generated based on this process, TransferTOD, which authentically simulates human-computer dialogues in 30 popular life service scenarios. Leveraging this dataset, we trained a model using full-parameter fine-tuning called TransferTOD-7B, showcasing notable abilities in slot filling and questioning. Our work has demonstrated its strong generalization capabilities in various downstream scenarios, significantly enhancing both data utilization efficiency and system performance. The data is released in https://***/KongLongGeFDU/TransferTOD. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Data Driven Comprehensive Performance Evaluation of Aeroengines:A Network Dynamic Approach

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Engineering 2025年第3期46卷 292-305页

作者： Yuting Wang Feng Liu Feng Xi Bofei Wei Dongli Duan Zhiqiang Cai Shubin Si School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory of Industrial Engineering and Intelligent Manufacturing(Ministry of Industry and Information Technology of the People’s Republic of China) Xi’an 710072China AECC Aviation Power Co. Ltd.Xi’an 710005China School of Information and Control Engineering Xi’an University of Architecture and TechnologyXi’an 710311China

Aeroengines,often regarded as the heart of aircraft,are crucial for flight safety and *** performance evaluation of aeroengines supports Prognostics and Health Management(PHM)and aeroengine digital *** to their highly integrated nature,aeroengines present challenges in performance evaluation because their test-run data are high-dimensional,large-scale,and exhibit strong nonlinear correlations among test *** solve this problem,this study proposes a unified framework of the comprehensive performance evaluation of aeroengines to assess performance objectively and ***,the network model and the dynamics model of aeroengine performance are constructed driven by test-run data,which can explain the patterns of system state changes and the internal relationship,and depict the system *** on that,three perturbations in the model are used to simulate three fault modes of ***,the comprehensive performance evaluation indexes of aeroengines are proposed to evaluate the performance dynamically from two dimensions,the coupling performance and the activity *** test-run qualified and four test-run failed aeroengines are used to validate and establish the qualified *** results demonstrate that the comprehensive evaluation indexes can distinguish test-run qualified and test-run failed *** changing the dynamic parameters,the comprehensive performance under any thrust and inlet guide vanes(IGV)angle can be estimated,broadening the test-run scenarios beyond a few typical *** novel approach offers significant advancements for the comprehensive performance evaluation and management of aeroengines,paving the way for future PHM and aeroengine digital engineering developments.

关键词： Comprehensive performance evaluation Aeroengine performance Network Resilience

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An Entity Alignment Algorithm Based on Ontology Enhancement and Multi-Feature Fusion 6

An Entity Alignment Algorithm Based on Ontology Enhancement ...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Song, Hao Lei, Yuxia Zi, Kangli Quan, Fuyuan Zhang, Tianhang Li, Qi School of Computer Science Qufu Normal University Shandong Rizhao China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Entity alignment plays a crucial role in the integration of knowledge graphs. However, current knowledge graphs often suffer from inconsistent construction standards, structural heterogeneity, and imprecise semantic representation of entities, which can lead to suboptimal embedding of structural information. Moreover, most existing entity alignment methods rely heavily on structural information, which limits alignment efficiency. To address these challenges, this paper presents Onto-IMF, an ontology-enhanced entity alignment method based on multi-feature fusion. This approach aims to enhance the semantic representation of entities by incorporating ontology information, thereby reducing structural discrepancies among heterogeneous graphs and improving the embedding of structural information. The multi-feature fusion entity alignment leverages diverse semantic features, including relations, attributes, and entity names, to achieve more accurate alignment. Experimental results on various DBP15K subsets indicate that the proposed method achieves Hits@l scores of 86.6%, 93.0%, and 97.7%, respectively, representing an average improvement of 7.9% over the best-performing baseline models. © 2024 IEEE.

关键词： Knowledge graph

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Multi-frame Temporal Modeling for Multi-object Tracking in Satellite Video 2

Multi-frame Temporal Modeling for Multi-object Tracking in S...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Chai, Bingqian Qiao, Tingting Xie, Baorong Wang, Jue Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Shanghai Aerospace Electronic Technology Institute Shanghai China

ISBN: (纸本)9798331515669

With the advancement of remote sensing technology, video satellites have played an increasingly important role in both military and civilian domains, and they are widely used in areas such as military guidance, intelligent transportation, and disaster response. Multi-object tracking (MOT) in satellite video is a crucial research topic in these fields and has made certain progress. However, due to the difficulty of tracking objects in complex scene and low resolution, current MOT methods face issues of low accuracy in satellite video. In this work, we propose a joint-detection-and-tracking method called multi-frame temporal MOT (MFT-MOT). Firstly, a heatmap guided multi-frame temporal modeling method is introduced to extract features with temporal semantic information while suppressing background noise. Then, we utilize a weights-shared encoder-decoder network to facilitate interaction of temporal and spatial information and capture inter-frame object motion tracks at low resolution. Experiments on SAT-MTB dataset shows the effectiveness of our method. © 2024 IEEE.

关键词： Object tracking

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Research on Weld Detection Based on Weighted Feature Fusion Network 23

Research on Weld Detection Based on Weighted Feature Fusion ...

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15th International Conference on Digital Image processing, ICDIP 2023

作者： Wang, Mengxue Ma, Xing Mu, Chunyang Hou, Bing College of Electronic and Information Engineering North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing North Minzu University Yinchuan China

ISBN: (纸本)9798400708237

Weld seam grinding is a crucial process for welding cast products. Not only are the polished welds more attractive and durable, but they also have superior stress effects. However, the workplace is harsh and can significantly impact health. The first task for automated weld grinding is weld detection. This paper, a pyramid feature fusion network (YOLOv5-BiCA) learning model with embedded coordinate attention as a weighting condition that fully utilizes the shallow and deep network information. An online data augmentation strategy employing Mosaic+Mixup is proposed to address the problem of insufficient sample size. We use Focal Loss to enhance the classification function and increase the positive influence of positive samples on the loss function during the training process to address the problem of an imbalance between positive and negative sample. Our proposed YOLOv5-BiCA performs better in weld detection, mAP improves by 3.5%, and recall improves by 7.97%, according to experimental results based on homegrown datasets. © 2023 ACM.

关键词： Welds

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MTL-SRN: Multi-task Learning-based Signal Recognition Network

MTL-SRN: Multi-task Learning-based Signal Recognition Networ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Pengxu Zhang, Xingjian Ma, Yuan Zhu, Hao Jiao, Jian Zhang, Qinyu Department of Electronics and Information Engineering Shenzhen518055 China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing The College of Electronic and Information Engineering Shenzhen518060 China

ISBN: (纸本)9798350351255

Wideband signal recognition is a crucial task in the cognitive wireless communication, involving accurate classification of different signal types, modulation types, center frequencies, etc. However, most conventional approaches treat the recognition of different parameters as multiple independent tasks, and often face performance bottlenecks due to the complexity and diversity of wideband signals. To overcome these challenges, we propose a multi-task learning (MTL) network that integrates multiple tasks of signal recognition into an end-to-end model to accomplish spectrum sensing, modulation recognition, and signal classification simultaneously. By employing a shared feature extraction network and a multi-task classification header, the proposed framework effectively captures the correlations and shared information among different tasks, thereby enhancing overall recognition performance. To validate the effectiveness of the proposed scheme, we compare its performance with other state-of-the-art recognition and classification networks. Experimental results demonstrate the significant performance of the proposed MTL network in spectrum sensing, modulation recognition, and signal classification tasks. © 2024 IEEE.

关键词： Signal modulation

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LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration

LONGAGENT: Achieving Question Answering for 128k-Token-Long ...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhao, Jun Zu, Can Xu, Hao Lu, Yi He, Wei Ding, Yiwen Gui, Tao Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Institute of Modern Languages and Linguistics Fudan University China

ISBN: (纸本)9798891761643

Large language models (LLMs) have achieved tremendous success in understanding language and processing text. However, question-answering (QA) on lengthy documents faces challenges of resource constraints and a high propensity for errors, even for the most advanced models such as GPT-4 and Claude2. In this paper, we introduce LONGAGENT, a multi-agent collaboration method that enables efficient and effective QA over 128k-token-long documents. LONGAGENT adopts a divide- and-conquer strategy, breaking down lengthy documents into shorter, more manageable text chunks. A leader agent comprehends the user's query and organizes the member agents to read their assigned chunks, reasoning a final answer through multiple rounds of discussion. Due to members' hallucinations, it's difficult to guarantee that every response provided by each member is accurate. To address this, we develop an inter-member communication mechanism that facilitates information sharing, allowing for the detection and mitigation of hallucinatory responses. Experimental results show that a LLaMA-2 7B driven by LONGAGENT can effectively support QA over 128k-token documents, achieving 16.42% and 1.63% accuracy gains over GPT-4 on single-hop and multi-hop QA settings, respectively. © 2024 Association for Computational Linguistics.

关键词： Question answering

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Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation

Inference-Time Decontamination: Reusing Leaked Benchmarks fo...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhu, Qin Cheng, Qinyuan Peng, Runyu Li, Xiaonan Liu, Tengxiao Peng, Ru Qiu, Xipeng Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9798891761681

The training process of large language models (LLMs) often involves varying degrees of test data contamination (Yang et al., 2023b).Although current LLMs are achieving increasingly better performance on various benchmarks, their performance in practical applications does not always match their benchmark *** of benchmarks can prevent the accurate assessment of LLMs' true ***, constructing new benchmarks is costly, labor-intensive and still carries the risk of ***, in this paper, we ask the question "Can we reuse these leaked benchmarks for LLM evaluation?" We propose Inference-Time Decontamination (ITD) to address this issue by detecting and rewriting leaked samples without altering their *** can mitigate performance inflation caused by memorizing leaked *** proof-of-concept experiments demonstrate that ITD reduces inflated accuracy by 22.9% on GSM8K and 19.0% on *** MMLU, using Inference-time Decontamination can lead to a decrease in the results of Phi3 and Mistral by 6.7% and 3.6% *** hope that ITD can provide more truthful evaluation results for large language models. © 2024 Association for Computational Linguistics.

关键词： Decontamination

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Efficient phase-induced gabor cube selection and weighted fusion for hyperspectral image classification

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Science China(Technological Sciences) 2022年第4期65卷 778-792页

作者： CAI RunLin LIU ChenYing LI Jun Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and PlanningSun Yat-sen UniversityGuangzhou 510275China Hubei Key Laboratory of Intelligent Geo-Information Processing School of Computer ScienceChina University of GeosciencesWuhan 430078China

Spectral-spatial Gabor filtering(GF),a robust feature extraction tool,has been widely investigated for hyperspectral image(HSI)***,a new type of GF method,named phase-induced GF,which showed great potential for HSI feature extraction,was *** this new type of GF possibly better explores the frequency characteristics of HSIs,with a new parameter added,it generates a much larger amount of features,yielding redundancies and noises,and is therefore risky to severely deteriorate the efficiency and accuracy of *** tackle this problem,we fully exploit phase-induced Gabor features efficiently,proposing an efficient phase-induced Gabor cube selection and weighted fusion(EPCS-WF)method for HSI ***,to eliminate the redundancies and noises,we first select the most representative Gabor cubes using a newly designed energy-based phase-induced Gabor cube selection(EPCS)algorithm before feeding them into ***,a weighted fusion(WF)strategy is adopted to integrate the mutual information residing in different feature cubes to generate the final *** experimental results obtained on four well-known HSI datasets demonstrate that the EPCS-WF method,while only adopting four selected Gabor cubes for classification,delivers better performance as compared with other Gabor-based *** code of this work is available at https://***/cairlin5/EPCS-WF-hyperspectral-image-classification for the sake of reproducibility.

关键词： hyperspectral image(HSI)classification Gabor filtering(GF) phase offset feature selection weighted fusion(WF)

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A method for generating Dunhuang dance instrumental images based on optimized diffusion model

A method for generating Dunhuang dance instrumental images b...

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2024 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Wang, Jiaxin He, Xiangzhen Suzuki, Kenneth M. Northwest Minzu University Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Lanzhou730030 China Northwest Minzu University Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Lanzhou730030 China

ISBN: (纸本)9781510685840

Dunhuang culture is a treasure house of human history and culture. As an integral part of Dunhuang culture, Dunhuang dance is famous for its unique national characteristics and artistic characteristics. In the form of Dunhuang dance, instrumental music and props are used to greatly enrich the stage expression, forming a unique dance form of "holding music while dancing, dancing with instruments". However, the phenomenon of homogenization of instrumental props occurs frequently, which hinders the overall presentation effect and beauty of Dunhuang dance. The development based on the generative diffusion model provides new ideas for the design of Dunhuang dance instrumental props. First, the role played by musical instruments and props in the presentation of Dunhuang dance art was sorted out, and the Dunhuang dance instrumental props were digitally collected and processed, and a special data set was constructed. Secondly, based on the recognition of related instruments, a diffusion model instrumental prop image generation that can be used in the Chinese context is trained. Experimental results show that the instrumental props generated by this method not only conform to realistic logic but also have strong applicability. © 2024 SPIE.

关键词： Photons

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