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检索条件"任意字段=Conference on empirical methods in natural language processing"

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Research on Maintenance Strategy Generation of Substation Equipment Based on Large Model Retrieval Enhancement Technology

Research on Maintenance Strategy Generation of Substation Eq...

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Smart Grid and Artificial Intelligence (SGAI), International conference on

作者： Xiaodong Wang Linyu Zhang Feng Zhao Jingpo Bai Xiaoguang Huang Xiaohang Zhang Min Jin Dongmei Cui State Grid Information and Communication Industry Group Beijing China Beijing Sgitg Accenture Information Technology Center. Co. Ltd Beijing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

With the continuous expansion of power grid scale and the increasing complexity of power equipment, substation equipment maintenance decision-making faces challenges such as big data challenges, information complexity, and high decision-making barriers. Traditional maintenance strategy generation methods mainly rely on human expertise and domain knowledge, which are not only inefficient but also easily affected by human factors, leading to inaccurate and delayed decision-making. In recent years, with the rapid development of natural language processing (NLP) technology, especially the emergence of large language models (LLMs), a innovative methodology for the automatic generation of substation equipment maintenance strategies has been provided. This study presents a novel methodology for generating substation maintenance strategies through retrieval-augmented large language model technology By constructing a retrieval-augmented knowledge repository containing a vast amount of historical maintenance cases and professional knowledge, and utilizing the generation capability of large language models, the method achieves rapid and accurate diagnosis of substation equipment faults and automatic generation of maintenance strategies. empirical evaluations demonstrate that this method can significantly improve the efficiency and accuracy of maintenance strategy generation, providing strong technical support for the stable operation of the power system.

关键词： Substations Accuracy Large language models Knowledge based systems Decision making natural language processing Maintenance Complexity theory Tuning Videos

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Trends in Bio-Based 3D Concrete Printing: An NLP-Driven Analysis 4th

Trends in Bio-Based 3D Concrete Printing: An NLP-Driven Anal...

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4th International conference on Sustainable Development in Civil, Urban and Transportation Engineering, CUTE 2024

作者： Rajczakowska, Magdalena Department of Civil Environmental and Natural Resources Engineering Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9789819793990

The EU Bioeconomy Strategy underscores the importance of fostering a shared understanding of the shift to a bioeconomy and raising awareness of the diverse biomass demand. Since concrete is a globally prevalent material with a substantial environmental impact, it presents an enticing opportunity to integrate biomass products. Particularly, in the context of the burgeoning interest in digital concrete manufacturing, the use of bio-based cementitious "ink" could offer a promising resolution to the environmental challenges faced by the construction sector. This paper examines the current trends in substituting traditional concrete components with bio-based materials, such as bio-binders and bio-admixtures, for use in 3D printing technologies. natural language processing (NLP) methods are employed to extract features from a substantial number of published documents automatically. This process aids in identifying the most frequently occurring bio-based ingredients and their biomass sources. Latent Dirichlet Allocation (LDA) is utilized to perform topic modeling and unveil underlying patterns within the corpus. A comprehensive discussion of both current and potential future developments is conducted. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bioeconomy

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Leveraging AI to Ensure Authenticity in Student Assignments: A Knowledge-Based Validation and Evaluation Framework 5

Leveraging AI to Ensure Authenticity in Student Assignments:...

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5th International conference on Advanced Research in Computing, ICARC 2025

作者： Hondarangala, Chinthaka Wickramaarachchi, Dilani University of Kelaniya Department of Industrial Management Sri Lanka

ISBN: (纸本)9798331530983

This research explores the development of an AI-driven assessment system designed to evaluate student knowledge while minimizing cheating in educational settings. The system operates in two distinct phases. In Phase 1, AI-generated, contextually relevant questions based on student-submitted assignments assess whether the student has genuinely engaged with the content or used external tools, such as generative AI, without proper understanding. In Phase 2, the system delves deeper into conceptual understanding by aligning submissions with course-specific materials, evaluating relevance, depth, and originality. A fine-tuned large language model (LLM), trained on domain-specific data, enables precise evaluation against academic standards. Results indicate that students demonstrated marked improvements in understanding and performance, validating the effectiveness of the two-phase model. This model encouraged deeper engagement and comprehension, increasing students' knowledge of the material. By addressing the shortcomings of traditional assessment methods, this research introduces a model that reduces the likelihood of students blindly submitting assignments without truly understanding the content. © 2025 IEEE.

关键词： natural language processing systems

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The Reliability Paradox: Exploring How Shortcut Learning Undermines language Model Calibration 58

The Reliability Paradox: Exploring How Shortcut Learning Und...

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58th Hawaii International conference on System Sciences, HICSS 2025

作者： Bihani, Geetanjali Rayz, Julia Purdue University United States

ISBN: (纸本)9780998133188

The advent of pre-trained language models (PLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found PLMs to suffer from miscalibration, indicating a lack of accuracy in the confidence estimates provided by these models. Current evaluation methods for PLM calibration often assume that lower calibration error estimates indicate more reliable predictions. However, fine-tuned PLMs often resort to shortcuts, leading to overconfident predictions that create the illusion of enhanced performance but lack generalizability in their decision rules. The relationship between PLM reliability, as measured by calibration error, and shortcut learning, has not been thoroughly explored thus far. This paper aims to investigate this relationship, studying whether lower calibration error implies reliable decision rules for a language model. Our findings reveal that models with seemingly superior calibration portray higher levels of non-generalizable decision rules. This challenges the prevailing notion that well-calibrated models are inherently reliable. Our study highlights the need to bridge the current gap between language model calibration and generalization objectives, urging the development of comprehensive frameworks to achieve truly robust and reliable language models. © 2025 IEEE Computer Society. All rights reserved.

关键词： calibration generalization Pretrained language models robustness shortcut learning

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Research on the Improvement of Human-Machine Collaborative Machine Translation Model Based on Internet 14th

Research on the Improvement of Human-Machine Collaborative M...

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14th International conference on Frontier Computing, FC 2024

作者： Sun, Wei Yao, Man Foreign Language School Anhui Xinhua University Anhui Hefei China

ISBN: (纸本)9789819627936

Machine translation is a key study subject in computer science that has drawn interest from a wide range of academic disciplines, including linguistics, philosophy, psychology, physics, and engineering. Its journey over the last seventy years has been from the limelight to the back burner to the vanguard, then back to the forefront, demonstrating the use of modern research techniques and its continued importance in commerce and academics. The two basic strategies in machine translation are rule-based and corpus-based methods, and hybrid models that combine the two have grown in popularity over the last decade. The development of corpus-based methods that take advantage of breakthroughs in computational linguistics and natural language processing has enabled the emergence of niche machine translation solutions that are well-suited to specific fields and market segments. In this paper, we introduce a new, collaborative human-machine translation model based on an Internet scenario that differs from traditional methods to MT. Unlike the traditional MT model, in which all learning is automatically machine-driven, the suggested technique mixes human review with C-value methods to improve translation quality. In other words, this innovative approach integrates human experience with all stages of the MT process to restore machine performance to levels comparable to those of human translators. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine translation

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15th International conference on Software Business, ICSOB 2024

15th International Conference on Software Business, ICSOB 20...

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15th International conference on Software Business, ICSOB 2024

ISBN: (纸本)9783031858482

The proceedings contain 36 papers. The special focus in this conference is on Software Business. The topics include: Digital Sovereignty: Affordances in Open Source Projects;The Ethical Landscape in Public Procurement of ICT Systems;Exploring Classification Consistency of natural language Requirements Using GPT-4o;unveiling the Skills and Responsibilities of Serverless Practitioners: An empirical Investigation;on the Way to the Best Information System of the Future – Reflections on Scenarios in Favor of Good Technology Development;what Affects Developer Experience in Software Platforms?;enabling Inter-organizational Data Sharing: Towards a Method for Assessing Data Assets;Fairness in AI Systems Development: Beyond EU AI Act Compliance;The EU AI Act is a Good Start But Falls Short;Professionals’ Opinions on the Use of AI Software for HRM;Emotion AI in Workplace Environments: A Case Study;exploring the Factors that Impact the Half-Life of Software;exploring Diversity-Driven Power Dynamics in Software Engineering Environments: Insights from a Qualitative Study;Towards Sustainable ICT Solutions: Analyzing Call for Tender Documents;exploring Perceptions of Blockchain in Cross-Border Workforce Mobility;Experience of Gender Among Professionals in Finnish IT Companies;secondary Use of Health Data: Centralized Structure and Information Security Frameworks in Finland;Sustainable ICT Procurement: Data-Driven Decision-Making in B2B Green ICT Adoption;integrating Sustainability into Scrum Agile Software Development: An Action Research Approach;optimizing for Sustainability: Product Ops and Software Waste Reduction;designing for Sustainability When Architecture Standards are Involved: An Industrial Case Study;challenges in Incorporating Sustainability Practices in the Software Lifecycle;pre-release Experimentation in Indie Game Development: An Interview Survey;marketplace for Multi-party Development of Artificial Intelligence Systems: Perceptions on Value Creation;Data-Limited Co

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Application and Development of natural language processing Algorithm System in Corpus Linguistics

Application and Development of Natural Language Processing A...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： Yingying Wei Department of Human Resources Wanjiang University of Technology Maanshan China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In the digital age, the explosive growth of information has led to significant challenges such as the burden of reading long texts, cross-language communication barriers, and information overload. To address these issues, this paper investigates the application and development of natural language processing (NLP) algorithm systems in corpus linguistics. We employ two methods for automatic summary generation-extractive and generative-which identify key sentences or phrases and utilize Seq2Seq models to produce natural and fluent summaries. For machine translation, we build a multi-language parallel corpus and train a neural machine translation (NMT) model using statistical techniques and the Transformer architecture with attention mechanisms. This approach enhances translation accuracy and fluency. Additionally, we explore summarization technology to combat information overload, improving summary relevance and accuracy through multi-task learning and generative adversarial networks (GANs). Our Re3Sum model further guides text summary generation using real summaries as soft templates. That the Transformer-based model significantly outperforms traditional methods in accuracy, fluency, and information retention, achieving a maximum information retention score of 88.76 points. This reduces the burden of reading long texts and enhances cross-language communication efficiency. Overall, this study not only offers new research directions for the NLP field but also provides practical solutions to language processing challenges.

关键词： Integrated circuits Accuracy Translation Neural machine translation Linguistics Transformers Multitasking Information age Generative adversarial networks natural language processing

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InVideo Search: Scene Description Clustering and Integrating Image and Audio Captioning for Enhanced Video Search 21st

InVideo Search: Scene Description Clustering and Integrating...

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21st International conference on Distributed Computing and Intelligent Technology

作者： Khan, Almira Asif Muhammed Shaji, Asher Mathews Sujith, Devika Nath, Aneesh G. Udmale, Sandeep S. TKM Coll Engn Dept Comp Sci & Engn Kollam Kerala India Veermata Jijabai Technol Inst VJTI Dept Comp Engn & Informat Technol Mumbai 400019 Maharashtra India

ISBN: (纸本)9783031814037;9783031814044

In the digital era, the vast amount of long videos highlights the need for an advanced system to efficiently find specific segments based on text descriptions. The proposed solution aims to develop a video content retrieval system by utilizing established tools in video analysis, image/video captioning, and information retrieval. The work is organized into three modules: Keyframe extraction, Captioning, and Query-based searching. Our primary focus is on effectively identifying key elements within these videos, achieving a semantic understanding of these videos in the form of captions, and establishing precise matches of user queries. In short, users input descriptive text prompts, and the application, equipped with pre-trained models, promptly and accurately identifies and retrieves the video segments. This application carries significant potential across various domains, from enabling efficient scene location in movies to helping extract knowledge from educational lectures. Furthermore, this research fundamentally departs from conventional video search methods that heavily rely on metadata or manually assigned tags, which often fall short of capturing the nuances of video content. This work also focuses on a multimodal captioning strategy to integrate more semantic content. Clustering of captions was also incorporated to improve search performance in the case of long videos. This approach allows us to deliver a robust and efficient solution, taking advantage of the extensive knowledge and research underlying these established technologies. Beyond the immediate applications, this research has broader applications in fields like natural language understanding, multimedia analysis, and information retrieval.

关键词： Clustering Keyframe extraction natural language processing Video content retrieval system

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Universal Low-Resource Speech Synthesis Via Phoneme Fusion Coordinating Low-Rank Decomposition

Universal Low-Resource Speech Synthesis Via Phoneme Fusion C...

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

作者： Li, Yanliang Yu, Zhengtao Wang, Linqin Gao, Shengxiang Dong, Ling Wang, Wenjun Info. Engineering and Automation Yunnan Key Lab. of AI Kunming University of Tech. and Sci. Kunming China

ISBN: (纸本)9798350368741

Recent advancements in end-to-end text-to-speech models have made significant progress. However, these approaches based on high-resource languages, are inapplicable for low-resource languages, and existing low-resource speech synthesis methods are typically specific to single languages. Consequently, the pursuit of a universal methodology for low-resource speech synthesis emerges as a critical problem that requires immediate attention. Unlike previous works, we propose a novel and universal approach for multiple low-resource languages by leveraging phoneme fusion coordinating low-rank decomposition in a pre-trained multilingual model. Specifically, by establishing a multilingual phoneme dictionary through phoneme fusion and applying parameter freezing and matrix decomposition techniques for fine-tuning, the proposed method was extensively evaluated across four different languages, each utilizing approximately 3 hours of speech data. The experimental results demonstrate that the quality of the synthesized speech surpasses current mainstream methods for low-resource speech synthesis, achieving outcomes comparable to those trained with tens of hours of data. Audio samples are available at https://***/***. © 2025 IEEE.

关键词： low-resource language matrix decomposition phoneme fusion

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Mitigating Hallucination in Visual-language Models via Re-balancing Contrastive Decoding 7th

Mitigating Hallucination in Visual-Language Models via Re-ba...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Liang, Xiaoyu Yu, Jiayuan Mu, Lianrui Zhuang, Jiedong Hu, Jiaqi Yang, Yuchen Ye, Jiangnan Lu, Lu Chen, Jian Hu, Haoji Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819786190;9789819786206

Although Visual-language Models (VLMs) have shown impressive capabilities in tasks like visual question answering and image captioning, they still struggle with hallucinations. Analysis of attention distribution in these models shows that VLMs tend to processing textual tokens rather than visual tokens. This imbalance of attention distribution causes VLMs to favor textual knowledge in the case of multimodal knowledge conflicts, resulting in differences from the image information. In this paper, we propose Re-Balancing Contrastive Decoding (RBD) method, which employs textual and visual branches to recalibrate attention distribution in VLMs. Specifically, the textual branch injects image noise to stimulate the model's dependency on text, thereby reducing textual bias. Concurrently, the visual branch focuses on the selection of significant tokens, refining the attention mechanism to highlight the primary subject. This dual-branch strategy enables the RBD method to diminish textual bias while enhancing visual information. Experimental results demonstrate that our method, RBD, outperforms the existing methods by the CHAIR and POPE metrics, mitigate hallucinations without reducing the model's general capabilities.

关键词： Multimodal Contrastive Decoding Hallucination

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