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

作者： Huang, Wenhao He, Qianyu Li, Zhixu Liang, Jiaqing Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Data Science Fudan University China

ISBN: (纸本)9798891761681

Definition bias is a negative phenomenon that can mislead models. Definition bias in information extraction appears not only across datasets from different domains but also within datasets sharing the same domain. We identify two types of definition bias in IE: bias among information extraction datasets and bias between information extraction datasets and instruction tuning datasets. To systematically investigate definition bias, we conduct three probing experiments to quantitatively analyze it and discover the limitations of unified information extraction and large language models in solving definition bias. To mitigate definition bias in information extraction, we propose a multi-stage framework consisting of definition bias measurement, bias-aware fine-tuning, and task-specific bias mitigation. Experimental results demonstrate the effectiveness of our framework in addressing definition bias. © 2024 Association for Computational Linguistics.

关键词： Information retrieval

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Federated learning-outcome prediction with multi-layer privacy protection

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Frontiers of computer science 2024年第6期18卷 205-214页

作者： Yupei ZHANG Yuxin LI Yifei WANG Shuangshuang WEI Yunan XU Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China MIIT Lab of Big Data Storage and Management Xi’an 710129China

Learning-outcome prediction(LOP)is a long-standing and critical problem in educational *** studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection *** this end,this study proposes a distributed grade prediction model,dubbed FecMap,by exploiting the federated learning(FL)framework that preserves the private data of local clients and communicates with others through a global generalized *** considers local subspace learning(LSL),which explicitly learns the local features against the global features,and multi-layer privacy protection(MPP),which hierarchically protects the private features,including model-shareable features and not-allowably shared features,to achieve client-specific classifiers of high performance on LOP per *** is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part,a local part,and a classification head in clients and averaging the global parts from clients on the *** evaluate the FecMap model,we collected three higher-educational datasets of student academic records from engineering *** results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP,compared with the state-of-the-art *** study makes a fresh attempt at the use of federated learning in the learning-analytical task,potentially paving the way to facilitating personalized education with privacy protection.

关键词： federated learning local subspace learning hierarchical privacy protection learning outcome prediction privacy-protected representation learning

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Classifying Argument Component using Deep Learning on English dataset

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Informatica (Slovenia) 2023年第5期47卷 63-68页

作者： Gunawan, William Suhartono, Derwin Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The study focuses on the argument component in argumentation mining, specifically examining claim and premise types. Various datasets exist for argumentation components, each with different classes. The study evaluates the performance of deep learning architectures, particularly using contextual embedding as the initial layer. Six datasets with diverse argument components are used for validation. The research provides a comprehensive comparison of deep learning architectures, combining multiple layers such as BERT or word embedding with LSTM, GRU, or CNN. The results and their implications are discussed in the concluding section of the journal. The study demonstrates significant results with the BERT-BiGRU-CRF architecture after conducting several experiments. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Embeddings

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Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works

Evaluating Character Understanding of Large Language Models ...

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

作者： Yuan, Xinfeng Yuan, Siyu Cui, Yuhan Lin, Tianhe Wang, Xintao Xu, Rui Chen, Jiangjie Yang, Deqing School of Data Science Fudan University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

ISBN: (纸本)9798891761643

Large language models (LLMs) have demonstrated impressive performance and spurred numerous AI applications, in which role-playing agents (RPAs) are particularly popular, especially for fictional characters. The prerequisite for these RPAs lies in the capability of LLMs to understand characters from fictional works. Previous efforts have evaluated this capability via basic classification tasks or characteristic imitation, failing to capture the nuanced character understanding with LLMs. In this paper, we propose evaluating LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development. Specifically, we construct the CROSS dataset from literature experts and assess the generated profiles by comparing them with ground truth references and evaluating their applicability in downstream tasks. Our experiments, which cover various summarization methods and LLMs, have yielded promising results. These results strongly validate the character understanding capability of LLMs. Resources are available at https://***/Joanna0123/character_profiling. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Boosting Text-to-SQL through Multi-grained Error Identification 31

Boosting Text-to-SQL through Multi-grained Error Identificat...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Bo Li, Shufei Jing, Hongyu Du, Ming Song, Hui Wang, Hongya Xiao, Yanghua School of Computer Science and Technology Donghua University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

ISBN: (纸本)9798891761964

Text-to-SQL is a technology that converts natural language questions into executable SQL queries, allowing users to query and manage relational databases more easily. In recent years, large language models have significantly advanced the development of text-to-SQL. However, existing methods often overlook validation of the generated results during the SQL generation process. Current error identification methods are mainly divided into self-correction approaches based on large models and feedback methods based on SQL execution, both of which have limitations. We categorize SQL errors into three main types: system errors, skeleton errors, and value errors, and propose a multi-grained error identification method. Experimental results demonstrate that this method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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CBIR: a novel identification approach for college students in need based on consumer behavior psychology theory

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Neural Computing and Applications 2025年第6期37卷 4663-4677页

作者： Liu, Xinze Liu, Shixi Hu, Xiaojing Zhang, Yudong Fang, Xianwen School of Computer and Information Engineering Chuzhou University Chuzhou239000 China School of Computer Science and Engineering Southeast University Nanjing210096 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying students in need rely on manual operations that include manually extracting consumption behavior information, statistical consumption characteristics and principal component analysis. However, this issue may lead to low prediction accuracy and inefficiency in identifying students in need. We design a three-stage framework to accurately identify college students in need from the perspective of consumer behavior psychology. The consumption behavior information is first obtained from the student consumption records using the consumption behavior clustering approach. The consumption behavior matrix is then built by extracting consumption and spatiotemporal information in different periods. Finally, a novel consumption behavior identification ResNeSt (CBIR) model is proposed to identify college students in need accurately. The experimental results on real datasets show that the CBIR model has higher prediction accuracy than the baseline models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Social psychology

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Enhancing Multimodal Named Entity Recognition through Adaptive Mixup Image Augmentation 31

Enhancing Multimodal Named Entity Recognition through Adapti...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Bo Jiang, Haiqi Wei, Jie Jing, Hongyu Du, Ming Song, Hui Wang, Hongya Xiao, Yanghua School of Computer Science and Technology Donghua University China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

ISBN: (纸本)9798891761964

Multimodal named entity recognition (MNER) extends traditional named entity recognition (NER) by integrating visual and textual information. However, current methods still face significant challenges due to the text-image mismatch problem. Recent advancements in text-to-image synthesis provide promising solutions, as synthesized images can introduce additional visual context to enhance MNER model performance. To fully leverage the benefits of both original and synthesized images, we propose an adaptive mixup image augmentation method. This method generates augmented images by determining the mixing ratio based on the matching score between the text and image, utilizing a triplet loss-based Gaussian Mixture Model (TL-GMM). Our approach is highly adaptable and can be seamlessly integrated into existing MNER models. Extensive experiments demonstrate consistent performance improvements, and detailed ablation studies and case studies confirm the effectiveness of our method. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Safety-certified Receding-horizon Motion Planning and Containment Control of Autonomous Surface Vehicles via Neurodynamic Optimization

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Lyu, Guanghao Peng, Zhouhua Wang, Dan Wang, Jun School of Marine Electrical Engineering Dalian Maritime University Dalian China Department of Computer Science and the School of Data Science City University of Hong Kong Hong Kong

This paper addresses the safety-certified motion planning and containment control of under-actuated autonomous surface vehicles subject to model uncertainties, external disturbances, and input constraints in the presence of stationary and moving obstacles. A three-level modular control architecture is proposed with a trajectory generation module at its planning level, an adaptive guidance module at its guidance level, and a kinetic control module at its control level. Specifically, at the planning level, a safety-certified containment trajectory generator is designed to generate safe trajectories over a rolling time window to achieve containment formation and collision avoidance with neighboring ASVs, stationary obstacles, and moving obstacles via dynamic control barrier functions and twotimescale neurodynamic optimization models. At the guidance level, an adaptive line-of-sight guidance law is developed based on a finite-time predictor to estimate unknown sideslip angles and generate guidance commands. At the control level, an optimal control law is designed based on finite-time neural predictors and control Lyapunov functions for the autonomous surface vehicle with input constraints to follow the desired guidance commands. The effectiveness and characteristics of the proposed method are demonstrated via simulations and hardware-in-theloop experiments for cooperative exploration. IEEE

关键词： Optimization

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Effectiveness of a touch-based hand therapeutic application for hand dexterity in children with handwriting difficulties-a pilot study

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Multimedia Tools and Applications 2024年 1-35页

作者： Smitha, John Renumol, V.G. School of Computer Science and Technology Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India School of Engineering Cochin University of Science and Technology Kochi India

Handwriting is an important skill for children during their academic years. It is the coordination of perceptual-motor and cognitive abilities. Some children have difficulties in handwriting, which is an indication of issues in their fine motor skills. Nowadays, technology-based instructions are available to develop fine motor skills in children with handwriting disabilities. This paper describes the details of a newly developed software application namely ‘HanDex’ for children with handwriting difficulties and the experiments conducted to test its effectiveness. The application comprises six hand therapeutic activities which were designed based on the oscillation theory of handwriting. Initially, in the first phase, the experiments were conducted in nine primary school children from various schools, aged 8–12 years. Then, in the second phase, the software training was given to six upper primary school children (10–12 years) with handwriting issues. The results of using this application were promising. So one more study was conducted in another set of nine children (8–11 years) with handwriting difficulties. The participants were assessed by their special educators, class teachers and parents. After the pretest, they received a total of 18 handwriting practice sessions using this new application. At the end of the training sessions, a post test was conducted, to measure the progress in handwriting. The handwriting rubrics with eight handwriting parameters such as letter formation, placement, letter sizing, spacing of letters, legibility, speed, neatness, and spacing of words were used for handwriting assessment. The findings from the study suggested that touch-based hand therapeutic activities were beneficial for children with writing difficulties to improve their handwriting performance and tripod grip. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Children with handwriting difficulties Hand Dexterity Handwriting performance and tripod grip Tablet computer ‘HanDex’ Software

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