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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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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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Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6293-6314页

作者： Asheralieva, Alia Niyato, Dusit Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering 1088 Xueyuan Avenue Shenzhen China Nanyang Technological University College of Computing and Data Science 50 Nanyang Avenue Singapore639798 Singapore

We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We consider the following critical DFL challenges: i) scarce bandwidth resources of ENs;ii) dynamic, heterogeneous edge environment;iii) incentive provisioning and complex tradeoffs between the DFL performance and training costs. To resolve these challenges, we develop a new model compression method where ENs utilize dynamic, non-identical compression rates to improve the communication efficiency of DFL under time-varying, heterogeneous resource constraints. We show that our method can be formulated as a graphical Markov potential game where ENs act as players deciding on their compression factors and the number of data samples used for model updates. Each EN is incentivized to participate in DFL through rewards based on the EN's contribution to training. We prove that our game has a dominant pure-strategy Nash equilibrium (NE) maximizing its potential function and propose a dynamic distributed compression algorithm in which each EN can find its dominant strategy independently. We show that this algorithm converges to the Pareto-optimal NE, representing the most efficient solution of our game enhancing the DFL performance with minimal costs. © 2002-2012 IEEE.

关键词： Federated learning

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Towards N-version Quantum Software Systems for Reliable Classical-Quantum Computing 35

Towards N-version Quantum Software Systems for Reliable Clas...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Saito, Shinobu Endo, Suguru Suzuki, Yasunari Ntt Computer and Data Science Laboratories Tokyo Japan

ISBN: (纸本)9798350367041

While quantum computers have attracted much attention, dealing with computational errors due to noise effects caused by the interaction between quantum hardware and the external environment is a significant challenge. In this paper, we propose an approach to apply N-version programming (NVP) to quantum software to improve the reliability of the entire quantum software system. First, we define architecture patterns for N-version quantum software systems (NVQS) based on a combination of quantum libraries and devices. Next, given that the output of the quantum software system is a probability distribution, we came up with an evaluation strategy inspired by the concept of NVP for clustering and selecting multiple probability distributions output from NVQS. Finally, we outline our future experimental plan. © 2024 IEEE.

关键词： Quantum computers

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Einocchio: Efficiently Outsourcing Polynomial Computation with Verifiable Computation and Optimized Newton Interpolation

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1981-1992页

作者： Pei, Xintao Chen, Yuling Long, Yangyang Sang, Haiwei Luo, Yun Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

Cloud computing, as a promising service platform, has gained significant popularity in addressing emerging data privacy issues in applications such as machine learning and data mining. Researchers have proposed the verifiable computing that allows the cloud users to delegate their computation tasks to the cloud server. Then, the cloud server computes the cryptographic proofs that verify the correctness of the results, a process that is generally faster ompared to local manual computation. However, performing computation tasks or verifying the correctness of encrypted data, such as multivariate polynomial functions, remains a significant challenge. To solve this problem, we propose Einocchio: A verifiable computation scheme that combines the efficient Pinocchio system with homomorphic encryption, which allows the public verification of the computational results on the server side while ensuring data confidentiality and the results. Compared with the existing solutions, Einocchio does not reveal the client's input. Furthermore, we extrapolate Einocchio by optimizing the Pinocchio's quadratic arithmetic program component using a differential optimization method, which reduces the computational workload owing to the conversion from quadratic to linear complexity, thereby increasing the efficiency of the quadratic arithmetic program preprocessing stage. Security analysis demonstrates that Einocchio achieves IND-CPA security. Finally, the performance evaluation confirmed its effectiveness and suitability for cloud computing environments. Compared to the corresponding scheme based on Newton interpolation, Einocchio achieves a threefold greater computational efficiency, with the generation of interpolation polynomials for 50 data inputs occurring in a mere 0.31 ms, while simultaneously reducing the number of computations. © 2005-2012 IEEE.

关键词： Cloud platforms

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PLD-Det: plant leaf disease detection in real time using an end-to-end neural network approach based on improved YOLOv7

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Neural Computing and Applications 2024年第34期36卷 21885-21898页

作者： Mehedi, Md Humaion Kabir Nawer, Nafisa Ahmed, Shafi Khan, Md Shakiful Islam Hasib, Khan Md Mridha, M.F. Alam, Md. Golam Rabiul Nguyen, Thanh Thi Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science American International University Dhaka Bangladesh Department of Data Science and AI Monash University Wellington Rd Clayton MelbourneVIC3800 Australia

In order to maintain sustainable agriculture, it is vital to monitor plant health. Since all species of plants are prone to characteristic diseases, it necessitates regular surveillance to search for any symptoms, which is utterly challenging and time-consuming. Besides, farmers may struggle to identify the type of plant disease and its potential symptoms. Hence, the interest in research like image-based computer-aided automated plant leaf disease detection by analyzing the early symptoms has increased enormously. However, limitations in the plant leaf image database, for instance, unfitting backgrounds, blurry images, and so on, sometimes cause underprivileged feature extraction, misclassification, and overfitting issues in existing models. As a result, we have proposed a real-time plant leaf disease detection architecture incorporating proposed PLD-Det model, which is based on improved YOLOv7 with the intention of assisting farmers while reducing the issues in existing models. The architecture has been trained on the widely used PlantVillage dataset, which resulted in an accuracy of 98.53%. Furthermore, SHapley Additive exPlanations (SHAP) values have been analyzed as a unified measure of feature significance. According to the experimental findings, the proposed PLD-Det model, which is an improved YOLOv7 architecture, outperformed the original YOLOv7 model in test accuracy by approximately 4%. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Seed

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Design and Implementation of a Customizable EEG dataset Construction System 17

Design and Implementation of a Customizable EEG Dataset Cons...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Han, Liyong Liu, Jianwen Cheng, Ye Huang, Zhihua College of Computer and Data Science Fuzhou University China

ISBN: (纸本)9798331507398

Electroencephalography (EEG) plays a pivotal role in diagnosing neurological conditions, such as epilepsy and cognitive impairments. The efficacy of EEG analysis heavily relies on the quality and diversity of the underlying dataset. A significant challenge in EEG data analysis is the pronounced class imbalance, where certain neurological patterns are underrepresented, potentially skewing predictive models and diagnostic accuracy. Moreover, the absence of negative examples in EEG datasets can lead to misclassifications and limit the generalizability of findings. To address these issues, we developed a sophisticated, modular EEG dataset construction system designed to enhance the integration of both positive and negative examples, thereby mitigating class imbalance. This system is highly flexible and scalable, allowing for the tailored creation of datasets that meet specific research and clinical demands. This paper details the design, implementation, and validation of the system, demonstrating its capability to construct balanced datasets that are essential for robust EEG analysis. The results affirm the system's efficacy in fostering more accurate and comprehensive diagnostic and research outcomes in the field of neurology. © 2024 IEEE.

关键词： Electroencephalography

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Tensor-based forward-backward algorithms in the physics-informed coupled hidden markov model

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1052-1066页

作者： Zhang, Shunli Yang, Laurence T. Zhang, Yue Lu, Zhixing Cui, Zongmin School of Computer and Big Data Science Jiujiang University Jiujiang332005 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Hainan University Haikou570228 China Department of Computer Science St. Francis Xavier University AntigonishNSB2G2W5 Canada

Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance, and human-robot. In the growing field of artificial intelligence, the application of activity prediction based on the physical coupled hidden Markov model (CHMM) and tensor theory with physical properties has attracted increasing attentions. However, existing CHMMs usually only consider the time-series characteristic of data, while ignoring physical characteristics of user activity such as periodicity, timing, and correlation. Moreover, they are all matrix-based models, which could not holistically analyze the dependencies among physical states. The aforementioned disadvantages lead to lower prediction accuracy of the CHMM. To remove these disadvantages, three physics-informed tensor-based CHMMs are first constructed by incorporating prior physical knowledge. Then, the corresponding forward-backward algorithms are designed for resolving the evaluation problem of the CHMM. These algorithms could overall model multiple physical features by imposing physics and prior knowledge into the CHMM during training to improve the precision of probabilistic computing. The algorithms reduce the dependence of the model on training data by adding physical features. Finally, the comparative experiments show that our algorithms have better performances than existing prediction methods in precision and efficiency. In addition, further self-comparison experiments verify that our algorithms are effective and practical. Impact Statement-Through the analysis of users' behavior habits, consumption habits, preferences, etc., users? potential needs may be discovered. This discovery could help predict users' activities. If a waiter predicts the user's next activity. He gives her/him unexpected services to meet users' next needs. Obviously, it would significantly improve user satisfaction. In addition, connecting the front and rear products co

关键词： Hidden Markov models

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Image-Based Natural Disaster Classification with Deep Learning: A Comparative Analysis

Image-Based Natural Disaster Classification with Deep Learni...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Alamsyah, Samuel Eka Djamhari, Simen Ferdinand Sudjono, Salomo Hendrian Gunawan, Alexander A.S. Setiawan, Karli Eka Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

Natural disasters, including earthquakes, cyclones, floods, and wildfires, cause significant environmental damage and have emerged as a major global issue. These events can result in loss of life and disrupt communities and economy. Effective disaster management relies on timely and accurate information. With social media becoming a primary source of real-time information during such events, many images of these disasters are shared, though some may not be accurately labeled. Therefore, the purpose of this study is classifying natural disaster images, mainly cyclone, wildfire, flood, earthquake from social media images. This paper is to conduct a comparative analysis over 4 models namely VGG16, VGG19, EfficientNetB0, and ResNet-50. This paper uses a dataset that contains 4428 human-annotated images including cyclones, earthquakes, floods, and wildfires. Results concluded that ResNet-50 and EfficientNetB0 give the best result with both got over 96% average accuracy. ResNet-50 delivers more stable and more convergent results compared to EfficientNetB0, but it delivers slightly less classification accuracy. VGG16 and VGG19 are both outclassed in terms of accuracy, but better in training time, with VGG19 having a more convergent result. ©2024 IEEE.

关键词： Convolutional neural networks

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Interpretable Machine Learning in Leveraging Customer Information for Success Direct Marketing Campaigns 10

Interpretable Machine Learning in Leveraging Customer Inform...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Octavia Tjandra, Clarissa Shirley Cam, Grace Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

A bank's marketing campaign execution is very important. Undoubtedly, a well-crafted marketing plan will contribute to the bank's increased revenue. Marketing is crucial because it can be used to connect with consumers and inform them of the advantages of making a deposit with the bank, which will increase the number of deposits the bank receives. Thus, the goal of this research is to determine the best customer information that affects their decision on subscribing a term deposit and providing the best model for predicting the outcome of future marketing campaign. By determining the significance of each feature, this research constructed machine learning models using methods like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Logistic Regression (LR) as our research contribution. According to the research's findings, out of the three machine learning models, Random Forest (RF) has the highest accuracy scores. Additionally, the feature importance model reveals that a customer's decision to make a deposit or not is greatly influenced by factors such as the age of the customer, how much does the customer has in their bank account, did the bank have contacts with longer call durations, and did it has a positive outcomes on the previous marketing campaigns. The customers who are contacted more frequently and have subscribed before are more likely to subscribe in the future marketing campaign. © 2024 IEEE.

关键词： bank marketing classification direct marketing machine learning predictive analysis

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