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arXiv 2024年

作者： Su, Huan-Yi Wu, Ke Huang, Yu-Hao Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Recently, many works have proposed various financial large language models (FinLLMs) by pre-training from scratch or fine-tuning open-sourced LLMs on financial corpora. However, existing FinLLMs exhibit unsatisfactory performance in understanding financial text when numeric variables are involved in questions. In this paper, we propose a novel LLM, called numeric-sensitive large language model (NumLLM), for Chinese finance. We first construct a financial corpus from financial textbooks which is essential for improving numeric capability of LLMs during fine-tuning. After that, we train two individual low-rank adaptation (LoRA) modules by fine-tuning on our constructed financial corpus. One module is for adapting general-purpose LLMs to financial domain, and the other module is for enhancing the ability of NumLLM to understand financial text with numeric variables. Lastly, we merge the two LoRA modules into the foundation model to obtain NumLLM for inference. Experiments on financial question-answering benchmark show that NumLLM can boost the performance of the foundation model and can achieve the best overall performance compared to all baselines, on both numeric and non-numeric questions. Copyright © 2024, The Authors. All rights reserved.

关键词： Finance

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Improving Chinese Spelling Correction by Ranking

Improving Chinese Spelling Correction by Ranking

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International Joint Conference on Neural Networks (IJCNN)

作者： Junjia Feng Shuai Wang Wenbiao Yin Lin Shang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Chinese Spelling Check (CSC) aims to detect and correct Chinese spelling errors. Most Chinese spelling errors are the misuse of semantically, phonetically or graphically similar characters. Previous state-of-the-art works on the CSC task pursue transitions from misspelled sentences to correct sentences directly. However, it is difficult to force the current CSC methods to find the correct answer at one run. Thus, we propose a simple and effective method for CSC task by making fully use of the trained model to generate multiple candidate sentences and simply ranking to select the best, in which no additional training and parameters are required. The experimental results show that our approach outperforms previous methods and achieves the state-of-the-art performances.

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Finding Cross-rule Optimization Bugs in Datalog Engines

arXiv

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arXiv 2024年

作者： Zhang, Chi Wang, Linzhang Rigger, Manuel State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China National University of Singapore Singapore

Datalog is a popular and widely-used declarative logic programming language. Datalog engines apply many cross-rule optimizations;bugs in them can cause incorrect results. To detect such optimization bugs, we propose an automated testing approach called Incremental Rule Evaluation (IRE), which synergistically tackles the test oracle and test case generation problem. The core idea behind the test oracle is to compare the results of an optimized program and a program without cross-rule optimization;any difference indicates a bug in the Datalog engine. Our core insight is that, for an optimized, incrementally-generated Datalog program, we can evaluate all rules individually by constructing a reference program to disable the optimizations that are performed among multiple rules. Incrementally generating test cases not only allows us to apply the test oracle for every new rule generated—we also can ensure that every newly added rule generates a non-empty result with a given probability and eschew recomputing already-known facts. We implemented IRE as a tool named Deopt, and evaluated Deopt on four mature Datalog engines, namely Soufflé, CozoDB, µZ, and DDlog, and discovered a total of 30 bugs. Of these, 13 were logic bugs, while the remaining were crash and error bugs. Deopt can detect all bugs found by queryFuzz, a state-of-the-art approach. Out of the bugs identified by Deopt, queryFuzz might be unable to detect 5. Our incremental test case generation approach is efficient;for example, for test cases containing 60 rules, our incremental approach can produce 1.17× (for DDlog) to 31.02× (for Soufflé) as many valid test cases with non-empty results as the naive random method. We believe that the simplicity and the generality of the approach will lead to its wide adoption in practice. Copyright © 2024, The Authors. All rights reserved.

关键词： Engines

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Differentially Private K-Means Publishing with Distributed Dimensions

Differentially Private K-Means Publishing with Distributed D...

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International Conference on computer Supported Cooperative Work in Design

作者： Boyu Zhu Yuan Zhang Tingting Chen Sheng Zhong Computer Science and Technology Department State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Computer Science Department College of Science California State Polytechnic University Pomona USA

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In this paper, we address the critical concerns related to dataset privacy in the context of k-means clustering publishing within a distributed dimension setting. By leveraging differential privacy mechanisms, we propose a novel framework that integrates a differentially private classifier, constructed through voting based on raw clustering results, and an enhanced generative adversarial network (GAN) simulating the classifier’s behavior in inferring class labels for a public dataset. Our approach generates synthetic clustering results that mimic real outcomes in classification tasks, ensuring differential privacy and minimizing noise. Our contributions include a comprehensive exploration of privacy issues, the introduction of a novel privacy-preserving k-means clustering framework, and theoretical analyses demonstrating sensitivity and differential privacy guarantees. Evaluation on the MNIST dataset demonstrates the effectiveness of the framework, achieving 82.22% accuracy with a (10.48, 10 −9 )-differential-privacy guarantee, compared to 83.45% accuracy without privacy-preserving.

关键词： Differential privacy Privacy Accuracy Sensitivity Publishing Federated learning Computational modeling

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Contextual Structure Knowledge Transfer for Graph Neural Networks 39

Contextual Structure Knowledge Transfer for Graph Neural Net...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Zhiyuan Li, Wenzhong Yin, Zhangyue Hong, Xiaobin Xiao, Shijian Lu, Sanglu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science Fudan University China

ISBN: (纸本)157735897X

Graph transfer learning endeavors to develop a Graph Neural Network (GNN) model in a fully-labeled source domain, with the intention of deploying it on a target domain that has limited labeled data for inference. We reveal that prevalent graph transfer learning methods are susceptible to the homophily shift problem. This issue arises from the divergence in homophily structures between the source and target graphs, leading to a notable deterioration in the performance of GNN models. In this paper, we introduce a novel Contextual Structural Graph Neural Network (CS-GNN) method, leveraging a tailored attention mechanism to apprehend a variety of local structural cues, facilitating structural knowledge transfer across domains. It features an ego-network module to distill local structural diversity and a moment-based approach to gauge structural patterns without needing ground-truth labels. CS-GNN crafts a feature smoothness matrix from node attributes, guiding a customized attention mechanism for feature aggregation. A group-wise fairness loss is employed to balance learning across various structural patterns, enhancing the model’s ability to transfer knowledge across domains. Comprehensive experiments conducted on six benchmark datasets substantiate the superiority of CS-GNN over the state-of-the-art methods, demonstrating significant improvements in accuracy and robustness against homophily shifts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Approximation Designs for Energy Harvesting Relay Deployment in Wireless Sensor Networks

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Journal of computer science & technology 2022年第4期37卷 779-796页

作者： Yi Wang Yi-Xue Liu Shun-Jia Zhu Xiao-Feng Gao Chen Tian School of Modern Posts Nanjing University of Posts and TelecommunicationsNanjing 210003China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China ByteDance Inc. Shanghai 201100China Key Laboratory of Artificial Intelligence of Ministry of Education Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Energy harvesting technologies allow wireless devices to be recharged by the surrounding environment, providing wireless sensor networks (WSNs) with higher performance and longer lifetime. However, directly building a wireless sensor network with energy harvesting nodes is very costly. A compromise is upgrading existing networks with energy harvesting technologies. In this paper, we focus on prolonging the lifetime of WSNs with the help of energy harvesting relays (EHRs). EHRs are responsible for forwarding data for sensor nodes, allowing them to become terminals and thus extending their lifetime. We aim to deploy a minimum number of relays covering the whole network. As EHRs have several special properties such as the energy harvesting and depletion rate, it brings great research challenges to seek an optimal deployment strategy. To this end, we propose an approximation algorithm named Effective Relay Deployment Algorithm, which can be divided into two phases: disk covering and connector insertion using the partitioning technique and the Steinerization technique, respectively. Based on probabilistic analysis, we further optimize the performance ratio of our algorithm to (5 + 6/K) where K is an integer denoting the side length of a cell after partitioning. Our extensive simulation results show that our algorithm can reduce the number of EHRs to be deployed by up to 45% compared with previous work and thus validate the efficiency and effectiveness of our solution.

关键词： approximation algorithm constraint relay deployment energy harvesting wireless sensor network

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A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions

arXiv

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arXiv 2024年

作者： Wang, Xiaxia Cheng, Gong State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science University of Oxford United Kingdom

With the continuous growth of large Knowledge Graphs (KGs), extractive KG summarization becomes a trending task. Aiming at distilling a compact subgraph with condensed information, it facilitates various downstream KG-based tasks. In this survey paper, we are among the first to provide a systematic overview of its applications and define a taxonomy for existing methods from its interdisciplinary studies. Future directions are also laid out based on our extensive and comparative review. © 2024, CC BY.

关键词： Knowledge graph

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A generative deep learning framework for airfoil flow field prediction with sparse data

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Chinese Journal of Aeronautics 2022年第1期35卷 470-484页

作者： Haizhou WU Xuejun LIU Wei AN Hongqiang LYU MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Aerodynamic Noise Control Mianyang 621000China State Key Laboratory of Aerodynamics Mianyang 621000China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Deep learning has been probed for the airfoil performance prediction in recent *** with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper ***,effective training of the data-driven models in deep learning severely hinges on the data in diversity and *** this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse *** presented approach consists of two modules,pre-training module and fine-tuning *** pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training *** the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the *** use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training *** results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.

关键词： CFD Flow field Generative adversarial networks(GANs) Sparse data Supercritical airfoil

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GPT-4 enhanced multimodal grounding for autonomous driving:Leveraging cross-modal attention with large language models

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Communications in Transportation Research 2024年第1期4卷 5-23页

作者： Haicheng Liao Huanming Shen Zhenning Li Chengyue Wang Guofa Li Yiming Bie Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of MacaoMacao SAR999078China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610000China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering and Computer and Information Science University of MacaoMacao SAR999078China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of MacaoMacao SAR999078China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Transportation Jilin UniversityChangchun130000China

In the field of autonomous vehicles(AVs),accurately discerning commander intent and executing linguistic commands within a visual context presents a significant *** paper introduces a sophisticated encoder-decoder framework,developed to address visual grounding in *** Context-Aware Visual Grounding(CAVG)model is an advanced system that integrates five core encoders—Text,Emotion,Image,Context,and Cross-Modal—with a multimodal *** integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features,augmented by state-of-the-art Large Language Models(LLMs)including *** architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic(RSD)layer for attention *** architectural design enables the model to efficiently process and interpret a range of cross-modal inputs,yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual *** evaluations on the Talk2Car dataset,a real-world benchmark,demonstrate that CAVG establishes new standards in prediction accuracy and operational ***,the model exhibits exceptional performance even with limited training data,ranging from 50%to 75%of the full *** feature highlights its effectiveness and potential for deployment in practical AV ***,CAVG has shown remarkable robustness and adaptability in challenging scenarios,including long-text command interpretation,low-light conditions,ambiguous command contexts,inclement weather conditions,and densely populated urban environments.

关键词： Autonomous driving Visual grounding Cross-modal attention Large language models Human-machine interaction

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Identifying key features from app user reviews 21

Identifying key features from app user reviews

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43rd IEEE/ACM International Conference on software Engineering, ICSE 2021

作者： Wu, Huayao Deng, Wenjun Niu, Xintao Nie, Changhai State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China

ISBN: (纸本)9780738113197

Due to the rapid growth and strong competition of mobile application (app) market, app developers should not only offer users with attractive new features, but also carefully maintain and improve existing features based on users' feedbacks. User reviews indicate a rich source of information to plan such feature maintenance activities, and it could be of great benefit for developers to evaluate and magnify the contribution of specific features to the overall success of their apps. In this study, we refer to the features that are highly correlated to app ratings as key features, and we present KEFE, a novel approach that leverages app description and user reviews to identify key features of a given app. The application of KEFE especially relies on natural language processing, deep machine learning classifier, and regression analysis technique, which involves three main steps: 1) extracting feature-describing phrases from app description;2) matching each app feature with its relevant user reviews;and 3) building a regression model to identify features that have significant relationships with app ratings. To train and evaluate KEFE, we collect 200 app descriptions and 1,108,148 user reviews from Chinese Apple App Store. Experimental results demonstrate the effectiveness of KEFE in feature extraction, where an average F-measure of 78.13% is achieved. The key features identified are also likely to provide hints for successful app releases, as for the releases that receive higher app ratings, 70% of features improvements are related to key features. © 2021 IEEE.

关键词： Feature extraction

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