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arXiv 2024年

作者： Chen, Tianyu Zhang, Yiming Yu, Guoxin Zhang, Dapeng Zeng, Li He, Qing Ao, Xiang Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China School of IoT Engineering Jiangsu Vocational College of Information Technology Wuxi China Information Technology Department I Shenzhen Stock Exchange China

In this paper, we extend financial sentiment analysis (FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictions, it has specialized challenges due to the lengthy and discontinuity of events in a financial text. To this end, we reconceptualize the event extraction as a classification task by designing a categorization comprising coarse-grained and finegrained event categories. Under this setting, we formulate the Event-Level Financial Sentiment Analysis (EFSA for short) task that outputs quintuples consisting of (company, industry, coarse-grained event, fine-grained event, sentiment) from financial text. A large-scale Chinese dataset containing 12, 160 news articles and 13, 725 quintuples is publicized as a brand new testbed for our task. A four-hop Chainof- Thought LLM-based approach is devised for this task. Systematically investigations are conducted on our dataset, and the empirical results demonstrate the benchmarking scores of existing methods and our proposed method can reach the current state-of-the-art. Our dataset and framework implementation are available at https://***/cty1934/EFSA. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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An External Knowledge-Based Sentiment Classification Method for Chinese Short Texts

An External Knowledge-Based Sentiment Classification Method ...

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Neuromorphic computing (ICNC), International Conference on

作者： Zhaoxun Li Huaqing Li Huiwei Wang College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Intelligent Information Processing Chongqing Three Gorges University Chongqing China and Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

With the rapid development of Internet applications, a large number of short texts expressing public opinion with emotional views have been generated. The subtexts of these short texts are complex, which bring a great challenge to emotional classification. In order to solve this problem, this paper proposes a method for sentiment classification of Chinese short texts based on fused external knowledge. Firstly, we construct a prompt text with fused sentiment knowledge, splice the prompt text with the original input text into the pre-training language model, and guide the pre-training model through the prompt. Then, we design label words for the text domain and map the prediction results for classification. The experimental results show that this method exhibits good performance on the Meituan Waimai dataset, with an accuracy of 81.3%, which are 1.8%, 2.2% and 3.5% higher than the mainstream modeling methods such as BERT, TextCNN and ERINE, respectively.

关键词： Vocabulary Analytical models Accuracy Reviews Neuromorphic engineering Knowledge based systems Natural language processing Internet

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Control of Flexible Launch Vehicle Based on Singular Perturbation and Reinforcement Learning

Control of Flexible Launch Vehicle Based on Singular Perturb...

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Chinese Control and Decision Conference, CCDC

作者： Tong Qin Lifu Du Yihui Ruan Jingqi Li Lei Liu Huijin Fan National Key Laboratory of Multispectral Information Intelligent Processing Technology. School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In this paper, a data-driven algorithm based on singular perturbation (SP) and reinforcement learning (RL) for the elastic launch vehicle attitude control and elastic suppression has been proposed. Considering that the rigid body state and elastic mode exhibit different time-scale properties, the model is initially decomposed into a stiff body slowly changing subsystem and an elastic fast changing subsystem by employing the SP theory. In addition, a data-driven off-policy learning algorithm is proposed to address the problem of the inability to obtain a precise mathematical model due to aerodynamic parameter perturbation during flight. Without using the precise mathematical model of the system, the algebraic Riccati equation (ARE) of the subsystem can be solved iteratively to obtain its control law, then the control law of the original system is obtained through composite control strategy. Finally, the elastic suppression performance of the proposed method and using a notch filter is compared, which demonstrates the proposed method has a better performance and the rigid body attitudes converge more quickly and steadily.

关键词： Attitude control System dynamics Perturbation methods Heuristic algorithms Riccati equations Reinforcement learning Aerodynamics

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing 39

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

ISBN: (纸本)157735897X

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. Our codes will be made publicly available. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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SERGCN: Graph Convolutional Neural Networks with Skip-Edge Residual Connection for Node Classification Tasks

SERGCN: Graph Convolutional Neural Networks with Skip-Edge R...

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Aqian Qin Guoqing He Huiwei Wang College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Intelligent Information Processing Chongqing Three Gorges University Chongqing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Graph Convolutional Networks (GCNs) have been widely used for node categorization, graph embedding, and link prediction, etc. Two main challenges regarding developing deep GCNs for node classifying are overfitting and oversmoothing. The main causes of oversmoothing are overfitting and training barriers, which is directly caused by gradient disappearance. To resolve this issue, we propose the Skip-Edge Residual Graph Con-volution Network (SERGCN), which extracts a certain proportion of edge information at each layer in the novel way and integrates the initial information and the information of the previous layer in the convolution process, so as to prevent and slow down the gradient disappearance and further oversmoothing. Based on extensive experiments and real-world data sets used in several benchmarks, our proposed solution incorporates the benefits of a deep architecture while equaling or even exceeding the accuracy of existing approaches.

关键词： Training Degradation Accuracy Graph convolutional networks Convolution Deep architecture Benchmark testing Data mining Informatics

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PFACO: Potential Field Ant Colony Optimization for Mobile Robot Path Planning

PFACO: Potential Field Ant Colony Optimization for Mobile Ro...

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Neuromorphic computing (ICNC), International Conference on

作者： Hao Wei Huiwei Wang College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Intelligent Information Processing Chongqing Three Gorges University Chongqing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

In response to the challenge of global path planning difficulties for automated guided vehicle in complex environments, traditional ant colony algorithms face several issues such as long search times, unordered search, and difficulty in finding the optimal route. We propose an optimized ant colony algorithm with potential field, named PFACO, that combines heuristic search and artificial potential field methods, along with the utilization of the pruning algorithm for secondary optimization of turning points. Specifically, we introduce improved potential fields and design heuristic functions to guide ants to swiftly navigate through unordered search states. After that, we incorporate the Triangle Pruning Algorithm (TPA) to perform secondary optimization by connecting turning points along the path. Simulation experiments demonstrate that PFACO, compared to both traditional Ant Colony Optimization (ACO) algorithms and other improved ACO algorithms, achieves an average improvement of 93% and 78% in convergence speed, and an average optimization of 25.26% and 3.25% in path length, respectively, across various map conditions. The results show that PFACO has better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.

关键词： Ant colony optimization Remotely guided vehicles Smoothing methods Heuristic algorithms Turning Path planning Robustness Planning Optimization Convergence

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ESNet: evolution and succession network for high-resolution salient object detection 24

ESNet: evolution and succession network for high-resolution ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongyu Liu Runmin Cong Hua Li Qianqian Xu Qingming Huang Wei Zhang Institute of Information Science Beijing Jiaotong University & Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University & Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature; the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24.

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Incremental attribute reduction based on knowledge granularity under incomplete data 3

Incremental attribute reduction based on knowledge granulari...

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2021 3rd International Conference on Artificial Intelligence and Computer Science, AICS 2021

作者： Zhong, Qiangqiang Wang, Lei Yang, Wen Liu, Chao School of Information Engineering Nanchang Institute of Technology Nanchang330000 China Jiangxi Provincial Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang330000 China

Traditional attribute reductions can’t be directly applied to miss attribute values and dynamic changes data. Therefore, how to dynamically update attribute reductions efficiently when incomplete data changes dynamically is great research value. For the change of the object, using matrix’s simple and clear characteristics, the paper focus on the incremental attribute reduction algorithm in matrix form of knowledge granularity. Firstly, the knowledge granularity is obtained by matrix which uses to measure the uncertainty of the system, then calculating the attribute importance, and using the attribute importance to devise a static algorithm, and then analyzing incremental matrix form when the object increases. The comparative experiments show that the incremental algorithm can effectively and efficiently update the result of attribute reduction as the object changes. © Journal of Physics: Conference Series 2021.

关键词： Matrix algebra

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Joint Beamforming and Offloading Design for Integrated Sensing, Communication and Computation System

arXiv

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arXiv 2024年

作者： Liu, Peng Fei, Zesong Wang, Xinyi Zhou, Yiqing Zhang, Yan Liu, Fan The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The State Key Laboratory of Processors The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Mobile edge computing (MEC) is powerful to alleviate the heavy computing tasks in integrated sensing and communication (ISAC) systems. In this paper, we investigate joint beamforming and offloading design in a three-tier integrated sensing, communication and computation (ISCC) framework comprising one cloud server, multiple mobile edge servers, and multiple terminals. While executing sensing tasks, the user terminals can optionally offload sensing data to either MEC server or cloud servers. To minimize the execution latency, we jointly optimize the transmit beamforming matrices and offloading decision variables under the constraint of sensing performance. An alternating optimization algorithm based on multidimensional fractional programming is proposed to tackle the non-convex problem. Simulation results demonstrates the superiority of the proposed mechanism in terms of convergence and task execution latency reduction, compared with the state-of-the-art two-tier ISCC framework. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Large Model-Based Data Augmentation for Imbalanced Text Classification

Large Model-Based Data Augmentation for Imbalanced Text Clas...

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Artificial Intelligence, Networking and information technology (AINIT), International Seminar on

作者： Dawei Zhang Rongxin Mi Peiyao Zhou Dawei Jin Manman Zhang Tianhang Song School of Computer Science Jiangsu University of Science and Technology Zhenjiang Jiangsu China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

This study focuses on the application of large models to deal with imbalanced data problems in text classification. In view of the central position of text in web data and the negative impact of class imbalance on classifier performance, researchers have explored the method of using large models to generate high-quality minority class samples to enhance model performance. This paper reviews the technical progress of machine learning, deep learning, and large language models and their applications in text classification tasks. Although large models perform well in complex tasks due to their excellent language understanding ability, traditional machine learning and deep learning methods are popular in text classification scenarios that require fast response due to their simple structure and higher computational efficiency. This study proposes a data augmentation technique inspired by SMOTE, which uses a large language model combined with a simple prompt engineering strategy to generate high-quality minority samples. The experimental results show that the proposed method significantly improves the macro average precision, recall and F1 score on multiple text classification models, and effectively alleviates the challenge of class imbalance.

关键词： Deep learning Seminars Computational modeling Large language models Text categorization Data augmentation Data models

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