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33rd ACM Web Conference, WWW 2024

作者： Lin, Xin Su, Tianhuang Huang, Zhenya Xue, Shangzi Liu, Haifeng Chen, Enhong School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen China University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Knowledge-based question answering (KBQA) is a key task in natural language processing research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs) for reasoning. In the literature, one promising solution for KBQA is to incorporate the pretrained language model (LM) with KGs by generating KG-centered pretraining corpus, which has shown its superiority. However, these methods often depend on specific techniques and resources to work, which may not always be available and restrict its application. Moreover, existing methods focus more on improving language understanding with KGs, while neglect the more important human-like complex reasoning. To this end, in this paper, we propose a general K nowledge-I njected C urriculum P retraining framework (KICP) to achieve comprehensive KG learning and exploitation for KBQA tasks, which is composed of knowledge injection (KI), knowledge adaptation (KA) and curriculum reasoning (CR). Specifically, the KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps that could work with different implementations for flexible application. Next, the KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability to reduce the negative impacts of the difference between the generated and natural corpus. Last, to enable the LM with complex reasoning, the CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner to promote model learning. We provide an implementation of the general framework, and evaluate the proposed KICP on four real-word datasets. The results demonstrate that our framework can achieve higher performances, and have good generalization ability to other QA tasks. © 2024 ACM.

关键词： Curricula

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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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Option Pricing Based on GA-BP neural network

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Procedia Computer Science 2022年 199卷 1340-1354页

作者： Long Qian Jianbin Zhao Yue Ma International College of Zhengzhou University Zhengzhou University Zhengzhou 450052 China Zhengzhou Key Laboratory of Big Data Analysis and Application Henan Academy of Big Data Zhengzhou University Zhengzhou 450052 China

Machine Learning has received widespread attention in the financial field, especially in the issue of option pricing. Compared with the original BSM (Black-Scholes-Merton) option pricing method, this paper introduces the genetic algorithm into the option pricing process based on the characteristics of financial data, and constructs a GA-BP neural network (Neural Networks) option pricing Model and empirical analysis of European call options based on the Shanghai and Shenzhen 300 Index. The research results show that the option price calculation result of the GA-BP neural network algorithm has better accuracy than the BP neural network model or the classic BSM method, while taking into account efficiency, which is helpful to predicting option prices in financial practice to a certain extent.

关键词： BP Neural Network Genetic Algorithm Option Pricing BSM Model

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A method for correcting characteristic X-ray net peak count from drifted shadow peak

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Nuclear Science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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FairLISA: fair user modeling with limited sensitive attributes information 23

FairLISA: fair user modeling with limited sensitive attribut...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zheng Zhang Qi Liu Hao Jiang Fei Wang Yan Zhuang Le Wu Weibo Gao Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Hefei University of Technology

User modeling techniques profile users' latent characteristics (e.g., preference) from their observed behaviors, and play a crucial role in decision-making. Unfortunately, traditional user models may unconsciously capture biases related to sensitive attributes (e.g., gender) from behavior data, even when this sensitive information is not explicitly provided. This can lead to unfair issues and discrimination against certain groups based on these sensitive attributes. Recent studies have been proposed to improve fairness by explicitly decorrelating user modeling results and sensitive attributes. However, most existing approaches assume that fully sensitive attribute labels are available in the training set, which is unrealistic due to collection limitations like privacy concerns, and hence bear the limitation of performance. In this paper, we focus on a practical situation with limited sensitive data and propose a novel FairLISA framework, which can efficiently utilize data with known and unknown sensitive attributes to facilitate fair model training. We first propose a novel theoretical perspective to build the relationship between data with both known and unknown sensitive attributes with the fairness objective. Then, based on this, we provide a general adversarial framework to effectively leverage the whole user data for fair user modeling. We conduct experiments on representative user modeling tasks including recommender system and cognitive diagnosis. The results demonstrate that our FairLISA can effectively improve fairness while retaining high accuracy in scenarios with different ratios of missing sensitive attributes.

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Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 987-1001页

作者： Zichao Zhang Jian Chen Xinyu Xu Cunjia Liu Yu Han College of Engineering China Agricultural UniversityBeijingChina Jiangsu Province and Education Ministry Co‐sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu UniversityZhenjiangChina Key Laboratory of Spatial‐temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghaiChina Department of Aeronautical and Automotive Engineering Loughborough UniversityLoughboroughLeicestershireUK College of Water Resources and Civil Engineering China Agricultural UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijingChina Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhenChina

The binocular stereo vision is the lowest cost sensor for obtaining 3D *** the weakness of long‐distance measurement and stability,the improvement of accuracy and stability of stereo vision is urgently required for application of precision *** address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade,inspired by hawk eyes,higher resolution perception and the adaptive HDR(High Dynamic Range)were introduced in this *** the function from physiological structure of‘deep fovea’and‘shallow fovea’of hawk eye,the higher resolution reconstruction method in this paper was aimed at ac-curacy *** by adjustment of pupils,the adaptive HDR method was proposed for high dynamic range optimisation and stable *** various light conditions,compared with default stereo vision,the accuracy of proposed algorithm was improved by 28.0%evaluated by error ratio,and the stability was improved by 26.56%by disparity *** fixed distance measurement,the maximum improvement was 78.6%by standard *** on the hawk‐eye‐inspired perception algorithm,the point cloud of orchard was improved both in quality and *** hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud recon-struction in orchard navigation map.

关键词： adaptive high dynamic range binocular stereo vision hawk‐eye‐inspired perception point cloud of orchard super‐resolution generative adversarial network

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TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

arXiv

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arXiv 2023年

作者： Cheng, Mingyue Liu, Qi Liu, Zhiding Zhang, Hao Zhang, Rujiao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Data Science University of Science and Technology of China Anhui Hefei230027 China

Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the current methods are not sufficient to learn optimal time series representations due to solely unidirectional encoding over sparse point-wise input units. In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks. The distinct characteristics of the TimeMAE lie in processing each time series into a sequence of non-overlapping sub-series via window-slicing partitioning, followed by random masking strategies over the semantic units of localized sub-series. Such a simple yet effective setting can help us achieve the goal of killing three birds with one stone, i.e., (1) learning enriched contextual representations of time series with a bidirectional encoding scheme;(2) increasing the information density of basic semantic units;(3) efficiently encoding representations of time series using transformer networks. Nevertheless, it is a non-trivial to perform reconstructing task over such a novel formulated modeling paradigm. To solve the discrepancy issue incurred by newly injected masked embeddings, we design a decoupled autoencoder architecture, which learns the representations of visible (unmasked) positions and masked ones with two different encoder modules, respectively. Furthermore, we construct two types of informative targets to accomplish the corresponding pretext tasks. One is to create a tokenizer module that assigns a codeword to each masked region, allowing the masked codeword classification (MCC) task to be completed effectively. Another one is to adopt a siamese network structure to generate target representations for each masked input unit, aiming at performing the mask

关键词： Time series

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A novel ship detection method based on Gabor feature 5

A novel ship detection method based on Gabor feature

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IET International Radar Conference 2020, IET IRC 2020

作者： Chang, Jiahui Fu, Zewen Li, Ning Chang, Jike College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) Systems can be used for discovery and surveillance. According to the current requirements, accurate ship detection is becoming more and more important. However, the accuracy of traditional ship detection algorithm is not satisfying in complex scene. As a consequence, this paper proposes a novel method for detecting ships within SAR imagery using Gabor feature and Two Parameter Constant False Alarm Rate (2P-CFAR) detection technique. First, feature extraction was performed by using Gabor filter. Then, combined with the extracted orientation feature, the ship recognition of SAR image is obtained by 2P-CFAR detector. Finally, false alarms among the candidate targets were removed by geometric features of the ships. To prove the rationality of this method, experiments were carried with two high-resolution Gaofen-3 satellite SAR images in open sea background. The experimental results show that the presented method has preferable performance in the accuracy of ship identification. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Synthetic aperture radar

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Paralle acceleration of multi-mode SAR imaging on GPU 5

Paralle acceleration of multi-mode SAR imaging on GPU

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IET International Radar Conference 2020, IET IRC 2020

作者： Bai, Lan Guo, Zhengwei Wang, Kuan Fu, Zewen College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

With the continuous expansion of the application field of multi-mode synthetic aperture radar (SAR) system, which can satisfy different imaging demands. In order to get SAR image, it is necessary to image the echo received by multi-mode SAR system. However, the huge computation of the imaging algorithm affects the processing efficiency of SAR system. The power of Graphics Processing Unit (GPU) can suffice the increasing demands in processing, which has been used in many common areas. The acceleration effect of GPU is very obvious compared with Central Processing Unit (CPU) platform. This paper proposed a Chirp Scaling acceleration algorithm based on GPU for multi-mode SAR, and comparison of computational time over traditional CPU and GPU platform. The experimental results shows that the GPU accelerated SAR imaging algorithm can achieve a good acceleration effect. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Program processors

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A new vegetation index for cooperative inversion of winter wheat covered surface soil moisture based on sentinel-1/2 remote sensing data 5

A new vegetation index for cooperative inversion of winter w...

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IET International Radar Conference 2020, IET IRC 2020

作者： Zhang, Bei Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Aiming at the problem of the interfere of winter wheat on radar backscattering coefficient in surface soil moisture inversion, a new vegetation index called Fusion Vegetation Index (FVI) is defined in this study. Based on the Multi-Spectral Imager (MSI) data of Sentinel-2, and the Synthetic Aperture Radar (SAR) data of Sentinel-1, using FVI to retrieve water content of winter wheat, combined with Water Cloud model, the interference of winter wheat in soil moisture inversion was reduced. The results show that good soil moisture retrieval results can be obtained by combining with FVI. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Soil moisture

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