Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition最新文献_第2页

Integrating Hybrid Features for Document-Level Event Role Extraction Method 集成混合特征的文档级事件角色提取方法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574051

Jingyao Zhang, Tao Xu

{"title":"Integrating Hybrid Features for Document-Level Event Role Extraction Method","authors":"Jingyao Zhang, Tao Xu","doi":"10.1145/3573942.3574051","DOIUrl":"https://doi.org/10.1145/3573942.3574051","url":null,"abstract":"Event extraction is a sub-task of information extraction and is an important part of natural language processing. Depending on the range of features used, event extraction methods are classified as sentence-level or document-level. However, document-level event extraction is more practical for practical tasks. Document-level event extraction is a difficult task, as it requires features to be extracted from a larger amount of text to determine which span of text is the desired event element. However, most methods do not utilize both sentence-level and document-level features. In order to utilize hybrid feature information and fuse it, this paper proposes a document-level event extraction method that integrating hybrid features. The event extraction method is based on Dynamic Multi-Pooling Convolutional Neural Network (DMCNN) and Bi-directional Long Short-Term Memory (BiLSTM), combined with self-attention mechanisms and Conditional Random Field (CRF). We evaluate the model proposed in this paper on the MUC-4 dataset and the experimental results show that our proposed model outperforms previous work.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115928438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual Model Checking Distributed System 分布式系统可视化模型检测

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574023

Yiyang Jia, Xinfeng Shu

引用次数: 0

Research on Blockchain Consensus Algorithm for Secure Data Sharing on Industrial Internet Platform 面向工业互联网平台数据安全共享的区块链共识算法研究

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573973

Chao Jia, Lili Gao, Min Li

{"title":"Research on Blockchain Consensus Algorithm for Secure Data Sharing on Industrial Internet Platform","authors":"Chao Jia, Lili Gao, Min Li","doi":"10.1145/3573942.3573973","DOIUrl":"https://doi.org/10.1145/3573942.3573973","url":null,"abstract":"The traditional industrial Internet platform adopts a centralized data storage model, and the security sharing of data generated by terminal devices is a major bottleneck that hinders the development of industrial Internet. Along with the geometric growth of terminal data, protecting the security and integrity of data has become the core research area of the Industrial Internet. Blockchain is distributed, open, transparent and tamper-evident, and can provide a reliable underlying service to realize a distributed data security sharing system. Therefore, this paper proposes a blockchain-based data security sharing model for industrial Internet platforms, with a distributed blockchain network as the core to build up a decentralized data security sharing service. Meanwhile, to address the problems of high consensus latency, low throughput and performance, and no support for node dynamic management of the practical Byzantine fault-tolerant (PBFT) algorithm used in blockchain, a simplified consistency protocol and a new node management mechanism are introduced to achieve dynamic management of nodes while reducing the complexity of algorithm communication.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133474221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MFR Working Mode Recognition Based on CNN-BILSTM-SoftAttention Model 基于CNN-BILSTM-SoftAttention模型的MFR工作模式识别

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574115

Jie Yang, Jinghua Tian

引用次数: 0

High Speed Multi-channel Data Cache Design Based on DDR3 SDRAM 基于DDR3 SDRAM的高速多通道数据缓存设计

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573972

Xiaofeng Yang, Ancheng Liu, Jinjin Wang

{"title":"High Speed Multi-channel Data Cache Design Based on DDR3 SDRAM","authors":"Xiaofeng Yang, Ancheng Liu, Jinjin Wang","doi":"10.1145/3573942.3573972","DOIUrl":"https://doi.org/10.1145/3573942.3573972","url":null,"abstract":"With the rapid development of microelectronics technology, the amount of data information is becoming larger and larger, and the speed of data processing is becoming higher and higher. In order to meet the needs of today's data cache and solve a series of problems such as unstable data transmission and data loss caused by the common data cache technology due to its small capacity and slow data processing speed, a synchronous dynamic random access memory (DDR3 SDRAM) based data cache design method with high speed and large capacity and multi-channel is proposed to achieve fast and efficient real-time storage of eight-channel video data. Based on Vivado MIG IP core and Kintex-7 FPGA as the control core, asynchronous FIFO with read/write bit width ratio of 8:1 is realized, and the read/write cache control module is designed, and the real-time data is finally cached to the corresponding address of DDR3 SDRAM. Improved DDR3 SDRAM bandwidth utilization. The experimental results show that the system can access 8-channel high speed video data, and the data transmission is stable and reliable. The design is mainly composed of multi-channel data acquisition module, cross-clock domain data processing module, read and write priority arbitration and other modules, with a working frequency of up to 400M Hz. It has been verified that the design can be used for real-time acquisition system of space-borne video storage.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133593182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wireless Sensor Networks Based on UAV Auxiliary Energy-Saving Data Collection Algorithms 基于无人机的无线传感器网络辅助节能数据采集算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573982

Aijing Sun, Yijia Li, Shichang Li

引用次数: 0

CBAM-DCE: A Non-Reference Image Correction Algorithm for Uneven Illumination CBAM-DCE:一种非参考图像不均匀光照校正算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574098

Mengyu Fan, Jinjun Lu, Xianguang Kong, Wei Sun, Wei Sun, Yijun Sun

{"title":"CBAM-DCE: A Non-Reference Image Correction Algorithm for Uneven Illumination","authors":"Mengyu Fan, Jinjun Lu, Xianguang Kong, Wei Sun, Wei Sun, Yijun Sun","doi":"10.1145/3573942.3574098","DOIUrl":"https://doi.org/10.1145/3573942.3574098","url":null,"abstract":"Affected by the change in daytime illumination sequence and by the shooting angle in the complex field environment, the kiwifruit images possess the unfriendly features of uneven illumination, such as local darkness and local brightness. The ill-posed image with uneven illumination will seriously constraint the subsequent image analysis processing. Current deep learning methods have achieved satisfactory results, and a large number of paired images (one is the input image, one is the ground truth image) is required to train the better network performance. However, it is difficult to capture ground truth images of the kiwifruit in the field. Based on this, the paper proposed Convolutional Block Attention Module Deep Curve Estimation (CBAM-DCE) to accomplish a non-reference illumination unevenness correction for field kiwifruit images. A deep learning network model is used to estimate the image-specific curve for image enhancement, and a non-reference loss function is applied to evaluate the image enhancement effect. Compared with seven related enhancement algorithms, the presented algorithm shakes off uneven illumination or normal-light image pairs for training. Five different public datasets and our Kiwifruit dataset were used in the experiments. Experiments demonstrate that our proposed CBAM-DCE is superior to other state-of-the-art algorithms for enhancing natural images under different lighting conditions.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124313876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Efficient Image Encryption Algorithm Based on Hyperchaotic and Two Times of Scrambling Diffusion 基于超混沌和二次置乱扩散的高效图像加密算法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574063

Wei Bu, Shu-cui Xie

引用次数: 0

NMT Sentence Granularity Similarity Calculation Method Based on Improved Cosine Distance 基于改进余弦距离的NMT句子粒度相似度计算方法

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3574021

Shuyan Wang, Jingjing Ma

引用次数: 0

Prompt and Contrastive Learning for Few-shot Sentiment Classification 基于提示和对比学习的少镜头情感分类

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2022-09-23 DOI: 10.1145/3573942.3573969

Fei Wang, Long Chen, Xiaohua Huang, Cai Xu, Wei Zhao, Ziyu Guan, Guangyue Lu

{"title":"Prompt and Contrastive Learning for Few-shot Sentiment Classification","authors":"Fei Wang, Long Chen, Xiaohua Huang, Cai Xu, Wei Zhao, Ziyu Guan, Guangyue Lu","doi":"10.1145/3573942.3573969","DOIUrl":"https://doi.org/10.1145/3573942.3573969","url":null,"abstract":"Sentiment classification is a hot topic in the field of natural language processing. Currently, state-of-the-art classification models follow two steps: pre-training a large language model on upstream tasks, and then using human-labeled data to fine-tune a task-related model. However, there is a large gap between the upstream tasks of the pre-trained model and the downstream tasks being performed, resulting in the need for more labeled data to achieve excellent performance. Manually annotating data is expensive. In this paper, we propose a few-shot sentiment classification method based on Prompt and Contrastive Learning (PCL), which can significantly improve the performance of large-scale pre-trained language models in low-data and high-data regimes. Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra-class distribution patterns of labeled data. Thanks to the integration of the two strategies, PCL markedly exceeds baselines with low resources. Extensive experiments on three datasets show that our method has outstanding performance in the few-shot settings.","PeriodicalId":103293,"journal":{"name":"Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117112159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0