Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00086

Li Liu, Wujun Yang, Zhixian Chang

引用次数: 0

Unsupervised Contradiction Detection using Sentence Transformations 基于句子变换的无监督矛盾检测

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00065

Gerrit Schumann, Jorge Marx Gómez

引用次数: 0

Multi-level Feature Extraction and Edge Reconstruction Fused Generative Adversarial Networks for Image Super Resolution 面向图像超分辨率的多层次特征提取与边缘重建融合生成对抗网络

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00027

Yinghua Li, Yue Liu, Y. Liu, Yangge Qiao, Jinglu He

{"title":"Multi-level Feature Extraction and Edge Reconstruction Fused Generative Adversarial Networks for Image Super Resolution","authors":"Yinghua Li, Yue Liu, Y. Liu, Yangge Qiao, Jinglu He","doi":"10.1109/ICNLP58431.2023.00027","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00027","url":null,"abstract":"At present, the image super-resolution method based on convolutional neural network has achieved a very high PSNR, but the high-frequency information obtained by using the mean square error as the loss function is not sufficient, and when the scale factor is large, the detail texture of the restored image is blurred, and it is not completely consistent with the human visual perception. Therefore, this paper proposes an image super-resolution algorithm based on GAN. We modify the residual block of the original SRGAN generator network into three modules: Edge-Reconstruction network, Low-Frequency feature (LF-feature) extraction module and Residual network. The Edge-Reconstruction network reconstructs the edge of SR image, and the LF-feature extraction module extracts the low-frequency information of the image. After that, the two parts of information are fused and transmitted to Residual network to extract the high-frequency information of the image, and then the SR image is reconstructed and enlarged. And use skip connection in the network to increase the network depth. The training results show that our network has better performance in both objective evaluation indicators and subjective vision. Even with a large-scale factor, our network can recover fine texture information.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84894210","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

Perceptual assimilation of Mandarin consonants in second language acquisition 普通话辅音在二语习得中的知觉同化

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00054

Dan Du, Minghu Jiang

引用次数: 0

Intelligent Fault Diagnosis of Rolling Bearing based on VMD and Improved Self-training Semi-supervised Ensemble Learning 基于VMD和改进自训练半监督集成学习的滚动轴承智能故障诊断

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00080

Xiangyu Li, Yao Liu, Gaige Chen, Jiantao Chang

{"title":"Intelligent Fault Diagnosis of Rolling Bearing based on VMD and Improved Self-training Semi-supervised Ensemble Learning","authors":"Xiangyu Li, Yao Liu, Gaige Chen, Jiantao Chang","doi":"10.1109/ICNLP58431.2023.00080","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00080","url":null,"abstract":"Intelligent fault diagnosis of rolling bearing is of great importance to improve the predictive maintenance ability of key assets in the context of industrial big data and smart manufacturing. Due to the usually high cost or infeasibility of obtaining data labels, large amount of data is unlabeled in practical industrial scenarios, which poses a challenge for conducting data-driven bearing fault diagnosis. In view of the characteristics of non-stationary and low signal-to-noise ratio of bearing vibration signals and the fact of lacking labeled samples but there exist lots of unlabeled samples, this paper proposes an intelligent diagnosis method for bearing faults based on variational mode decomposition (VMD) and improved self-training semi-supervised ensemble learning. Firstly, the original vibration signal is decomposed into several intrinsic mode functions using VMD, then correlation coefficient criterion is used to select the bearing fault feature bands to improve the signal-to-noise ratio, then time domain features are extracted, the labeled samples are expanded by the improved self-training semisupervised learning model, and finally the bearing fault diagnosis model is established based on ensemble learning by stacking method. Through the validation on two different experimental data sets, the proposed method was able to effectively extract the bearing fault feature information and improve the model accuracy by using unlabeled data compared with typical supervised learning models and other comparative models, which can meet the demand for intelligent diagnosis of bearing fault under the scenario of lacking labeled samples in real industries.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84010040","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

Multi-user Computing Offloading Based on Deep Reinforcement Learning 基于深度强化学习的多用户计算卸载

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00091

Liyuan Feng, Wujun Yang

引用次数: 0

Named Entity Recognition Method Based on BERT-whitening and Dynamic Fusion Model 基于bert -白化和动态融合模型的命名实体识别方法

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00041

Meng Liang, Yao Shi

{"title":"Named Entity Recognition Method Based on BERT-whitening and Dynamic Fusion Model","authors":"Meng Liang, Yao Shi","doi":"10.1109/ICNLP58431.2023.00041","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00041","url":null,"abstract":"In the context of Natural Language Processing (NLP), Named Entity Recognition (NER) plays a crucial role in tasks like entity relationship extraction and knowledge graph construction. The accuracy of Chinese NER heavily relies on the representation of word embeddings. However, traditional word representation methods like word2vec suffer from word ambiguity and singular word vectors. Similarly, BERT-based word embeddings also exhibit anisotropy. To tackle these challenges, we propose a novel NER method that leverages BERT-whitening and dynamic fusion of BERT’s output from different layers. The dynamic fusion module calculates a weighted sum of BERT’s output across multiple layers, while the whitening module applies a whitening operation to eliminate the anisotropy of word embeddings. By integrating these modules, our model effectively captures the characteristics of input words, providing robust support for subsequent decoding. We evaluate our approach on the CLUENER2020 Chinese fine-grained named entity recognition dataset. Experimental results demonstrate that our method outperforms the traditional BERT-BiLSTM-CRF model without external resources and data expansion, leading to significant improvements in performance.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88991284","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

Research on Switching Strategy with Reinforcement Learning and Game Theory in Satellite-Terrestrial Integrated Networks 基于强化学习和博弈论的星地集成网络切换策略研究

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00088

Shihao Fei, Junxuan Wang, Fan Jiang, Yuan Ren, Senhu Zhou

{"title":"Research on Switching Strategy with Reinforcement Learning and Game Theory in Satellite-Terrestrial Integrated Networks","authors":"Shihao Fei, Junxuan Wang, Fan Jiang, Yuan Ren, Senhu Zhou","doi":"10.1109/icnlp58431.2023.00088","DOIUrl":"https://doi.org/10.1109/icnlp58431.2023.00088","url":null,"abstract":"In Satellite-Terrestrial Integrated Networks (STIN), from the perspective of increasing the capacity of the networks, the user experience, and the adaptability to high-speed motion occasions, a non-cooperative multi-service network selection scheme based on Q-learning and game theory (QRSG) is proposed. QRSG first obtains the multi-service network utility through the fuzzy process and uses it as the reward of Q-learning. The state of Q-learning includes the quality of service (QoS) and price attributes of the network currently connected by the user, as well as the situation of the user speed. The corresponding network selection strategy is the action of Q-learning. Then, the user predicts the payoff of the network selection strategy through a game algorithm to avoid access to an overloaded network. In addition, Binary Exponential Backoff Algorithm is introduced in QRSG to solve the problem of inaccurate throughput prediction in the scenario where multiple users concurrently switch to the same service node (SN). Simulations reveal that: 1) With QRSG, users with different speeds and QoS requirements can adaptively switch to the most suitable network. 2) Compared with the existing algorithms, QRSG can increase network throughput by more than 8% and reduce the total number of switching by about 60% in the case of a maximum loss of 1 to 2% of the system fairness.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88838960","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

Target Tracking Algorithm Based on Mixed Attention and Siamese Network 基于混合注意力和暹罗网络的目标跟踪算法

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00013

Haibo Ge, Yu An, Wenhao He, Haodong Feng, Chaofeng Huang, Shuxian Wang

{"title":"Target Tracking Algorithm Based on Mixed Attention and Siamese Network","authors":"Haibo Ge, Yu An, Wenhao He, Haodong Feng, Chaofeng Huang, Shuxian Wang","doi":"10.1109/ICNLP58431.2023.00013","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00013","url":null,"abstract":"Siamese convolutional neural network, which is a classic framework for object tracking, has received extensive attention from the research community. The method uses a convolutional neural network to obtain target features and matches them with the search area features to achieve target tracking. Aiming at the problems that multi-layer features are difficult to extract effectively and network model parameters are complex, a target tracking algorithm (MA-SiamRPN++) with mixed attention mechanism is proposed based on SiamRPN++. Firstly, the channel attention mechanism is inserted into the backbone network, and then the output features of the channel attention network are fed into the spatial attention network, so as to improve the efficiency of feature extraction in different convolution layers by using mixed attention. At the same time, the deep cross -correlation network is used to better retain the feature information that is conducive to tracking and reduce the parameter complexity of the network to maintain the tracking speed. Finally, experiments on OTB100, VOT2016, and the long-term tracking dataset LaSOT show that the tracker proposed in this paper achieves higher accuracy and success rate than other state-of-the-art trackers.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77061572","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

Data-Driven Pruning Algorithm Based on Result Orientation 基于结果导向的数据驱动剪枝算法

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00043

Jin Wu, Zhaoqi Zhang, Bo Zhao, Yu Wang

{"title":"Data-Driven Pruning Algorithm Based on Result Orientation","authors":"Jin Wu, Zhaoqi Zhang, Bo Zhao, Yu Wang","doi":"10.1109/ICNLP58431.2023.00043","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00043","url":null,"abstract":"With their outstanding performance in natural language processing tasks such as machine translation and semantic recognition, deep neural networks have attracted great attention from both academia and industry. For more complex NLP tasks, people try to add more parameters to the network, expand more layers, input larger data samples, to produce a large model to solve the complex task. However, it is not the case that the deeper the layers, the better the parameters. There is a large amount of redundant information in the parameters, which not only contributes nothing to the results, but also increases the computational burden of the model and the storage burden of the hardware. Eliminating a small amount of redundant information often has no effect on the recognition rate of the model, but slightly improves it [1], so the neural network model needs to be compressed. Existing compression methods include model pruning, parameter quantization, tensor decomposition, knowledge distmation, etc. [2] In this paper, model pruning algorithm is selected to implement a result-oriented data-driven pruning algorithm by introducing the propagation characteristics and inter-layer correlation of neural networks and automatic decision making based on Feature Map information. Finally, the effectiveness of the result - oriented data - driven pruning algorithm is proved by comparative experiments.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88876216","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

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