AI Communications最新文献_第5页

Sound event localization and detection using element-wise attention gate and asymmetric convolutional recurrent neural networks 基于元素注意门和非对称卷积递归神经网络的声音事件定位与检测

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-04-20 DOI: 10.3233/aic-220125

Lean Yan, Min Guo, Zhiqiang Li

{"title":"Sound event localization and detection using element-wise attention gate and asymmetric convolutional recurrent neural networks","authors":"Lean Yan, Min Guo, Zhiqiang Li","doi":"10.3233/aic-220125","DOIUrl":"https://doi.org/10.3233/aic-220125","url":null,"abstract":"There are problems that standard square convolution kernel has insufficient representation ability and recurrent neural network usually ignores the importance of different elements within an input vector in sound event localization and detection. This paper proposes an element-wise attention gate-asymmetric convolutional recurrent neural network (EleAttG-ACRNN), to improve the performance of sound event localization and detection. First, a convolutional neural network with context gating and asymmetric squeeze excitation residual is constructed, where asymmetric convolution enhances the capability of the square convolution kernel; squeeze excitation can improve the interdependence between channels; context gating can weight the important features and suppress the irrelevant features. Next, in order to improve the expressiveness of the model, we integrate the element-wise attention gate into the bidirectional gated recurrent network, which is to highlight the importance of different elements within an input vector, and further learn the temporal context information. Evaluation results using the TAU Spatial Sound Events 2019-Ambisonic dataset show the effectiveness of the proposed method, and it improves SELD performance up to 0.05 in error rate, 1.7% in F-score, 0.7° in DOA error, and 4.5% in Frame recall compared to a CRNN method.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"27 1","pages":"147-157"},"PeriodicalIF":0.8,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78478034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The 11th IJCAR automated theorem proving system competition - CASC-J11 第十一届IJCAR自动定理证明系统竞赛——CASC-J11

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-03-30 DOI: 10.3233/aic-220244

G. Sutcliffe, Martin Desharnais

引用次数: 1

Automatic Chinese knowledge-based question answering by the MGBA-LSTM-CNN model 基于MGBA-LSTM-CNN模型的中文知识自动问答

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-03-13 DOI: 10.3233/aic-210003

Wenyuan Liu, Mingliang Fan, Kai Feng, Dingding Guo

引用次数: 0

PairTraining: A method for training Convolutional Neural Networks with image pairs PairTraining:一种用图像对训练卷积神经网络的方法

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-02-10 DOI: 10.3233/aic-220145

Yuhong Shi, Yan Zhao, C. Yao

{"title":"PairTraining: A method for training Convolutional Neural Networks with image pairs","authors":"Yuhong Shi, Yan Zhao, C. Yao","doi":"10.3233/aic-220145","DOIUrl":"https://doi.org/10.3233/aic-220145","url":null,"abstract":"In the field of image classification, the Convolutional Neural Networks (CNNs) are effective. Most of the work focuses on improving and innovating CNN’s network structure. However, using labeled data more effectively for training has also been an essential part of CNN’s research. Combining image disturbance and consistency regularization theory, this paper proposes a model training method (PairTraining) that takes image pairs as input and dynamically modify the training difficulty according to the accuracy of the model in the training set. According to the accuracy of the model in the training set, the training process will be divided into three stages: the qualitative stage, the fine learning stage and the strengthening learning stage. Contrastive learning images are formed using a progressively enhanced image disturbance strategy at different training stages. The input image and contrast learning image are combined into image pairs for model training. The experiments are tested on four public datasets using eleven CNN models. These models have different degrees of improvement in accuracy on the four datasets. PairTraining can adapt to a variety of CNN models for image classification training. This method can better improve the effectiveness of training and improve the degree of generalization of classification models after training. The classification model obtained by PairTraining has better performance in practical application.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"36 1","pages":"111-126"},"PeriodicalIF":0.8,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83259888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning invariant representation using synthetic imagery for object detection 使用合成图像学习不变表示用于目标检测

4区计算机科学

AI Communications Pub Date : 2023-02-09 DOI: 10.3233/aic-220039

Ning Jiang, Jinglong Fang, Yanli Shao

引用次数: 0

Defending against adversarial attacks on graph neural networks via similarity property 基于相似性的图神经网络对抗性攻击防御

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-01-23 DOI: 10.3233/aic-220120

Minghong Yao, Haizheng Yu, H. Bian

引用次数: 0

Automatic prediction of epileptic seizure using hybrid deep ResNet-LSTM model 基于混合深度ResNet-LSTM模型的癫痫发作自动预测

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-01-23 DOI: 10.3233/aic-220177

Y. Singh, D. K. Lobiyal

{"title":"Automatic prediction of epileptic seizure using hybrid deep ResNet-LSTM model","authors":"Y. Singh, D. K. Lobiyal","doi":"10.3233/aic-220177","DOIUrl":"https://doi.org/10.3233/aic-220177","url":null,"abstract":"Numerous advanced data processing and machine learning techniques for identifying epileptic seizures have been developed in the last two decades. Nonetheless, many of these solutions need massive data sets and intricate computations. Our approach transforms electroencephalogram (EEG) data into the time-frequency domain by utilizing a short-time fourier transform (STFT) and the spectrogram (t-f) images as the input stage of the deep learning model. Using EEG data, we have constructed a hybrid model comprising of a Deep Convolution Network (ResNet50) and a Long Short-Term Memory (LSTM) for predicting epileptic seizures. Spectrogram images are used to train the proposed hybrid model for feature extraction and classification. We analyzed the CHB-MIT scalp EEG dataset. For each preictal period of 5, 15, and 30 minutes, experiments are conducted to evaluate the performance of the proposed model. The experimental results indicate that the proposed model produced the optimum performance with a 5-minute preictal duration. We achieved an average accuracy of 94.5%, the average sensitivity of 93.7%, the f1-score of 0.9376, and the average false positive rate (FPR) of 0.055. Our proposed technique surpassed the random predictor and other current algorithms used for seizure prediction for all patients’ data in the dataset. One can use the effectiveness of our proposed model to help in the early diagnosis of epilepsy and provide early treatment.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"15 1","pages":"57-72"},"PeriodicalIF":0.8,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85929328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Learning invariant representation using synthetic imagery for object detection 使用合成图像学习不变表示用于目标检测

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-12-30 DOI: 10.2139/ssrn.4033177

Ning Jiang, Jinglong Fang, Yanli Shao

引用次数: 0

Multimodal interaction aware embedding for location-based social networks 基于位置的社交网络的多模态交互感知嵌入

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-12-12 DOI: 10.3233/aic-220161

Ruiyun Yu, Kang Yang, Zhihong Wang, Shi Zhen

{"title":"Multimodal interaction aware embedding for location-based social networks","authors":"Ruiyun Yu, Kang Yang, Zhihong Wang, Shi Zhen","doi":"10.3233/aic-220161","DOIUrl":"https://doi.org/10.3233/aic-220161","url":null,"abstract":"Location-based social networks (LBSNs) have greatly promoted the development of the field of human mobility mining. However, the sparsity, multimodality and heterogeneity nature of the user check-in data remains a great concern for learning high-quality user or other entities representations, especially in the downstream application tasks, such as point-of-interest (POI) recommendation. Most existing methods focus on user preference modeling based on sequential POI tags without exploring the interaction between different modalities (e.g., user-user interactions, user-timestamp interactions, user-POI interactions, etc.). To this end, we introduce a multimodal interaction aware embedding framework to generate reliable entity embeddings on the heterogeneous socio-spatial network. At its core, first, multi-modal interaction sub-graph sampling techniques are designed to capture the heterogeneous contexts; then, a self-supervised contrastive learning technique is leveraged to extract intra-modality and inter-modality interactions in a light way. We conduct experiments on the next-POI recommendation tasks based on three real-world datasets. Experimental results demonstrate the superiority of our model over the state-of-the-art embedding learning algorithms.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"1 1","pages":"41-55"},"PeriodicalIF":0.8,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89563516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SECL: Sampling enhanced contrastive learning SECL:抽样增强对比学习

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-22 DOI: 10.3233/aic-210234

Yixin Tang, Hua Cheng, Yiquan Fang, Tao Cheng

引用次数: 0