Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence最新文献_第2页

EGFNet: Efficient guided feature fusion network for skin cancer lesion segmentation EGFNet:用于皮肤癌病灶分割的高效引导特征融合网络

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529482

Rui Fan, Zhiqiang Wang, Qing Zhu

引用次数: 2

DGIC: A Distributed Graph Inference Computing Framework Suitable For Encoder-Decoder GNN 一种适用于编码器-解码器GNN的分布式图推理计算框架

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529493

Zeting Pan, Junsheng Chang

{"title":"DGIC: A Distributed Graph Inference Computing Framework Suitable For Encoder-Decoder GNN","authors":"Zeting Pan, Junsheng Chang","doi":"10.1145/3529466.3529493","DOIUrl":"https://doi.org/10.1145/3529466.3529493","url":null,"abstract":"∗ A graph is a structure that can express the relationship between objects. The emergence of GNN enables deep learning to be applied in the field of graphs. However, most GNNs are trained offline and cannot be directly used in real-time monitoring scenarios such as financial risk control. In addition, due to the large scale of graph data, a single machine often cannot meet actual needs, and there are bottlenecks such as throughput performance. Therefore, we propose a distributed graph inference computing framework, which can be applied to Encoder-Decoder GNN models. We complete the adaptation of the model by disassembling the graph data and using the extension storage and dynamic invocation mechanism to solve the model invocation problem. For inference performance, we implement dynamic graph construction through incremental composition and decouple the inference process to apply to different scenarios, so that GNNs conforming to the Encoder-Decoder style can be applied to the framework. A large number of experiments show that this method has good timeliness while improving the throughput upper limit, and can maintain the model effect of multi-tasking.","PeriodicalId":375562,"journal":{"name":"Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114392870","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

Music generation based on emotional EEG 基于情绪脑电图的音乐生成

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529492

Gang Luo, Hao Chen, Zhengxiu Li, M. Wang

引用次数: 2

Semi-Supervised Anomaly Detection Based on Deep Generative Models with Transformer 基于变压器深度生成模型的半监督异常检测

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529470

Weimin Shangguan, Wentao Fan, Ziyi Chen

{"title":"Semi-Supervised Anomaly Detection Based on Deep Generative Models with Transformer","authors":"Weimin Shangguan, Wentao Fan, Ziyi Chen","doi":"10.1145/3529466.3529470","DOIUrl":"https://doi.org/10.1145/3529466.3529470","url":null,"abstract":"In this work, we propose a novel semi-supervised anomaly detection approach based on deep generative models with Transformers for identifying unusual (abnormal) images from normal ones. Our approach is based on the combination of autoencoder (AE) and generative adversarial networks (GAN). Similar to the vanilla GAN, our model is mainly composed of the generator and discriminator. The generator adopts an encoder-decoderencoder structure to extract meaningful latent representations, in which each encoder is constructed by a Transformer whereas the decoder is realized through the transposed convolution. The discriminator, which is built upon another Transformer, is used to distinguish whether the given image comes from the generator or the training set, while optimizing the encoder in the generator for better latent representations through adversarial training. The distribution of the normal data can be learned by minimizing the gap between the original image space and the latent image space during the training process. The abnormal images are detected if their distributions are different from the learned normal distributions. The merits of the proposed anomaly detection approach are demonstrated by comparing it with other generative anomaly detection approaches through experiments on three benchmark image data sets.","PeriodicalId":375562,"journal":{"name":"Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126984402","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: 1

Fine-Grained Cross-Domain Recommendation via Two-Tier Attention and Three-Channel Learning 基于两层注意和三通道学习的细粒度跨领域推荐

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529487

Qinhang Xu, Jintao Tang, Ting Wang

{"title":"Fine-Grained Cross-Domain Recommendation via Two-Tier Attention and Three-Channel Learning","authors":"Qinhang Xu, Jintao Tang, Ting Wang","doi":"10.1145/3529466.3529487","DOIUrl":"https://doi.org/10.1145/3529466.3529487","url":null,"abstract":"Cross-Domain Recommendation (CDR) algorithms, aimed at alleviating the long-standing data sparsity and cold-start problems by transferring information collected from the source domains to the target domains, have attracted increasing attention recently. Other than ratings, existing works on CDR mostly consider side information like tags, reviews, contents etc., yet cannot make full use of texts (i.e. reviews and contents etc.) efficiently or fuse these side information with ratings deeply. Inspired by the advantages shown in review-based recommendations and aspect-based ones, we propose to model fine-grained user preference transfer at aspect level. To achieve this goal, we propose an end2end CDR framework via aspect transfer network with two-tier attention and three-channel learning (named TATCL). TATCL is devised to extract aspects to represent each user or item from their reviews by a review encoder and a subsequent user/item encoder with two-tier attention, and learn accurate aspect correlations across domains with three-channel learning. In addition, we enhance the user and item representation with auxiliary reviews and item contents. Experimental results on datasets demonstrate that, under certain condition, the proposed TATCL has superior predictive performance than existing models in terms of rating prediction accuracy.","PeriodicalId":375562,"journal":{"name":"Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130080618","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: 2

Petri net optimization technology of intelligent assembly line for complex process timing 智能装配线复杂工艺时序Petri网优化技术

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529474

Changmao Qin, Yanhua Lei, Pengfei Ren

引用次数: 0

Effective Community Detection Algorithm Based on Edge Influence Weight 基于边缘影响权的有效社区检测算法

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529495

Chang Wang, Yan Yang

引用次数: 0

One-Class Directed Heterogeneous Graph Neural Network for Intrusion Detection 一类有向异构图神经网络的入侵检测

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529480

Zeqi Huang, Yonghao Gu, Qing Zhao

{"title":"One-Class Directed Heterogeneous Graph Neural Network for Intrusion Detection","authors":"Zeqi Huang, Yonghao Gu, Qing Zhao","doi":"10.1145/3529466.3529480","DOIUrl":"https://doi.org/10.1145/3529466.3529480","url":null,"abstract":"The Host-based Intrusion Detection System (HIDS) is widely used to safeguard the security of the enterprise environment and the main detection target of HIDS is the provenance graph. HIDS makes extensive use of the provenance graph which models the interactions between processes and other system entities (e.g. files), to assign anomaly scores to the provenance graph based on expert experience. However, the nonlinear interactions on the provenance graph cannot be captured by expert experience. In addition, attack data is difficult to obtain in the field of intrusion detection. To tackle these problems, we propose OC-DHetGNN (One-Class Directed Heterogeneous Graph Neural Network), an unsupervised anomaly detection method for intrusion detection by combining heterogeneous graph neural networks with the one-class neural network. Specifically, we first model the provenance graph as the attributed heterogeneous graph. Then we propose a directed heterogeneous graph neural network module, which is used to obtain the embedding of the heterogeneous graph and the nodes. After that, the embedding of the heterogeneous graph and the embedding of the node are fed into two one-class neural network modules respectively to output the anomaly score. Extensive experiments on real enterprise data sets have verified OC-DHetGNN is superior to the baseline.","PeriodicalId":375562,"journal":{"name":"Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127780359","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: 2

Outlier Item Detection in Fashion Outfit 时尚服装中的异常项检测

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529472

Zhi Lu, Yang Hu, Yang Chen, B. Zeng

引用次数: 3

Multi-modal Intermediate Fusion Model for diagnosis prediction 诊断预测的多模态中间融合模型

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529496

You Lu, Ke Niu, Xueping Peng, Jingni Zeng, Su Pei

{"title":"Multi-modal Intermediate Fusion Model for diagnosis prediction","authors":"You Lu, Ke Niu, Xueping Peng, Jingni Zeng, Su Pei","doi":"10.1145/3529466.3529496","DOIUrl":"https://doi.org/10.1145/3529466.3529496","url":null,"abstract":"The goal of the diagnostic prediction task is to predict what disease patients are likely to have at their next visit, based on their historical electronic medical records. Existing studies mainly conduct the prediction task by separately using discrete medical codes or clinical notes. However, few existing studies fuse multi-modal features from medical codes and clinical notes together for diagnostic prediction. Practically, using multiple modes of EHRs data can obtain more complete patient representation to improve the predictive performance of the model. Therefore, we proposed a Multi-modal intermediate Fusion Model (MFM) to predict patient diagnosis based on diagnostic codes and clinical notes. Specifically, MFM is mainly based on recurrent neural network to model data in different modes to extract effective features. Then, an intermediate fusion module is used to not only extract the global context information of data in each mode, but also capture the correlation between data in different modes. Finally, a multi-modal fusion matrix is generated for diagnosis prediction. Experimental results on a real dataset show that the proposed method improves the prediction performance compared with the baseline methods.","PeriodicalId":375562,"journal":{"name":"Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132776944","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: 1