Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence最新文献_第4页

ECA-CBAM: Classification of Diabetic Retinopathy: Classification of diabetic retinopathy by cross-combined attention mechanism ECA-CBAM:糖尿病视网膜病变的分类:交叉联合注意机制对糖尿病视网膜病变的分类

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529468

Xiaohui Li, Haiying Xia, Lidan Lu

引用次数: 6

BGAT: A Multi Information Fusion Drug Repurposing Framework Based on Graph Convolutional Network 基于图卷积网络的多信息融合药物再利用框架

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529498

Dingan Sun, Zhao-hui Wang, Shuai Jiang, Wei Huang

{"title":"BGAT: A Multi Information Fusion Drug Repurposing Framework Based on Graph Convolutional Network","authors":"Dingan Sun, Zhao-hui Wang, Shuai Jiang, Wei Huang","doi":"10.1145/3529466.3529498","DOIUrl":"https://doi.org/10.1145/3529466.3529498","url":null,"abstract":"Traditional drug research and development is time-consuming, expensive and low success rate. Computational drug repurposing method can find the possible drug-disease associations quickly and systematically, which is of great significance for clinical research. In recent studies, computational drug repurposing is regarded as the prediction of drug-disease link. The biological function is more and more used to interpret biological significance. According to our research, biological function data has not been used in the research of drug repurposing, but it has practical research significance.Therefore, we implement an information fusion model BGAT based on drug/disease-target, protein-biological function and PPI. BGAT model uses the fusion of multiple bipartite graph convolution networks to effectively fuse various types of data information, and deeply extract protein features to update the hidden embedding representation of drugs nodes, disease nodes and biological functions nodes. Then the BGAT model scores the drug-disease pair through the improved multilayer perceptron BMLP to accurately predict the drug-disease associations. The superiority and practicability of our model are verified by comparing with the existing dominant algorithms BiFusion, NeoDTI, and baseline algorithms that include SVM and random forest.","PeriodicalId":375562,"journal":{"name":"Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence","volume":"93 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":"126048463","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

Analysis and comparison of improved artificial potential field method and A* in complex obstacle environment 复杂障碍环境下改进人工势场法与A*法的分析比较

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529490

Jiading Yang

引用次数: 0

A Novel Knowledge Tracing Model Based on Collaborative Multi-Head Attention 一种基于协同多头注意的知识追踪模型

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529477

Wei Zhang, Kaiyuan Qu, Yahui Han, Longan Tan

{"title":"A Novel Knowledge Tracing Model Based on Collaborative Multi-Head Attention","authors":"Wei Zhang, Kaiyuan Qu, Yahui Han, Longan Tan","doi":"10.1145/3529466.3529477","DOIUrl":"https://doi.org/10.1145/3529466.3529477","url":null,"abstract":"Online education is playing a more and more important role in today's education. The key link of online education is to model students' knowledge mastery according to their historical behaviors, so as to obtain the knowledge tracing represented by students' current knowledge state. Previous Transformer-based knowledge tracing models have disadvantages such as inefficient model computation and redundant information on the one hand. On the other hand, the traditional knowledge tracing model cannot solve the problem of imbalanced positive and negative samples in the data well. In order to better model the current knowledge state of students, this paper proposes a knowledge tracing model based on the collaborative multi-head attention mechanism. The model uses a collaborative multi-head attention mechanism to solve the information redundancy problem in the previous Transformer-based knowledge tracing model, and improves the computational efficiency and performance of the model. The model also introduces a focal loss function, which not only solves the problem of imbalanced question labeling divisions in knowledge tracing but also improves the differentiation of difficulty level among the questions and enhances the accuracy of model prediction. The experimental results on three public experimental datasets show that the knowledge tracing model based on the collaborative multi-head attention mechanism proposed in this paper outperforms other recent knowledge tracing models in terms of evaluation metric AUC and also has better performance in predicting students' responses.","PeriodicalId":375562,"journal":{"name":"Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence","volume":"115 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":"124164082","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

Fast Gradient Scaled Method for Generating Adversarial Examples 生成对抗性示例的快速梯度缩放方法

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529497

Zhefeng Xu, Zhijian Luo, Jinlong Mu

引用次数: 0

Transformer-based Question Text Generation in the Learning System 学习系统中基于转换的问题文本生成

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529484

Jiajun Li, Huazhu Song, Jun Li

引用次数: 1

Heterogeneous Collaborative Refining for Real-Time End-to-End Image-Text Retrieval System 面向实时端到端图像-文本检索系统的异构协同精炼

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529486

Nan Guo, Min-Uk Yang, Xiaoping Chen, Xiao Xiao, Chenhao Wang, Xiaochun Ye, Dongrui Fan

引用次数: 0

An Improved Crowd Aggregation Prediction Algorithm Based on ARMA 一种改进的基于ARMA的人群聚集预测算法

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529491

Zhan Gao, Yang Chen, Zhiyong Li, Tao Li, Junjiang He, Yuehao Li

引用次数: 0

Dynamic Time Series Data Reduction for NILM Appliance Identification 动态时间序列数据约简用于NILM电器识别

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2022-03-04 DOI: 10.1145/3529466.3529489

Saad Tariq, K. Sim, K. Sim

引用次数: 0

Parallel Physics-Informed Neural Networks with Bidirectional Balance 具有双向平衡的并行物理信息神经网络

Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence Pub Date : 2021-11-10 DOI: 10.1145/3529466.3529467

Yuhao Huang

{"title":"Parallel Physics-Informed Neural Networks with Bidirectional Balance","authors":"Yuhao Huang","doi":"10.1145/3529466.3529467","DOIUrl":"https://doi.org/10.1145/3529466.3529467","url":null,"abstract":"As an emerging technology in deep learning, physics-informed neural networks (PINNs) have been widely used to solve various partial differential equations (PDEs) in engineering. However, PDEs based on practical considerations contain multiple physical quantities and complex initial boundary conditions, thus PINNs often returns incorrect results. Here we take heat transfer problem in multilayer fabrics as a typical example. It is coupled by multiple temperature fields with strong correlation, and the values of variables are extremely unbalanced among different dimensions. We clarify the potential difficulties of solving such problems by classic PINNs, and propose a parallel physics-informed neural networks with bidirectional balance. In detail, our parallel solving framework synchronously fits coupled equations through several multilayer perceptron. Moreover, we design two modules to balance forward process of data and back-propagation process of loss gradient. This bidirectional balance not only enables the whole network to converge stably, but also helps to fully learn various physical conditions in PDEs. We provide a series of ablation experiments to verify the effectiveness of the proposed methods. The results show that our approach makes the PINNs unsolvable problem solvable, and achieves excellent solving accuracy.","PeriodicalId":375562,"journal":{"name":"Proceedings of the 2022 6th International Conference on Innovation in Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130402231","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