2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)最新文献_第2页

Distance Function Selection for Multivariate Time-Series 多元时间序列的距离函数选择

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00021

Gleb Morgachev, A. Goncharov, V. Strijov

引用次数: 0

The Robosoccer as a Modern Educational Platform in the Field of Artificial Intelligence 机器人足球作为人工智能领域的现代教育平台

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00019

R. Gorbachev, S. Semendyaev, Ivan Khokhlov, Vladimir Litvinenko, Ilya Ryakin

引用次数: 2

[Title page i] [标题页i]

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/ic-aiai48757.2019.00001

引用次数: 0

Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm 基于深度神经网络和匹配算法的视频交通标志识别

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00013

I. Belkin, S. Tkachenko, D. Yudin

{"title":"Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm","authors":"I. Belkin, S. Tkachenko, D. Yudin","doi":"10.1109/IC-AIAI48757.2019.00013","DOIUrl":"https://doi.org/10.1109/IC-AIAI48757.2019.00013","url":null,"abstract":"The paper analyzes data sets containing images with labeled traffic signs, as well as modern approaches for their detection and classification on images of urban scenes. Particular attention is paid to the recognition of Russian types of traffic signs. Various modern architectures of deep neural networks for the simultaneous object detection and classification were studied, including Faster R-CNN, Mask R-CNN, Cascade R-CNN, RetinaNet. To increase the efficiency of neural network recognition of objects in a video sequence, the Seq-BBox Matching algorithm is used. Training and testing of the proposed approach was carried out on Russian Traffic Sign Dataset and IceVision Dataset containing over 150 types of road signs and more than 65,000 marked images. For all the approaches considered, quality metrics are defined: mean average precision mAP, mean average recall mAR and processing time of one frame. The highest quality performance was demonstrated by the architecture of Faster R-CNN with Seq-BBox Matching, while the highest performance is provided by the architecture of RetinaNet. Implementation was carried out using the Python 3.7 programming language and PyTorch deep learning library using NVidia CUDA technology. Performance indicators were obtained on the workstation with the NVidia Tesla V-100 32GB video card. The obtained results demonstrate the possibility of applying the proposed approach both for the resource-intensive procedure for automated labeling of road scene images for new data sets preparation, and for traffic sign recognition in on-board computer vision systems of unmanned vehicles.","PeriodicalId":374193,"journal":{"name":"2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123215877","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: 3

The Expert System for Generating Front-End Code 前端代码生成专家系统

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00027

Tatjana Stojanović, Sasa Lazarevic

引用次数: 0

PhysTech Development Strategy — 2024 物理科技发展战略- 2024年

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00007

S. Garichev

{"title":"PhysTech Development Strategy — 2024","authors":"S. Garichev","doi":"10.1109/IC-AIAI48757.2019.00007","DOIUrl":"https://doi.org/10.1109/IC-AIAI48757.2019.00007","url":null,"abstract":"In 2017, the Government of the Russian Federation approved the National Program \"Digital Economy of the Russian Federation\". Five basic directions for the development of the digital economy in Russia for the period until 2024 were identified. To implement the decision of the Government of the Russian Federation, a competitive selection was held for the organization and state support of the centers of the National Technological Initiative on the basis of educational organizations of higher education and scientific organizations. As a result of the competitive selection, it was decided to create the Center for the National Technological Initiative in the field of \"Artificial Intelligence\" on the basis of the Moscow Institute of Physics and Technology (MIPT). National Research University MIPT is among the 50 best universities in the world in natural sciences (THE). The article sets out the main points of the development strategy of the Center for the National Technological Initiative \"Artificial Intelligence\" for the period up to 2024. To implement the Program in the field of artificial intelligence, a consortium was formed, which included MIPT partners: enterprises of the real sector of the Russian economy, leading research organizations and National Research Universities. The center is also interested in collaborating with foreign companies and universities and implementing joint research and educational projects.","PeriodicalId":374193,"journal":{"name":"2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)","volume":"440 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121461471","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

Application of Similarity Metrics in Collaborative Filtering Based Recommendation Systems 相似度度量在协同过滤推荐系统中的应用

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00024

Igor Radisic, Sasa Lazarevic

引用次数: 0

Domain Specific word Embedding Matrix for Training Neural Networks 训练神经网络的领域特定词嵌入矩阵

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00022

Dorde Petrovic, S. Janicijevic

引用次数: 4

Domain Dependence of Definitions Required to Standardize and Compare Performance Characteristics of Weak AI Systems 标准化和比较弱人工智能系统性能特征所需定义的领域依赖性

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00020

A. Kuleshov, Sergei P. Prokhorov

引用次数: 1

An Application of Artificial Intelligence for Detecting Emotions in Neuromarketing 人工智能在神经营销中的应用

2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI) Pub Date : 2019-09-01 DOI: 10.1109/IC-AIAI48757.2019.00016

Filip Filipović, M. Despotović-Zrakić, B. Radenkovic, B. Jovanic, L. Živojinović

{"title":"An Application of Artificial Intelligence for Detecting Emotions in Neuromarketing","authors":"Filip Filipović, M. Despotović-Zrakić, B. Radenkovic, B. Jovanic, L. Živojinović","doi":"10.1109/IC-AIAI48757.2019.00016","DOIUrl":"https://doi.org/10.1109/IC-AIAI48757.2019.00016","url":null,"abstract":"The subject of this paper is the application of artificial intelligence for detecting emotions in neuromarketing. The goal is to enable the identification of user emotions through a webcam, using convolutional neural networks. The first part of the paper describes the neural networks, the basic types, and their differences. The greatest attention has been given to the description and application of convolutional neural networks. A Convolutional Neural Network, also known as CNN, is specialized in processing data that has a grid-like topology, such as an image. User emotion recognition is enabled using the face-api.js library. It implements the following models: SSD Mobilenet V1, Tiny Face Detector and MTCNN. Tiny Face Detector, used in the application, is a model for real-time face detection with small size, speed, and moderate resource consumption. The model is compatible with the web and mobile platforms. In the second part of the paper, an application was developed, which uses the face-api.js library to detect emotions. It has been developed as a tool to support neuromarketing research. It allows the marketer to create research to analyze advertising material. Its basic functionality is to display advertising content and collect data while watching. Data is stored and graphically displayed to the marketer. This section describes in detail how the detection process works. In the third part of the paper, evaluation was made. Evaluation of the developed solution was performed by experiment. The results show that the emotions of the user can be recognized by the developed system, with a satisfactory level of precision. The advertising content has previously entered parameters, which represent the desired results. By comparing these parameters and the obtained results, the marketer decides whether the advertisement is successful.","PeriodicalId":374193,"journal":{"name":"2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124020458","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: 6