{"title":"Knowledge Model and Ontology for Security Services","authors":"Oleksii Kovalenko, Taras Kovalenko","doi":"10.1109/SAIC.2018.8516875","DOIUrl":"https://doi.org/10.1109/SAIC.2018.8516875","url":null,"abstract":"The paper reviews current state in security management methods and standards. Key aspects of security management are pointed. The concept of mutual dependence on risks, threats and security architecture is proposed. The structure and components of formal knowledge model for security services realization are outlined. Promising directions for future research are outlined.","PeriodicalId":157794,"journal":{"name":"2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127147665","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}
{"title":"Application of Cellular Automates in Some Models of Artificial Intelligence","authors":"O. Makarenko, V. Osaulenko","doi":"10.1109/SAIC.2018.8516837","DOIUrl":"https://doi.org/10.1109/SAIC.2018.8516837","url":null,"abstract":"The problem of modeling many natural phenomena is too complex and requires interaction on different scales. Here we show that one of the possible and convineient way to approach this problem with hierarchical cellular automata. The peculiarity of this approach is that the state of the cell is modeled by another cellular automaton with as many recursive layers as needed. We make an example of how the “Smart cube”, as we called it, can be used for simulating such a complex system as the brain. Different levels from neurotransmitters to large-scale neural population interaction can be represented into one model of cellular automata. Furthermore, this allows to experiment with different learning rules and incorporate many interesting mechanisms like anticipation.","PeriodicalId":157794,"journal":{"name":"2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125411707","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}
M. Zgurovsky, V. Putrenko, Iryna Dzhygyrey, Andrey Boldak, K. Yefremov, Nataliia Pashynska, I. Pyshnograiev, Sergiy Nazarenko
{"title":"Parameterization of Sustainable Development Components Using Nightlight Indicators in Ukraine","authors":"M. Zgurovsky, V. Putrenko, Iryna Dzhygyrey, Andrey Boldak, K. Yefremov, Nataliia Pashynska, I. Pyshnograiev, Sergiy Nazarenko","doi":"10.1109/SAIC.2018.8516726","DOIUrl":"https://doi.org/10.1109/SAIC.2018.8516726","url":null,"abstract":"The paper deals with the question of establishing dependencies between the data of night satellite monitoring and indicators of sustainable development on the example of the territory of Ukraine. The method of obtaining zonal statistics data for administrative units of different levels is described, based on the estimation of illumination in the night in the longterm period. The use of local correlation and regression indicators is grounded to establish spatial differences in the tightness of the dependence between nightlight luminocity and indicators of sustainable development.","PeriodicalId":157794,"journal":{"name":"2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124707763","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}
{"title":"Unsupervised Pre-Training with Spiking Neural Networks in Semi-Supervised Learning","authors":"Yaroslav Dorogyy, V. Kolisnichenko","doi":"10.1109/SAIC.2018.8516733","DOIUrl":"https://doi.org/10.1109/SAIC.2018.8516733","url":null,"abstract":"Semi-supervised learning appeared when people understood that ignoring the possibility of getting benefit from unlabeled data during supervised learning with little labeled data is not wise. It is difficult to overestimate the importance of this as much more unlabeled data exists than labeled and much easier it can be collected. Many semi-supervised learning methods were developed over the past decades. A new approach is presented in this paper, which is based on using spiking neural networks in the pre-training phase. Spiking neural networks are biologically plausible neural networks, which try to simulate the behavior of neurons and processes which occur in biological neural networks. Most of the learning rules used in spiking neural networks are unsupervised as unsupervised learning is thought to be a major drive for developmental plasticity in the brain. It is considered that it is hard for the brain to do supervised learning, things like doing math or classification. In a nutshell, the proposed method is a combination of the advantages of both spiking neuron networks (unsupervised learning) and classical artificial neural networks (supervised learning). We showed that such approach may increase the accuracy of the classifier when a small amount of labeled data is given.","PeriodicalId":157794,"journal":{"name":"2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC)","volume":"os-25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127772417","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}
M. Petrova, O. Sushchenko, I. Trunina, N. Dekhtyar
{"title":"Big Data Tools in Processing Information from Open Sources","authors":"M. Petrova, O. Sushchenko, I. Trunina, N. Dekhtyar","doi":"10.1109/SAIC.2018.8516800","DOIUrl":"https://doi.org/10.1109/SAIC.2018.8516800","url":null,"abstract":"The article describes the main fields of big data tools implementation in socially economic researches, demonstrates the process of a query construction for data retrieval from social networks on the example of Twitter using Twitter API and Python Libraries, further information processing via the selected parameters and illustrates the possibility of data visualisation using standard charts.","PeriodicalId":157794,"journal":{"name":"2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116733596","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}
{"title":"Human Action Recognition Using Fusion of Modern Deep Convolutional and Recurrent Neural Networks","authors":"Dmytro Tkachenko","doi":"10.29007/WJ5T","DOIUrl":"https://doi.org/10.29007/WJ5T","url":null,"abstract":"This paper studies the application of modern deep convolutional and recurrent neural networks to video classification, specifically human action recognition. Multi-stream architecture, which uses the ideas of representation learning to extract embeddings of multimodal features, is proposed. It is based on 2D convolutional and recurrent neural networks, and the fusion model receives a video embedding as input. Thus, the classification is performed based on this compact representation of spatial, temporal and audio information. The proposed architecture achieves 93.1 % accuracy on UCF101, which is better than the results obtained with the models that have a similar architecture, and also produces representations which can be used by other models as features; anomaly detection using autoencoders is proposed as an example of this.","PeriodicalId":157794,"journal":{"name":"2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC)","volume":"40 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124385744","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}