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Human integration in an ontology-based IoT system 基于本体的物联网系统中的人类集成
Studia Universitatis BabesBolyai Series Informatica Pub Date : 2021-10-21 DOI: 10.34739/si.2020.24.04
Kamil Skarżyński, Waldemar Bartyna, M. Stępniak
{"title":"Human integration in an ontology-based IoT system","authors":"Kamil Skarżyński, Waldemar Bartyna, M. Stępniak","doi":"10.34739/si.2020.24.04","DOIUrl":"https://doi.org/10.34739/si.2020.24.04","url":null,"abstract":"The IoT systems are growing field of automation. In contrast to industrial applications, where the system is custom made for each customer or use case, the home IoT systems can be composed and used in many, sometimes dangerous and unpredictable, ways. This paper presents a system that is based on a common ontology as a unified and universal method of representing the environment including humans. Such approach allows for easy integration of heterogenous devices and declarative definition of services, tasks, and rules ensuring human safety and/or comfort.","PeriodicalId":30071,"journal":{"name":"Studia Universitatis BabesBolyai Series Informatica","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78793078","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
ICT security in tax administration - Rapid7 Nexpose vulnerability analysis 税务管理中的信息通信技术安全- Rapid7暴露漏洞分析
Studia Universitatis BabesBolyai Series Informatica Pub Date : 2021-10-21 DOI: 10.34739/si.2020.24.03
Tomasz Muliński
{"title":"ICT security in tax administration - Rapid7 Nexpose vulnerability analysis","authors":"Tomasz Muliński","doi":"10.34739/si.2020.24.03","DOIUrl":"https://doi.org/10.34739/si.2020.24.03","url":null,"abstract":"The article focuses on the subject of IT security in tax administration. This study presents the research on the security level of endpoints, servers, printing devices, network switches and other ICT devices using the Rapid Nexpose vulnerability scanner. We discuss the specifics of security research in public administration resulting from the laws in force in these institutions.","PeriodicalId":30071,"journal":{"name":"Studia Universitatis BabesBolyai Series Informatica","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82193757","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
Object classification with artificial neural networks: A comparative analysis 用人工神经网络进行对象分类的比较分析
Studia Universitatis BabesBolyai Series Informatica Pub Date : 2020-12-22 DOI: 10.34739/si.2019.23.03
A. Niewiadomski, Kornel Domeradzki
{"title":"Object classification with artificial neural networks: A comparative analysis","authors":"A. Niewiadomski, Kornel Domeradzki","doi":"10.34739/si.2019.23.03","DOIUrl":"https://doi.org/10.34739/si.2019.23.03","url":null,"abstract":"Object classification is a problem which has attracted a lot of research attention in recent years. Traditional approach to this problem is built on a shallow trainable architecture that was meant to detect handcrafted features. That approach works poorly and introduces many complications in situations where one is to work with more than a couple types of objects in an image with a large resolution. That is why in the past few years convolutional and residual neural networks have experienced a tremendous rise in popularity. In this paper, we provide a review on topics related to artificial neural networks and a brief overview of our research. Our review begins with a short introduction to the topic of computer vision. Afterwards we cover briefly the concepts of neural networks, convolutional and residual neural networks and their commonly used models. Then we provide a comparative performance analysis of the previously mentioned models in a binary and multi-label classification problem. Finally, multiple conclusions are drawn, which are to serve as guidelines for future computer vision systems implementations.","PeriodicalId":30071,"journal":{"name":"Studia Universitatis BabesBolyai Series Informatica","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73618357","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
Studia Informatica 学习计算机
Studia Universitatis BabesBolyai Series Informatica Pub Date : 2020-12-22 DOI: 10.34739/si.2019.23
Mateusz Nowak
{"title":"Studia Informatica","authors":"Mateusz Nowak","doi":"10.34739/si.2019.23","DOIUrl":"https://doi.org/10.34739/si.2019.23","url":null,"abstract":"380 Skorowidz waŜniejszych oznaczeń i symboli 382","PeriodicalId":30071,"journal":{"name":"Studia Universitatis BabesBolyai Series Informatica","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88316237","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: 5
The Influence of the Artificial Neural Network type on the quality of learning on the Day-Ahead Market model at Polish Power Exchange joint-stock company 人工神经网络类型对波兰电力交易所股份公司日前市场模型学习质量的影响
Studia Universitatis BabesBolyai Series Informatica Pub Date : 2020-12-22 DOI: 10.34739/si.2019.23.05
Dariusz Ruciński
{"title":"The Influence of the Artificial Neural Network type on the quality of learning on the Day-Ahead Market model at Polish Power Exchange joint-stock company","authors":"Dariusz Ruciński","doi":"10.34739/si.2019.23.05","DOIUrl":"https://doi.org/10.34739/si.2019.23.05","url":null,"abstract":"The work contains the results of the Day-Ahead Market modeling research at Polish Power Exchange taking into account the numerical data on the supplied and sold electricity in selected time intervals from the entire period of its operation (from July 2002 to June 2019). Market modeling was carried out based on three Artificial Neural Network models, ie: Perceptron Artificial Neural Network, Recursive Artificial Neural Network, and Radial Artificial Neural Network. The examined period of the Day-Ahead Market operation on the Polish Power Exchange was divided into sub-periods of various lengths, from one month, a quarter, a half a year to the entire period of the market's operation. As a result of neural modeling, 1,191 models of the Market system were obtained, which were assessed according to the criterion of the least error MSE and the determination index R2.","PeriodicalId":30071,"journal":{"name":"Studia Universitatis BabesBolyai Series Informatica","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85367172","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
Database security: combining neural networks and classification approach 数据库安全:结合神经网络和分类方法
Studia Universitatis BabesBolyai Series Informatica Pub Date : 2020-12-22 DOI: 10.34739/si.2019.23.06
J. Skaruz
{"title":"Database security: combining neural networks and classification approach","authors":"J. Skaruz","doi":"10.34739/si.2019.23.06","DOIUrl":"https://doi.org/10.34739/si.2019.23.06","url":null,"abstract":"In the paper we present a new approach based on application of neural networks to detect SQL attacks. SQL attacks are those attacks that take the advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction problem. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker, we divide SQL statements into tokens and pass them to our detection system, which predicts the next token, taking into account previously seen tokens. In the learning phase tokens are passed to a recurrent neural network (RNN) trained by backpropagation through time (BPTT) algorithm. Then, two coefficients of the rule are evaluated. The rule is used to interpret RNN output. In the testing phase RNN with the rule is examined against attacks and legal data to find out how evaluated rule affects efficiency of detecting attacks. All experiments were conducted on Jordan network. Experimental results show the relationship between the rule and a length of SQL queries.","PeriodicalId":30071,"journal":{"name":"Studia Universitatis BabesBolyai Series Informatica","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90914924","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
Comparative Study on Game Engines 游戏引擎的比较研究
Studia Universitatis BabesBolyai Series Informatica Pub Date : 2020-12-22 DOI: 10.34739/si.2019.23.01
A. Barczak, Hubert Woźniak
{"title":"Comparative Study on Game Engines","authors":"A. Barczak, Hubert Woźniak","doi":"10.34739/si.2019.23.01","DOIUrl":"https://doi.org/10.34739/si.2019.23.01","url":null,"abstract":"Game engines are platforms that make it easier to create computer games. They allow you to integrate and combine into single unit individual game elements such as animations, interaction with the user, or detection of collisions between objects.\u0000Game engine is a software that facilitates the production of games for platforms such as desktops, consoles, and mobile devices [1]. In addition to specialized editors, game engines provide ready-made functionalities that can be used by users. A great advantage of using the game engine is the possibility of their reuse. Game engines are an example of software isolating rigid logic and game rules, from easily modifiable and expandable components, which can be used repeatedly in a way that does not require great modifications.\u0000This article is an attempt to perform a comparative analysis of three engines to create games: CryEngine, Unreal Engine, and Unity. The criteria for comparison will be technical capabilities and factors influencing the popularity and acceptance of engines by users. The aim of this article is to show the strengths and weaknesses of engines and to present the differences between these tools.","PeriodicalId":30071,"journal":{"name":"Studia Universitatis BabesBolyai Series Informatica","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78876441","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
Machine Learning Methods in E-mail Spam Classification 垃圾邮件分类中的机器学习方法
Studia Universitatis BabesBolyai Series Informatica Pub Date : 2020-12-22 DOI: 10.34739/si.2019.23.04
P. Switalski, Mateusz Kopówka
{"title":"Machine Learning Methods in E-mail Spam Classification","authors":"P. Switalski, Mateusz Kopówka","doi":"10.34739/si.2019.23.04","DOIUrl":"https://doi.org/10.34739/si.2019.23.04","url":null,"abstract":"Increasing number of unwanted e-mails has influence on users’ security in the Internet. Today spam e-mails can store potential malicious messages which e.g. can redirect user to fake sites. These messages recently appeared in social media. Filtering of this content is important due to minimize financial and branding costs. Traditional methods of spam filtering cannot be sufficient for present threats. We required new methods for constructing more dependable and robust antispam filters. Machine learning recently becomes very popular technique in classification methods. It has been successfully used in spam classification. In this paper we present some methods of machine learning for spam detecting. We would also like to introduce ways to solve the spam classification problem. We show that these methods can be useful in classification of malicious messages. We also compared developed methods and presented results in the experimental section.","PeriodicalId":30071,"journal":{"name":"Studia Universitatis BabesBolyai Series Informatica","volume":"247 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74968018","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
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