Pub Date : 2019-09-01DOI: 10.1109/idaacs.2019.8924229
{"title":"[Adverticements]","authors":"","doi":"10.1109/idaacs.2019.8924229","DOIUrl":"https://doi.org/10.1109/idaacs.2019.8924229","url":null,"abstract":"","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"186 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":"123264878","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924370
Veska Gancheva, P. Borovska
The volume of stored genomic data has increased significantly in the recent years. Main challenge in their analysis and knowledge discovery is to suggest advanced and efficient tools, methods and technologies for access and processing. SOA based system for adaptive knowledge discovery and decision making based on big genomic data analytics is proposed in this paper. The system architecture is comprised of web services for data integration, preprocessing of large data streams, knowledge discovery based on genomic data analytics, knowledge interpretation and results visualization. The functionality of the developed system is explained. A web service for breast cancer data processing has been developed for the purpose of system testing and validation. The proposed system architecture allows scientists an easy, fast and flexible approach for data processing. They can choose the services they wish to be executed, use the available data sets in databases, or enter their own data to be processed.
{"title":"SOA Based System for Big Genomic Data Analytics and Knowledge Discovery","authors":"Veska Gancheva, P. Borovska","doi":"10.1109/IDAACS.2019.8924370","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924370","url":null,"abstract":"The volume of stored genomic data has increased significantly in the recent years. Main challenge in their analysis and knowledge discovery is to suggest advanced and efficient tools, methods and technologies for access and processing. SOA based system for adaptive knowledge discovery and decision making based on big genomic data analytics is proposed in this paper. The system architecture is comprised of web services for data integration, preprocessing of large data streams, knowledge discovery based on genomic data analytics, knowledge interpretation and results visualization. The functionality of the developed system is explained. A web service for breast cancer data processing has been developed for the purpose of system testing and validation. The proposed system architecture allows scientists an easy, fast and flexible approach for data processing. They can choose the services they wish to be executed, use the available data sets in databases, or enter their own data to be processed.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"13 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":"122232100","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924299
Muhammad Muneeb Saad, Talha Iqbal, Hazrat Ali, Mohammad Farhad Bulbul, Shahid Khan, C. Tanougast
Artificial Intelligence (AI) techniques provide many intelligent methods for security solutions in various domains such as finance, networking, cloud computing, health records and individual's identity. AI achieves security mechanisms like antivirus, firewalls, intrusion detection system (IDS) and cryptography by using machine learning methods and data analysis techniques. As the modern AI techniques help improving security systems, criminal activities are also becoming updated simultaneously. Machine learning methods along with data analysis tools have become popular to prevent security systems from threats and hacking activities. This work contributes to secure cloud networks and help them prevent malicious attacks. In this paper, Bidirectional long short-term memory (BLSTM) is used to detect incidents over unified threat management (UTM) platform operated on cloud network. Results are compared with K-nearest neighbor which is a baseline technique. Time series input samples recorded over UTM platform are used for training and testing purposes. We obtain accuracy score of 98.47% with 0.0186 mean squared error (MSE) using KNN while BLSTM provides 98.6% accuracy score with 0.002 loss, which is better than the KNN.
人工智能(AI)技术为金融、网络、云计算、健康记录和个人身份等各个领域的安全解决方案提供了许多智能方法。人工智能通过机器学习方法和数据分析技术来实现防病毒、防火墙、入侵检测系统(IDS)和密码学等安全机制。随着现代人工智能技术帮助改善安全系统，犯罪活动也在同步更新。机器学习方法和数据分析工具已经变得流行，以防止安全系统受到威胁和黑客活动。这项工作有助于确保云网络的安全，并帮助它们防止恶意攻击。本文将双向长短期记忆(Bidirectional long - short- memory, BLSTM)用于云网络统一威胁管理(unified threat management, UTM)平台上的事件检测。将结果与基线技术k近邻进行比较。在UTM平台上记录的时间序列输入样本用于培训和测试目的。我们使用KNN获得98.47%的准确率分数，均方误差(MSE)为0.0186，而使用BLSTM获得98.6%的准确率分数，损失为0.002，优于KNN。
{"title":"Incident Detection over Unified Threat Management Platform on a Cloud Network","authors":"Muhammad Muneeb Saad, Talha Iqbal, Hazrat Ali, Mohammad Farhad Bulbul, Shahid Khan, C. Tanougast","doi":"10.1109/IDAACS.2019.8924299","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924299","url":null,"abstract":"Artificial Intelligence (AI) techniques provide many intelligent methods for security solutions in various domains such as finance, networking, cloud computing, health records and individual's identity. AI achieves security mechanisms like antivirus, firewalls, intrusion detection system (IDS) and cryptography by using machine learning methods and data analysis techniques. As the modern AI techniques help improving security systems, criminal activities are also becoming updated simultaneously. Machine learning methods along with data analysis tools have become popular to prevent security systems from threats and hacking activities. This work contributes to secure cloud networks and help them prevent malicious attacks. In this paper, Bidirectional long short-term memory (BLSTM) is used to detect incidents over unified threat management (UTM) platform operated on cloud network. Results are compared with K-nearest neighbor which is a baseline technique. Time series input samples recorded over UTM platform are used for training and testing purposes. We obtain accuracy score of 98.47% with 0.0186 mean squared error (MSE) using KNN while BLSTM provides 98.6% accuracy score with 0.002 loss, which is better than the KNN.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"59 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":"115222268","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924281
Yevgeniy V. Bodyanskiy, O. Boiko, I. Pliss, V. Volkova
In the paper, the 2D-deep neural network and the algorithm for its online learning are proposed. This system allows reducing the number of adjustable weights due to the rejection of the vectorization-devectorization operations. As a result, it saves the information that is contained between columns and rows of data inputs presented as 2D matrix.
{"title":"2D-Deep Neural Network and Its Online Rapid Learning","authors":"Yevgeniy V. Bodyanskiy, O. Boiko, I. Pliss, V. Volkova","doi":"10.1109/IDAACS.2019.8924281","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924281","url":null,"abstract":"In the paper, the 2D-deep neural network and the algorithm for its online learning are proposed. This system allows reducing the number of adjustable weights due to the rejection of the vectorization-devectorization operations. As a result, it saves the information that is contained between columns and rows of data inputs presented as 2D matrix.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"24 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":"123861631","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924336
Hao Xiao, Yanming Fan, Zhang Zhang, Xin Cheng
This paper presents a fast and accurate edge detection algorithm for real-time autonomous optical navigation used in deep-space missions. The proposed algorithm optimizes the non-maximum suppression (NMS) mechanism and the adaptive threshold selection approach of the conventional Canny algorithm. Instead of computing gradient directions, the proposed NMS approach adopts the vertical and horizontal gradients to determine the diagonal directions of gradient directions. In addition, an optimized noise edge suppression mechanism is presented for getting thinner edges without sacrificing the performance in terms of computation complexity. Furthermore, unlike the conventional double-thresholding method, this paper proposes a single-threshold selection approach, thus reducing the computational complexity and easing the real-time embedded implementation. More importantly, the proposed single-threshold scheme can efficiently suppress the noise edges caused by craters and atmosphere covered on celestial bodies. Experimental results show that, compared with the traditional Canny edge detector, the proposed algorithm enables more accurate celestial body edge detection, while reducing a lot of computation complexity.
{"title":"A Fast and Accurate Edge Detection Algorithm for Real-Time Deep-Space Autonomous Optical Navigation","authors":"Hao Xiao, Yanming Fan, Zhang Zhang, Xin Cheng","doi":"10.1109/IDAACS.2019.8924336","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924336","url":null,"abstract":"This paper presents a fast and accurate edge detection algorithm for real-time autonomous optical navigation used in deep-space missions. The proposed algorithm optimizes the non-maximum suppression (NMS) mechanism and the adaptive threshold selection approach of the conventional Canny algorithm. Instead of computing gradient directions, the proposed NMS approach adopts the vertical and horizontal gradients to determine the diagonal directions of gradient directions. In addition, an optimized noise edge suppression mechanism is presented for getting thinner edges without sacrificing the performance in terms of computation complexity. Furthermore, unlike the conventional double-thresholding method, this paper proposes a single-threshold selection approach, thus reducing the computational complexity and easing the real-time embedded implementation. More importantly, the proposed single-threshold scheme can efficiently suppress the noise edges caused by craters and atmosphere covered on celestial bodies. Experimental results show that, compared with the traditional Canny edge detector, the proposed algorithm enables more accurate celestial body edge detection, while reducing a lot of computation complexity.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"91 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":"124859826","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924273
Feng Chen, Z. Ye, Jun Su, Haofeng Lang, Xiaoxiao Shi, Shuqing Wang
In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric load forecasting are not proper enough. A short-term electric load forecasting method based on grey neural network based on snap-drift cuckoo search optimization algorithm(SDCS-GNN) is proposed in this paper. Parameters of gray neural network (GNN) are selected randomly which is similar to the initial spatial position of birds' eggs in the parasitic nest of cuckoo. The SDCS is utilized to search the better weight and threshold of the conventional gray neural network (GNN), which improves the stability and accuracy of the prediction model. To validate the superior performance of the proposed method, several well-known evolutionary algorithms such as particle swarm optimization (PSO), grey wolf optimization(GWO), moth-fire suppression optimization(MFO) and cuckoo search optimization (CS) are employed to constitute the contrast experiment of the prediction of short-term power load. The mean squared error predicted by the SDCS-GNN model is the smallest, which compared with GNN, PSO-GNN, GWO-GNN, MFO-GNN, and CS-GNN is 0.36, 1.79, 15.23, 4.53, 2.93, respectively. The Average prediction accuracy of SDCS-GNN model is better than other models which is 7.1592, 1.427, 15.1516, 11.5438, 10.5202, respectively. The simulation results show that the SDCS-GNN model has better approximation ability and higher prediction accuracy than the conventional GNN and other evolutionary algorithms in the short-term electric load forecasting. The experiments above indicates that the prediction method is effective and feasible.
{"title":"Research On Short-term Electric Load Forecast Based On Grey Neural Network and Snap-drift Cuckoo Search Algorithm","authors":"Feng Chen, Z. Ye, Jun Su, Haofeng Lang, Xiaoxiao Shi, Shuqing Wang","doi":"10.1109/IDAACS.2019.8924273","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924273","url":null,"abstract":"In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric load forecasting are not proper enough. A short-term electric load forecasting method based on grey neural network based on snap-drift cuckoo search optimization algorithm(SDCS-GNN) is proposed in this paper. Parameters of gray neural network (GNN) are selected randomly which is similar to the initial spatial position of birds' eggs in the parasitic nest of cuckoo. The SDCS is utilized to search the better weight and threshold of the conventional gray neural network (GNN), which improves the stability and accuracy of the prediction model. To validate the superior performance of the proposed method, several well-known evolutionary algorithms such as particle swarm optimization (PSO), grey wolf optimization(GWO), moth-fire suppression optimization(MFO) and cuckoo search optimization (CS) are employed to constitute the contrast experiment of the prediction of short-term power load. The mean squared error predicted by the SDCS-GNN model is the smallest, which compared with GNN, PSO-GNN, GWO-GNN, MFO-GNN, and CS-GNN is 0.36, 1.79, 15.23, 4.53, 2.93, respectively. The Average prediction accuracy of SDCS-GNN model is better than other models which is 7.1592, 1.427, 15.1516, 11.5438, 10.5202, respectively. The simulation results show that the SDCS-GNN model has better approximation ability and higher prediction accuracy than the conventional GNN and other evolutionary algorithms in the short-term electric load forecasting. The experiments above indicates that the prediction method is effective and feasible.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"248 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":"122461843","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924290
Dawei Dong, Z. Ye, Yu Cao, Shiwei Xie, Fengwen Wang, Wei Ming
Discovering knowledge from the amount of data plays an important role in the era of big data and FP-Growth algorithm is one of the most successful methods for learning association rules. Though the FP-Growth algorithm only needs scan two times, it has a poor efficiency for large datasets. There are already efforts have been made to solve the problem by using some Meta-heuristic optimization algorithms, such as particle swarm optimization algorithm (PSO), immune algorithms etc, which outperform the traditional FP-Growth algorithm and shows strong performance. However, PSO is easy to trap in the local optimums. A novel algorithm ant lion optimizer (ALO) was proposed and with the advantages of global optimization, good robustness, and high convergence accuracy, which was applied to many engineering fields like antenna array synthesis, integrated process planning, scheduling and so on. In the paper, a novel association rule extraction algorithm is put forward based on the ant lion optimization algorithm. A new fitness schema based on confidence and support has been used in this approach, which avoids part of unnecessary searching processes of the FP-Growth algorithm and leads the method of searching the optimization solution more effectively. In order to evaluate the effectiveness of our approach, experiments on various datasets are carried out and experimental results are compared with some other classical meta-heuristic algorithms, experimental results testify the performance of the proposed method.
{"title":"An Improved Association Rule Mining Algorithm Based on Ant Lion Optimizer Algorithm and FP-Growth","authors":"Dawei Dong, Z. Ye, Yu Cao, Shiwei Xie, Fengwen Wang, Wei Ming","doi":"10.1109/IDAACS.2019.8924290","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924290","url":null,"abstract":"Discovering knowledge from the amount of data plays an important role in the era of big data and FP-Growth algorithm is one of the most successful methods for learning association rules. Though the FP-Growth algorithm only needs scan two times, it has a poor efficiency for large datasets. There are already efforts have been made to solve the problem by using some Meta-heuristic optimization algorithms, such as particle swarm optimization algorithm (PSO), immune algorithms etc, which outperform the traditional FP-Growth algorithm and shows strong performance. However, PSO is easy to trap in the local optimums. A novel algorithm ant lion optimizer (ALO) was proposed and with the advantages of global optimization, good robustness, and high convergence accuracy, which was applied to many engineering fields like antenna array synthesis, integrated process planning, scheduling and so on. In the paper, a novel association rule extraction algorithm is put forward based on the ant lion optimization algorithm. A new fitness schema based on confidence and support has been used in this approach, which avoids part of unnecessary searching processes of the FP-Growth algorithm and leads the method of searching the optimization solution more effectively. In order to evaluate the effectiveness of our approach, experiments on various datasets are carried out and experimental results are compared with some other classical meta-heuristic algorithms, experimental results testify the performance of the proposed method.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"36 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":"122821423","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924244
Oleksandr Murzenko, S. Olszewski, O. Boskin, I. Lurie, N. Savina, M. Voronenko, V. Lytvynenko
The paper proposes a phased method of applying filtering algorithms, descriptor clustering. At the first stage, the features are reduced by sequential application of the moving average and FFT filtering algorithms and the reduction of the discretization step. At the second stage, for the selection of signs using the cluster analysis method X-means. At the final stage, regression models are constructed using the regulatory regression algorithms L1, L2, and Leastsquares. The resulting models are highly accurate, robust and adequate. In general, the work proposed a new method for predicting the binding affinity of peptides in order to find the numerical values of peptide bonds.
{"title":"Application of a Combined Approach for Predicting a Peptide-Protein Binding Affinity Using Regulatory Regression Methods with Advance Reduction of Features","authors":"Oleksandr Murzenko, S. Olszewski, O. Boskin, I. Lurie, N. Savina, M. Voronenko, V. Lytvynenko","doi":"10.1109/IDAACS.2019.8924244","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924244","url":null,"abstract":"The paper proposes a phased method of applying filtering algorithms, descriptor clustering. At the first stage, the features are reduced by sequential application of the moving average and FFT filtering algorithms and the reduction of the discretization step. At the second stage, for the selection of signs using the cluster analysis method X-means. At the final stage, regression models are constructed using the regulatory regression algorithms L1, L2, and Leastsquares. The resulting models are highly accurate, robust and adequate. In general, the work proposed a new method for predicting the binding affinity of peptides in order to find the numerical values of peptide bonds.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"92 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":"131592983","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924293
Halina I. Falfushynska, A. Kłos-Witkowska, B. Buyak, G. Tereshchuk, Uliana Yatsykovska, P. Falat, R. Szklarczyk
Distance education and blended learning has been becoming a major alternative to traditional teaching all over the world. The paper discloses the benefits and disadvantages of e-learning at Ukrainian Universities in particular at Ternopil V. Hnatiuk National Pedagogical University as the representative when compare to European models of distance education. Distance education at Ukrainian Universities bases on the principle of individuality and supports by tutor. Using multivariate statistics it has proven that self-motivation and both intrinsic and extrinsic goal orientation predict student success in distance education and e-learning. The perceived learning outcome is positively associated with to self-regulation, learning styles and success.
{"title":"The Development of Distance Learning in Ukrainian Liberal Arts Institutions Based on EU Experience","authors":"Halina I. Falfushynska, A. Kłos-Witkowska, B. Buyak, G. Tereshchuk, Uliana Yatsykovska, P. Falat, R. Szklarczyk","doi":"10.1109/IDAACS.2019.8924293","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924293","url":null,"abstract":"Distance education and blended learning has been becoming a major alternative to traditional teaching all over the world. The paper discloses the benefits and disadvantages of e-learning at Ukrainian Universities in particular at Ternopil V. Hnatiuk National Pedagogical University as the representative when compare to European models of distance education. Distance education at Ukrainian Universities bases on the principle of individuality and supports by tutor. Using multivariate statistics it has proven that self-motivation and both intrinsic and extrinsic goal orientation predict student success in distance education and e-learning. The perceived learning outcome is positively associated with to self-regulation, learning styles and success.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"2017 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":"121743581","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}
Pub Date : 2019-09-01DOI: 10.1109/IDAACS.2019.8924373
M. Yesina, M. Karpinski, V. Ponomar, Y. Gorbenko, T. Gancarczyk, Uliana Yatsykovska
The paper deals with the possible comparative analysis methods of the cryptographic primitives' properties. Methods of comparative analysis – analytic hierarchy process and variations of weight indices methods are investigated and analyzed. Conclusions are made and recommendations on the use of the cryptographic primitives' estimation methods are provided. Also the paper is devoted to the comparative analysis of candidates for the post-quantum key encapsulation standard according to the determined estimation technique. During the analysis, the technique of comparing cryptographic algorithms on the basis of expert estimations using the combination of conditional and unconditional criteria by the analytic hierarchy process was used.
{"title":"Comparative Analysis of Key Encapsulation Mechanisms","authors":"M. Yesina, M. Karpinski, V. Ponomar, Y. Gorbenko, T. Gancarczyk, Uliana Yatsykovska","doi":"10.1109/IDAACS.2019.8924373","DOIUrl":"https://doi.org/10.1109/IDAACS.2019.8924373","url":null,"abstract":"The paper deals with the possible comparative analysis methods of the cryptographic primitives' properties. Methods of comparative analysis – analytic hierarchy process and variations of weight indices methods are investigated and analyzed. Conclusions are made and recommendations on the use of the cryptographic primitives' estimation methods are provided. Also the paper is devoted to the comparative analysis of candidates for the post-quantum key encapsulation standard according to the determined estimation technique. During the analysis, the technique of comparing cryptographic algorithms on the basis of expert estimations using the combination of conditional and unconditional criteria by the analytic hierarchy process was used.","PeriodicalId":415006,"journal":{"name":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","volume":"55 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":"132547430","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}
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