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001-es BibID:BIBFORM060947
Első szerző:Csernoch Mária (informatika tanár)
Cím:Testing algorithmic skills in traditional and non-traditional programming environments / Mária Csernoch, Piroska Biró, János Máth, Kálmán Abari
Dátum:2015
ISSN:1648-5831 2335-8971
Megjegyzések:The Testing Algorithmic and Application Skills (TAaAS) project was launched in the 2011/2012 academic year to test first year students of Informatics, focusing on their algorithmic skills in traditional and non-traditional programming environments, and on the transference of their knowledge of Informatics from secondary to tertiary education. The results of the tests clearly show that students start their studies in Informatics with underdeveloped algorithmic skills, only a very few of them reaching the level of extended abstract. To find reasons for these figures we have analyzed the students' problem solving approaches. It was found that the students, almost exclusively, only consider traditional programming environments appropriate for developing computational thinking, algorithmic skills. Furthermore, they do not apply concept and algorithmic based methods in non-traditional computer related activities, and as such, mainly carry out ineffective surface approach methods, as practiced in primary and secondary education. This would explain the gap between the expectations of tertiary education, the students' results in the school leaving exams, and their overestimation of their knowledge, all of which lead to the extremely high attrition rates in Informatics.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
algorithmic skills
spreadsheet
deep and surface metacognitive approaches
self-assessment
school leaving exams
Intelligens város közösségi alkotásból
Megjelenés:Informatics in Education. - 14 : 2 (2015), p. 175-197. -
További szerzők:Biró Piroska (1983-) (informatikus, matematikus) Máth János (1959-) (matematikus) Abari Kálmán (1971-) (programtervező matematikus)
Pályázati támogatás:TÁMOP-4.2.2.C-11/1/KONV-2012-0001
TÁMOP
Adat menedzsment és tudásfeltárás intelligens város alkalmazásokhoz
K-105262
OTKA
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001-es BibID:BIBFORM117946
035-os BibID:(WOS)000565865300009
Első szerző:Nsaif, Mohammed (informatics)
Cím:Detection and Prevention Algorithm of DDoS Attack Over the IOT Networks / Mohammed Ridha Nsaif, Mohammed Falah Abbood, Abbas Fadhil Mahdi
Dátum:2020
ISSN:2217-8309 2217-8333
Megjegyzések:Traffic classification is a crucial aspect for Software- Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game and ICMP. The performance of a number of Machine Learning techniques is compared and the results are reported. As part of future work, we will incorporate classification into the power consumption model towards achieving further advances in this research area.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Machine learning
classification
dataset
SDN
Megjelenés:TEM Journal-Technology Education Management Informatics. - 9 : 3 (2020), p. 899-906. -
További szerzők:Mohammed, Falah Abbood Abbas Fadhil Mahdi
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DOI
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