TY  - JOUR
A1  - Lamprecht, Anna-Lena
A1  - Margaria, Tiziana
ED  - Lamprecht, Anna-Lena
ED  - Margaria, Tiziana
T1  - Scientific workflows and XMDD
JF  - Process design for natural scientists
Y1  - 2015
SN  - 978-3-662-45006-2
SP  - 1
EP  - 13
PB  - Springer
CY  - Berlin
ER  - 
TY  - JOUR
A1  - Grünewald, Franka
A1  - Meinel, Christoph
T1  - Implementation and Evaluation of Digital E-Lecture Annotation in Learning Groups to Foster Active Learning
JF  - IEEE transactions on learning technologies
N2  - The use of video lectures in distance learning involves the two major problems of searchability and active user participation. In this paper, we promote the implementation and usage of a collaborative educational video annotation functionality to overcome these two challenges. Different use cases and requirements, as well as details of the implementation, are explained. Furthermore, we suggest more improvements to foster a culture of participation and an algorithm for the extraction of semantic data. Finally, evaluations in the form of user tests and questionnaires in a MOOC setting are presented. The results of the evaluation are promising, as they indicate not only that students perceive it as useful, but also that the learning effectiveness increases. The combination of personal lecture video annotations with a semantic topic map was also evaluated positively and will thus be investigated further, as will the implementation in a MOOC context.
KW  - eLectures
KW  - tele-teaching
KW  - video annotation
KW  - collaborative learning
Y1  - 2015
U6  - https://doi.org/10.1109/TLT.2015.2396042
SN  - 1939-1382
VL  - 8
IS  - 3
SP  - 286
EP  - 298
PB  - Inst. of Electr. and Electronics Engineers
CY  - Los Alamitos
ER  - 
TY  - THES
A1  - Sadr-Azodi, Amir Shahab
T1  - Towards Real-time SIEM-based Network monitoring and Intrusion Detection through Advanced Event Normalization
Y1  - 2015
ER  - 
TY  - JOUR
A1  - Omranian, Nooshin
A1  - Müller-Röber, Bernd
A1  - Nikoloski, Zoran
T1  - Segmentation of biological multivariate time-series data
JF  - Scientific reports
N2  - Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets from the yeast Saccharomyces cerevisiae and the diatom Thalassiosira pseudonana.
Y1  - 2015
U6  - https://doi.org/10.1038/srep08937
SN  - 2045-2322
VL  - 5
PB  - Nature Publ. Group
CY  - London
ER  -