scholarly journals Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [Invited]

2020 ◽  
Vol 13 (1) ◽  
pp. A56
Author(s):  
Dario Azzimonti ◽  
Cristina Rottondi ◽  
Alessandro Giusti ◽  
Massimo Tornatore ◽  
Andrea Bianco
Keyword(s):  
Active Learning ◽  
Domain Adaptation ◽  
Learning Techniques ◽  
Quality Of Transmission ◽  
Transmission Estimation

On the benefits of domain adaptation techniques for quality of transmission estimation in optical networks

2020 ◽  
Vol 13 (1) ◽  
pp. A34 ◽  
Author(s):  
Cristina Rottondi ◽  
Riccardo di Marino ◽  
Mirko Nava ◽  
Alessandro Giusti ◽  
Andrea Bianco
Keyword(s):  
Optical Networks ◽  
Domain Adaptation ◽  
Quality Of Transmission ◽  
Transmission Estimation

Machine Learning Assisted Quality of Transmission Estimation and Planning with Reduced Margins

2020 ◽  
pp. 211-222
Author(s):  
Konstantinos Christodoulopoulos ◽  
Ippokratis Sartzetakis ◽  
Polizois Soumplis ◽  
Emmanouel Varvarigos
Keyword(s):  
Machine Learning ◽  
Quality Of Transmission ◽  
Transmission Estimation

STUDENT CONCERNS FOR ENGAGEMENT IN ONLINE ACTIVE LEARNING ENVIRONMENTS DURING COVID-19

2021 ◽  
Author(s):  
B. Memarian ◽  
S. Zuluaga ◽  
M. Stickel
Keyword(s):  
Active Learning ◽  
Learning Environments ◽  
Thematic Analysis ◽  
Engineering Students ◽  
Student Response ◽  
First Year ◽  
Positive Feeling ◽  
Learning Techniques ◽  
Neutral Tone

This paper shares a summary of the self-reported concerns of 134 first-year engineering students around engagement in online active learning environments during COVID-19. The students had volunteered to participate in remote weekly problem-solving workshops for four weeks that utilized Active Learning techniques. In this paper, we specifically analyze samples from the students who participated in only one workshop and responded to the following question: What concerns do you have that might limit your ability to engage in online active learning environments? Twenty of the participants reported no concerns. The tone of each student's response and personal feelings reported were also analyzed. Then, a thematic analysis of each student response was made, with the transcription and coding agreement being performed by two coders. As expected, most of the students expressed their concerns in a negative or neutral tone, and only a few expressed an affinity for current educational settings. Word mining of feeling terms shows that more students had verbalized being disengaged, followed by distracted and uncomfortable and none communicated a positive feeling. Our thematic analysis showed that learning socially (72/114, or 63%) is the most pressing concern for the students, followed by more personal regulating factors such as attitude and motivation (44%), quality of physical and virtual study environment (40%), as well as the guidance received from the course administrators (24%). Findings suggest the need for developing a global understanding of what active learning in an online environment entails in the context of engineering education, and to develop and adjust tools and practices to help students learn in this new context.

scholarly journals Quality of Transmission Estimation and Short-Term Performance Forecast of Lightpaths

2020 ◽  
Vol 38 (10) ◽  
pp. 2807-2814 ◽  
Author(s):  
Sandra Aladin ◽  
Anh Vu Stephan Tran ◽  
Stephanie Allogba ◽  
Christine Tremblay
Keyword(s):  
Short Term ◽  
Quality Of Transmission ◽  
Term Performance ◽  
Transmission Estimation

scholarly journals Bayesian Optimization-Based Algorithm to Improve the Quality of Transmission Estimation

2021 ◽  
Author(s):  
Reda Ayassi ◽  
Ahmed Triki ◽  
Maxime Laye ◽  
Esther Le Rouzic ◽  
Noel Crespi ◽  
...  
Keyword(s):  
Bayesian Optimization ◽  
Quality Of Transmission ◽  
Transmission Estimation
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