Malamas Nikolas

PhD Candidate

Aristotle University of Thessaloniki
Department of Electrical and Computer Engineering
54124 Thessaloniki – GREECE

Email: nikolas [dot] malamas (at) issel [dot] ee [dot] auth [dot] gr

Education

10/2020 – today

PhD Candidate
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki, Greece
PhD Thesis: “Techniques and Algorithms for Optimal Natural Language Understanding for Digital Assistants”

09/2014 – 11/2019

Diploma of Electrical and Computer Engineering
Electrical and Computer Engineering Department
Aristotle University of Thessaloniki, Greece
Diploma Thesis: “Full Coverage of Known Area with Unmanned Ground Vehicle using Path Patterns and Semantic Map Annotation”.

Professional Experience

09/2020 – today Research Associate, Gnomon Informatics SA, Thessaloniki, Greece
Funded Project: Sities (https://issel.ee.auth.gr/gr-sities/)

Research interests

  • Natural Language Processing
  • Chatbots
  • Software Engineering
  • Machine Learning

Languages

  • English: Proficient (Michigan Proficiency)
  • German: Fluent (Goethe-Zertifikat B2)

Memberships

  • Member of the Technical Chamber of Greece

Puplications

2021

Journal Articles

Nikolaos Malamas and Andreas Symeonidis
"Embedding Rasa in edge Devices: Capabilities and Limitations"
Procedia Computer Science, 192, pp. 109-118, 2021 Jan

Over the past few years, there has been a boost in the use of commercial virtual assistants. Obviously, these proprietary tools are well-performing, however the functionality they offer is limited, users are ”vendor-locked”, while possible user privacy issues rise. In this paper we argue that low-cost, open hardware solutions may also perform well, given the proper setup. Specifically, we perform an initial assessment of a low-cost virtual agent employing the Rasa framework integrated into a Raspberry Pi 4. We set up three different architectures, discuss their capabilities and limitations and evaluate the dialogue system against three axes: assistant comprehension, task success and assistant usability. Our experiments show that our low-cost virtual assistant performs in a satisfactory manner, even when a small-sized training dataset is used.

@article{malamas2021-rasa,
author={Nikolaos Malamas and Andreas Symeonidis},
title={Embedding Rasa in edge Devices: Capabilities and Limitations},
journal={Procedia Computer Science},
volume={192},
pages={109-118},
year={2021},
month={01},
date={2021-01-01},
url={https://www.sciencedirect.com/science/article/pii/S187705092101499X},
doi={https://doi.org/10.1016/j.procs.2021.08.012},
issn={1877-0509},
keywords={Spoken Dialogue Systems;NLU;Rasa;Chatbots},
abstract={Over the past few years, there has been a boost in the use of commercial virtual assistants. Obviously, these proprietary tools are well-performing, however the functionality they offer is limited, users are ”vendor-locked”, while possible user privacy issues rise. In this paper we argue that low-cost, open hardware solutions may also perform well, given the proper setup. Specifically, we perform an initial assessment of a low-cost virtual agent employing the Rasa framework integrated into a Raspberry Pi 4. We set up three different architectures, discuss their capabilities and limitations and evaluate the dialogue system against three axes: assistant comprehension, task success and assistant usability. Our experiments show that our low-cost virtual assistant performs in a satisfactory manner, even when a small-sized training dataset is used.}
}