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CS MSc Thesis Presentation Day June 21


From: 2021-06-21 09:15 to 15:00
Place: Online via zoom/teams (separate link for each presentation)
Contact: birger [dot] swahn [at] cs [dot] lth [dot] se
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Three MSc theses to be presented on Monday June 21, 2021

Monday June 21 is a day for coordinated master thesis presentations in Computer Science at Lund University, Faculty of Engineering. Three MSc theses will be presented. Other MSc thesis presentations will take place on June 9 and June 10. Please see links:

Four presentations on June 9

Fourteen presentations on June 10

The presentations will take place online via Zoom/Teams, see separate link for each presentation. A preliminary schedule follows.

Update: One presentation added at 14:15

Note to potential opponents: Register as an opponent to the presentation of your choice by following the instructions on Doodle at Note that the number of opponents may be limited (often to two), so you might be forced to choose another presentation if you register too late. Registrations are individual, just as the oppositions are! More instructions are found on this page.


Presenters: Martin Jakobsson, Johan Henriksson
Title: Bridging the gap between local machines and cloud notebooks
Examiner: Niklas Fors
Supervisor: Markus Borg (LTH)

Data science development largely revolves around working in notebooks, and these usually run in a cluster environment. These cloud notebooks do however come with limitations. Support for tools like version control, testing, refactoring and linting is limited, which negatively impacts developer experience and software quality. We propose a networked file system for sharing local files and directories with a remote cloud server. This allows the developer to work in a cloud notebook without having to upload files to an external storage provider, and it enables the use of traditional development tools since all files are stored on the local machine. The networked file system is implemented as a FUSE file system and is made available to Kubernetes as a custom volume driver.. We show that it is possible to build a widely compatible file sharing solution for any Kubernetes workload. It is easy to use and has reasonable performance.

Link to presentation (N.B. Link has been updated):

Info for opponents: Sign up to presentation no. 19 on Doodle and send email to the examiner

Link to popular science summary: TBU


Presenter: Hannes Fornell
Title: Employee Engagement Analysis on Communication
Examiner: Markus Borg
Supervisors: Elizabeth Bjarnason (LTH), Rickard Nygren (Grade AB)

Employee engagement is an important aspect when trying to gain a productive team. The problem is that measuring employee engagement is expensive and time consuming. The thesis’ goal is to examine the possibility of gaining insight about engagement levels from analysis of the employees’ internal communication. This was performed by designing a set of metrics that measured the amount of communication interactions. These metrics were based on a literature review and user interviews. When comparing the metrics to employee surveys and focus groups it turned out that the metrics did not sufficiently describe employee engagement. To accomplish this a more sophisticated collection of metrics is needed along side with expanding the source data. This thesis work has provided the case company with a wider understanding of the possibilities of extracting and analysing communication data through Microsoft Teams. Several factors that could be considered for accurate measurements were also identified.

Link to presentation (N.B. Link has been updated):

Info for opponents: Sign up to presentation no. 20 on Doodle and send email to the examiner

Link to popular science summary:

14:15-15:00 (N.B. This presentation has been added later)

Presenter: Simon Kristoffersson Lind, Johannes Tykesson
Title: Self-Optimization of Camera Hardware
Examiner: Volker Krueger
Supervisors: Luigi Nardi (LTH), Waqar Hameed (Axis Communications AB)

This thesis aims to investigate the automatic tuning of hardware parameters in a camera's image processing pipeline. In order to solve the tuning problem, it is formulated as a black-box optimization problem centered around a physical camera unit. Optimization is performed by comparing the camera's output to a reference image. Several black-box optimization algorithms were tested: Bayesian Optimization, Evolutionary Optimization, Particle Swarm Optimization, Simulated Annealing, DIRECT, and Rowan's Subplex Method. Results indicate that it is feasible to automatically tune camera hardware parameters using black-box optimization algorithms. For 14 parameters, Rowan's Subplex Method performs best with an average error of 6.25. When optimizing a much larger set of 71 parameters, Simulated Annealing, Evolutionary, and Rowan's Subplex Method perform best with an average error of 9.77, 17.92, and 18.05 respectively.

Link to presentation:

Info for opponents: Sign up to presentation no. 21 on Doodle and send email to the examiner

Link to popular science summary: