MSc Defences Summer 2024
See the list of MSc defences at DIKU this summer (incl. August, September and October). The list will be continuously updated.
Information about the thesis, supervisor, location of the defence, etc. can be found on the respective events below.
Computer Science
Name of student(s) |
Jingyi Zheng and Yaokun Li |
Study Programme |
Computer Science Thesis |
Title |
MobileDFL: Embracing Heterogeneity and Dynamism for Decentralized Federated Learning in Mobile Networks |
Abstract |
The exponential growth of mobile devices has led to abundant data for training AI models, but it has also raised privacy concerns. Decentralized federated learning (DFL) has emerged as a solution to balance user privacy and avoid single points of failure. However, DFL places higher demands on nodes, requiring increased communication and storage resources compared to conventional federated learning. The heterogeneity of mobile networks introduces nodes with varying capabilities, and some may struggle with resource-intensive requirements. Moreover, the dynamic nature of mobile networks can lead to ineffective communication. To address these inefficiencies, we propose the MobileDFL that integrates the advantages of both Client-Server and Peer-toPeer architectures, enabling each node to select the optimal strategy in heterogeneous and dynamic mobile networks. We also discuss incentive strategies to reward nodes undertaking greater communication and storage costs. Our experiments with different model complexities show that MobileDFL successfully mitigates communication and storage overhead of nodes while ensuring |
Supervisor(s) |
Xikun Jiang |
External examiner(s) |
Hua Lu |
Date and time |
June 6 2024, 13:00-14:00 |
Room |
UP1 771-01-2-16 |
Name of student(s) |
Qiongyan Wang |
Study Programme |
Computer Science Thesis |
Title |
DRL4DPRA: A Deep Reinforcement Learning Framework for Dynamic Public Resource Allocation |
Abstract |
The goal of allocating public resources, such as billboards, surveillance cameras, base stations, and trash bins, is to cater to a larger population. However, the uneven distribution of people across spatial and temporal domains is influenced by the dynamic patterns of human mobility. To address this, we introduce Hierarchical Spatial Temporal Netowrk (HSTNet) for dynamic public resources allocation tasks. Based on the reinforcement learning framework, our model can learn from experience without setting complex rules. It operates in two key stages: Firstly, We capture the spatiotemporal characteristics of crowd flow. Secondly, we use a hierarchical selection method to reduce the action space. We evaluate HSTNet’s performance using two real-world crowd flow datasets, demonstrating its superiority over baseline models. |
Supervisor(s) |
Xikun Jiang |
External examiner(s) |
Hua Lu |
Date and time |
June 6 2024, 14:00-15:00 |
Room |
Room UP1, 771-01-2-16 |
Name of student(s) |
He Lyu |
Study Programme |
Computer Science Thesis |
Title |
Securing privacy in reinforcement learning through zero-Knowledge proofs |
Abstract |
Reinforcement learning (RL) is widely used in real-world applications, but |
Supervisor(s) |
Xikun Jiang |
External examiner(s) |
Hua Lu |
Date and time |
June 6 2024, 15:00-16:00 |
Room |
UP1 771-01-2-16 |
Name of student(s) |
Jakob Krogh Petersen and Johan Valdemar Licht |
Study Programme |
Computer Science Thesis |
Title |
Contrastive Language-Image Pre-Training In Three-Dimensional Space |
Abstract |
Current state-of-the-art medical image analysis methods require dense |
Supervisor(s) |
Mads Nielsen |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
June 7 2024, 10:00-11:00 |
Room |
ØV 3 (The Pioneer Centre) |
Name of student(s) |
Aljaz Jazbec |
Study Programme |
Computer Science Thesis |
Title |
Targeted Security Analysis of ARYZE's platform |
Abstract |
In recent years, there has been an increase in the number of successful attacks on smart contracts. Therefore, in this project, we perform a targeted security analysis of ARYZE’s platform. Specifically, we analyze the platform’s smart contract implementation of ERC20 token eEUR published on Ethereum Mainnet in three different ways: manual static analysis, analysis of the output of automatic vulnerability scanner, and formal verification. The whole analysis is based on the customers requirements gathered during the requirements elicitation phase. We use Slither as a tool for the automatic vulnerability scanner, and F* proof-oriented language for the formal verification process. |
Supervisor(s) |
Boris Düdder |
External examiner(s) |
Pavel Hruby |
Date and time |
June 7 2024, 14:00-15:00 |
Room |
UP1-2-015 |
Name of student(s) |
Christopher Jung |
Study Programme |
Computer Science Thesis |
Title |
C-SPEC: Formal Specification for Blockchain-based Crowdsourcing Systems |
Abstract |
The digital crowdsourcing paradigm has revolutionized modern outsourcing through its highly-systematic approach to task coordination. Researchers in Blockchain technologies are currently investigating the |
Supervisor(s) |
Boris Düdder |
External examiner(s) |
Pavel Hruby |
Date and time |
June 7 2024, 13:00-14:00 |
Room |
UP1-2-015 |
Name of student(s) |
Sofie Sylvest Aastrup |
Study Programme |
Computer Science Thesis |
Title |
Fiber Break Segmentation in Composite Materials |
Abstract |
Composite materials have many applications and are used in buildings, |
Supervisor(s) |
Abraham George Smith |
External examiner(s) |
Melih Kandemir |
Date and time |
June 10 2024, 10:30-11:30 |
Room |
diku up1-2-0-15 |
Name of student(s) |
Alibek Cholponbaev |
Study Programme |
Computer Science Thesis |
Title |
AsyncDB: a database system for asynchronous application architectures |
Abstract |
The current software development paradigm is an ongoing trend towards asynchronous and concurrent programming, focusing on better exploitation of hardware in monolithic architectures and decoupled systems for better scalability and availability. While some domains are inherently straightforward to decouple without impacting the correctness of the system, domains such as finance and e-commerce, which require transactional logic, cannot enjoy the benefits of concurrency to its full extent. The issue lies in inadequate abstractions in the communication approach with databases that do not allow developers to exercise |
Supervisor(s) |
Yongluan Zhou |
External examiner(s) |
Alceste Scalas |
Date and time |
June 11 2024, 15:00-16:00 |
Room |
UP1 01-2-10 |
Name of student(s) |
Sigurður Kalman Oddsson |
Study Programme |
Computer Science Thesis |
Title |
Mapping Relational Benchmarks to Actor Systems |
Abstract |
In the last few decades, we have witnessed an unprecedented growth in computing power and a grand rise in accessible cloud infrastructure. The need for an efficient, parallel programming model that can take advantage of these advancements is at an all-time high. The actor model, which is specifically designed for high concurrency and distribution, is an excellent contender. The paradigm has already demonstrated its prowess in a number of fields and has recently caught the interest of database enthusiasts. Despite this, many industry-standard benchmarks for on-line transaction processing (OLTP) systems are tailored to relational schemas. Translating these relational schemas to actor systems, whilst still adhering to relevant specifications, is non-trivial. Questions about the correlation between actor composition and performance remain largely unanswered. |
Supervisor(s) |
Yongluan Zhou |
External examiner(s) |
Alceste Scalas |
Date and time |
June 11 2024, 16:00-17:00 |
Room |
UP1 01-2-10 |
Name of student(s) |
Yixuan Chen and Guodong Shi |
Study Programme |
Computer Science Thesis |
Title |
Toy with their shapes like a puppeteer - An exploration in the potential of elliptical shape-changing mechanisms |
Abstract |
Shape-changing devices are increasingly popular for their adaptive and responsive abilities. Geometry shapes like circles benefit from parametric design methods and have been applied in designs that perform shape-changing. But compared with circles, ellipses, as the geometry shape with the ability to perform non-symmetric shape-changing, are rarely discussed in this field. Therefore, we came up with the idea of stacked ellipses that can simulate objects such as ergonomic grips, VR proxies, etc., and perform parametric shape changing. There are two models we proposed that could perform such actions, the Shell model and the Tubes model. We have explored the influences of parameters on the shape-changing performance of our two models, both physically and geometrically. We have also verified our theory based on geometric properties of the ellipse, and discussed some real-life scenario applications that are based on our models. Our contribution lies in that we have come up with a new potential solution for manufacturing shape-changing devices that are based on elliptical structures, and proven that elliptical shapechanging is potentially useful in real-life scenarios. |
Supervisor(s) |
Valkyrie Savage |
External examiner(s) |
Anca-Simona Horvath |
Date and time |
June 12 2024, 15:00-16:00 |
Room |
Sigurdsgade 41, conference room 0-11 |
Name of student(s) |
Anja Vrecer |
Study Programme |
Computer Science Thesis |
Title |
Enhancing reliability of Language Models through minimization of uncertaint |
Abstract |
In recent years, text-generating AI assistants, such as ChatGPT, demonstrated remarkable abilities to quickly find information and answer questions. However, despite their proficiency in generating fluent, human-like text, a significant drawback is their inability to express uncertainty. This often leads to syntactically correct but factually imprecise or inaccurate answers, compromising their reliability. In this project, we focus on an aspect of uncertainty that can be resolved through interaction with users, specifically on ambiguity in question-answering tasks. Through a review of related work, we identify one research tightly connected to this problem and analyze it through re-implementation. The authors propose a framework consisting of prompting and estimation of entropy over |
Supervisor(s) |
Desmond Elliott |
External examiner(s) |
Claus Witfelt |
Date and time |
June 12 2024, 16:30-17:30 |
Room |
Hybrid defence (Vibenshuset + Zoom) |
Name of student(s) |
Zhongxing Ren |
Study Programme |
Computer Science Thesis |
Title |
Melody Code: A research study on how to make sound triggered by 3D printed objects have higher information density |
Abstract |
Information requires a medium for transmission, and sound, as a crucial carrier of information, has been extensively studied by researchers in the field of human-computer interaction, particularly in acoustic interaction. However, non-speech acoustic interaction is often less expressive, partly due to the low information density of non-speech sounds. In this paper, we first define information density in the context of non-speech sound. Based on this definition, we propose Melody Code, a technique that encodes information into melodies for information transmission. We detail the implementation of Melody Code using the music box mechanisms, including the mapping of information to notes to create melodies, the fabrication of sound triggering devices to form a melody, and the decoding of the melody by recognizing notes through a trained machine learning model (CNN). This demonstrates that Melody Code enhances the information density of encoded non-speech sounds. Furthermore, |
Supervisor(s) |
Daniel Lee Ashbrook |
External examiner(s) |
Anca-Simona Horvath |
Date and time |
June 12 2024, 10:00-11:00 |
Room |
Sigurdsgade 0-11 |
Name of student(s) |
Radu Taraburca |
Study Programme |
Computer Science Thesis |
Title |
Objectify: Item recommendation through LLMs and prompt engineering |
Abstract |
Nowadays, 3D printing has become more common, this can be done in one’s own home, in universities, and in public libraries, but 3D modeling and using a 3D printer to print is still difficult. Previous work like Objectify[1] explored an automated calendar-to-print workflow, but it had issues with the recommendation of real objects to print. It did not always suggest physical objects, sometimes it could suggest abstract things like ”internet connection” or ”relaxing music” and its system ignored data beyond users’ calendars. Other work on prompt engineering explores how to limit the kinds of answers that AIs can give to generate only what is needed for a specific task. We set out to merge these two areas, adding new data streams, exploring prompt engineering techniques, and building a specialized recommendation system that would generate real, physical, and printable 3D objects that would be culturally relevant for users. Also, Objectify[1] has its goal to highlight the ridiculousness of digital fabrication claims rather than attempting to seriously facilitate anything, so our motivation is to investigate what we need to actually do to achieve this. We evaluated the recommendation system to see how many of the generated objects are relevant, both from the point of view of the context and if they are printable physical objects (0.1% of the objects were ambiguous). We had 15 people who participated in the evaluation, 13 people speaking Romanian and English and the remaining 2 people speaking English |
Supervisor(s) |
Valkyrie Savage |
External examiner(s) |
Anca-Simona Horvath |
Date and time |
June 12 2024, 16:00-17:00 |
Room |
Sigurdsgade 41, conference room 0-11 |
Name of student(s) |
Barnabás Baka |
Study Programme |
Computer Science Thesis |
Title |
Critical Making |
Abstract |
This thesis investigates how can a critical design artifact foster conversation |
Supervisor(s) |
Pernille Bjørn |
External examiner(s) |
Nanna Inie Strømberg-Derczynski |
Date and time |
June 13 2024, 09:15-10:15 |
Room |
Sigurdsgade 41 |
Name of student(s) |
Yannick Neubert |
Study Programme |
Computer Science Thesis |
Title |
Shape Priors and Pose Invariance in Neural SDF |
Abstract |
This thesis presents an approach to disentangle pose and scale from the latent representation of shapes implicitly defined through signed distance fields as well as enforcing a normalization prior on the latent space. Using shape moments of order up to 3, a normalized pose and scale can be defined for arbitrary shapes, which is then used to learn a latent representation invariant under similarity transforms. The normalized latent codes are then combined with a set of pose parameters to reconstruct shapes of arbitrary pose and scale. Experiments conducted on 2-dimensional shape data produce promising initial results but also highlight some shortcomings of the proposed approach. Some potential solutions are discussed together with the potential for use in downstream tasks. Finally, it is demonstrated that the proposed method can be easily generalized to 3D data. All code is made publicly available on GitHub. |
Supervisor(s) |
Francois Bernard Lauze |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
June 14 2024, 09:30-10:30 |
Room |
Image Section Study office |
Name of student(s) |
Sebastian Paarmann |
Study Programme |
Computer Science Thesis |
Title |
A WebGPU backend for Futhark |
Abstract |
In this thesis project, we create a new backend for the Futhark compiler. Futhark is a functional data-parallel array programming language whose optimizing compiler can generate efficient GPGPU code. Our backend targets the WebGPU API, enabling Futhark programs to be run in web browsers while still taking advantage of GPU compute capability. |
Supervisor(s) |
Troels Henriksen |
External examiner(s) |
Willard þór Rafnsson |
Date and time |
June 14 2024, 13:45-14:45 |
Room |
DIKU UP1-2-0-04 |
Name of student(s) |
Nick Hauptvogel |
Study Programme |
Computer Science Thesis |
Title |
Bayesian vs. PAC-Bayesian Ensembles |
Abstract |
Diverse ensembles of deep neural networks (deep ensembles) can |
Supervisor(s) |
Christian Igel |
External examiner(s) |
Jes Frellsen |
Date and time |
June 14 2024, 11:00-13:00 |
Room |
P1, Øster Voldgade 3 |
Name of student(s) |
Nichlas Udengaard |
Study Programme |
Computer Science |
Title |
Computer Science |
Abstract |
U-Sleep is a software for analysing human sleep data. It is a convolutional |
Supervisor(s) |
Christian Igel |
External examiner(s) |
Kristoffer Hougaard Madsen |
Date and time |
June 14 2024 - 10:30 - 11:30 |
Room |
@DIKU |
Name of student(s) |
Louis Marott Normann |
Study Programme |
Computer Science Thesis |
Title |
A Deeper Dive: Improving the Partial Evaluation of RL |
Abstract |
Partial evaluation of RL has been accomplished previously. The purpose of this thesis is to build further on the groundwork done in this article, improving it in both breadth and depth. The thesis shows that a pointwise binding-time analysis can be used to further improve the offline partial evaluator, enabling it to specialize more programs in a non-trivial way. |
Supervisor(s) |
Robert Glück |
External examiner(s) |
Ulrik Pagh Schultz |
Date and time |
June 17 2024, 13:30-14:30 |
Room |
UP1, 1-0-34 |
Name of student(s) |
Mathias Marott Sundram |
Study Programme |
Computer Science Thesis |
Title |
Private synthetic data using public data |
Abstract |
Synthetic data presents a privacy-conscious approach to publishing accurate data, ensuring a faithful preservation of the data properties while also protecting the privacy of individuals. The advent of big data in numerous areas across society, underscores the importance of private, synthetic data, and Differential Privacy is a fundamental measure of privacy that helps quantify the degree to which an algorithm treats data |
Supervisor(s) |
Rasmus Pagh |
External examiner(s) |
Martin Aumüller |
Date and time |
June 17 2024, 11:00-12:00 |
Room |
Store UP1 |
Name of student(s) |
Nikolai Kjær Nielsen |
Study Programme |
Computer Science Thesis |
Title |
Semi-supervised multi-modal generative models for structure elucidation of tandem mass spectra |
Abstract |
Predicting molecule structures from tandem mass spectra is a critical challenge of modern analytical chemistry. Most existing computational methods rely on training the models on fully supervised datasets of molecular structures and mass spectra, which limits the predictive power for these complex modalities. Deep generative modeling is a powerful technique allowing to fit complex joint distributions of multiple data |
Supervisor(s) |
Svetlana Kutuzova |
External examiner(s) |
Jes Frellsen |
Date and time |
June 17 2024, 10:00-11:00 |
Room |
AI Pioneer Center |
Name of student(s) |
Sune Skaanning Engtorp |
Study Programme |
Computer Science Thesis |
Title |
Implementation of a type-safe generalized syntax-directed editor |
Abstract |
This thesis investigates the development and implementation of a type-safe, generalized syntax-directed editor. The goal is to create an editor capable of supporting any language, including but not limited to programming languages. The foundation of this work is a proposed generalized editor calculus, which has been encoded in an extended lambda calculus to theoretically establish the capability of building such an editor. This project realizes this calculus in practice by implementing it in the functional programming language Elm, which has already been proven capable of supporting a nongeneric structure editor. The report details the implementation process, encompassing the representation of abstract syntax, source code generation, and the handling of editor expressions. The implementation, written in Elm, features a language specification parser and a source code generator. Currently, the generated editor |
Supervisor(s) |
Hans Hüttel |
External examiner(s) |
Mads Rosendahl |
Date and time |
June 18 2024, 14:15-15:15 |
Room |
SCI-DIKU-HCO-01-0-029 |
Name of student(s) |
Thorbjørn Bülow Bringgaard |
Study Programme |
Computer Science Thesis |
Title |
Efficient Big Integer Arithmetic Using GPGPU |
Abstract |
Exact big integer arithmetic is a fundamental component of numerous |
Supervisor(s) |
Cosmin Eugen Oancea |
External examiner(s) |
Mads Rosendahl |
Date and time |
June 18 2024, 17:00-18:00 |
Room |
SCI-DIKU-HCO-01-0-029 (PLTC meeting room) |
Name of student(s) |
Siyi Wu |
Study Programme |
Computer Science Thesis |
Title |
SDF-TopoNet: A Hybrid Approach for Enhancing Topological Accuracy in Tubular Structure Segmentation |
Abstract |
Accurate segmentation of tubular structures such as blood vessels, neurons, and pathways has received increasing attention recently. Preserving the topological features of these structures has become particularly important in various applications. One of the main challenges in this area is to find effective loss functions to handle such data. Although several studies have explored some practical loss functions, they often encounter potential problems such as high training costs and degradation of pixel accuracy. Hu et al. proposed a topological loss based on Betti error and persistent homology. Based on their research, we propose further improvements to improve the segmentation performance and reduce the training cost. Our approach incorporates a pre-training and fine-tuning strategy based on the weighted sum of a pixelbased loss function (e.g., MSE) and the topological loss as the loss function for model training. Specifically, we use the signed distance function (SDF) as a prior task in the pre-training stage to enable the model to learn the topological structure information of the image and use a dynamic threshold layer and topological loss in the fine-tuning stage to ensure the topological |
Supervisor(s) |
Jon Sporring |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
June 18 2024, 09:15-10:10 |
Room |
SCI-DIKU-UP1-1-1-N116A |
Name of student(s) |
Asta Feodora Sjöberg Burhenne, Lucas Østergaard Jarmer, and Laufey Karitas Ólafsdóttir |
Study Programme |
Computer Science Thesis |
Title |
Root analysis using geometric and topological descriptors |
Abstract |
Topological data analysis (TDA) offer promising avenues for the classification and analysis of root systems, yet their application and efficacy in the field of root analysis remain unexplored. In this study, we investigate the performance of various topological descriptors in classifying root systems based on persistence diagrams derived from bifurcation |
Supervisor(s) |
Jon Sporring |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
June 18 2024, 10:20-12:15 |
Room |
DIKU, UP1-1-1-N116A |
Name of student(s) |
Anders Lietzen Holst |
Study Programme |
Computer Science Thesis |
Title |
Optimizing Tensor Contractions for GPU Execution in Futhark |
Abstract |
The tensor contraction, a higher-dimensional analogue to the matrix multiplication, is a widely used basic building block that is not only suitable for efficient GPU execution due to its highly parallel nature, but also ripe for locality of reference optimizations due to a high degree of data reuse. Futhark, a highly optimizing compiler targeting GPU hardware, generates efficient 2D block/register tiled code for GEMM-like programs, but does not apply the transformation to arbitrary contractions. With an offset in tensor contraction and GPU code transformation theory, we detail how we successfully implemented block/register tiling of arbitrary tensor contractions into the Futhark compiler, using generic LMAD copies to stage input data and a number of other minor optimizations, and describe some of the problems overcome in doing so as well as the roadblocks and limitations which unfortunately remain. Using a small benchmarking plan we examine the practical benefits of the transformation, using a hand-written prototype kernel and a GPU code generator for high-performance tensor contractions as points of reference – the implementation performs well, reaching between 68% and 98% of the reference programs, but the opportunities for optimization are many. Finally, we present some ideas for future work in both improving and generalizing the implementation. |
Supervisor(s) |
Cosmin Eugen Oancea |
External examiner(s) |
Mads Rosendahl |
Date and time |
June 18 2024, 16:00-17:00 |
Room |
SCI-DIKU-HCO-01-0-029 (PLTC meeting room) |
Name of student(s) |
Aske Rory Ching and Niklas Joost Borge |
Study Programme |
Computer Science Thesis |
Title |
Towards adversarially robust dataset compression |
Abstract |
As the need for larger datasets in state-of-the-art machine learning increases, so does the costs of storing and using these datasets. Both economically and environmentally. This has spawned an interest in dataset compression for faster and cheaper training. These methods create small but information rich datasets by smart selection or condensation techniques. Many condensation methods have shown promising results in benign settings. However, the adversarial robustness of these datasets has not been well studied. In this project, we take a closer look at adversarial training on compressed datasets. We show that these do not respond well to adversarial training by default. We further investigate ways to improve the robustness potential of state-of-the-art dataset condensation methods. Here, we found that Gradient Matching shows potential for improvements, although at the expense of benign accuracy and an increased computational cost. Alternatively, we propose a method of latent space feature selection to create an adversarially trainable synthetic dataset. Furthermore, we show that coreset selection can be improved by selecting data points in latent space instead of data space. Our results suggest that feature selection in latent space is promising for adversarially trainable dataset compression |
Supervisor(s) |
Raghavendra Selvan |
External examiner(s) |
Lee Herluf Lund Lassen |
Date and time |
June 18 2024, 10:00-12:00 |
Room |
DIKU UP1-2-0-04 |
Name of student(s) |
Lennart Mischnaewski |
Study Programme |
Computer Science Thesis |
Title |
Rating-Aware Sequential Recommendation Systems using Generative Retrieval and Semantic Encoding |
Abstract |
Given sets of users, items, and their interactions, recommendation systems aim to provide users with personalized selections of items that adhere to criteria such as user preferences or fairness metrics. To create meaningful suggestions, the recommendation engine may use a multitude of signals, including explicit signals such as ratings, implicit signals such as the user’s actions, or information about the users and items such as their country of residence or the item’s country of origin. Such signals provide recommendation models with information about which items the users prefer or dislike and can be used to tailor future recommendations. |
Supervisor(s) |
Christina Lioma |
External examiner(s) |
Konstantinos Manikas |
Date and time |
June 19 2024, 10:00-11:00 |
Room |
UP1 room 1.2.26 |
Name of student(s) |
Claudia Ann Hinkle |
Study Programme |
Computer Science Thesis |
Title |
Co-Designing Pacing Technologies for People with Energy-Limiting Conditions |
Abstract |
People with chronic illnesses affecting energy levels such as ME/CFS and |
Supervisor(s) |
Sarah Frances Homewood |
External examiner(s) |
Signe Louise Yndigegn |
Date and time |
June 20 2024, 10:15-11:15 |
Room |
2-03 at Sigurdsgade 41, 2200, KBH N |
Name of student(s) |
Yaqi Zhou |
Study Programme |
Computer Science Thesis |
Title |
Optimal external resizable arrays |
Abstract |
Resizable arrays are crucial in managing big data across various industries. The traditional implementation may leave up to half of the allocated space unused, which represents a significant inefficiency in the context of big data. Tarjan and Zwick’s implementation, which consumes N + O(N 1/r) memory cells for maintaining a resizable array of N items and temporarily uses N + O(N 1−1/r) memory cells for any integer r > 2, is designed for internal memory. This design assumes that each item and each pointer |
Supervisor(s) |
Mingmou Liu |
External examiner(s) |
Rüdiger Riko Jacob |
Date and time |
June 20 2024, 13:00-14:00 |
Room |
DIKU UP1-1-1-N116B |
Name of student(s) |
Oliver Christopher Juhl, Matthias Schultz Busch, Frederik Meyer Møller-Jørgensen, and Jonathan Gram Stenkilde |
Study Programme |
Computer Science Thesis |
Title |
Automatic Text Retrieval & Parsing of Digital Herbarium Sheets |
Abstract |
Organizations such as the Natural History Museum of Denmark (NHMD) specialize in the collection of herbarium sheet specimens for the purpose of documenting their samples for the future, according to a scientifically defined nomenclature. However, the process of digitizing such plant specimens into a database is currently dominated by manual labor, as no fully automatic system exists yet for that task. This calls for a more efficient solution, which would be highly beneficial for history and botanical |
Supervisor(s) |
Kim Steenstrup Pedersen |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
21 June 2024, 11:15-12:45 |
Room |
Konferencelokalet på Zoologisk Museum, UP 15 |
Name of student(s) |
Alexis Jean René Dumélié |
Study Programme |
Computer Science Thesis |
Title |
Technology Induced Lucid Dreams (TILDs) |
Abstract |
A lucid dream is a dream where the dreamer is aware they are dreaming. |
Supervisor(s) |
Valkyrie Savage |
External examiner(s) |
Jakob Eg Larsen |
Date and time |
June 21 2024, 10:00-11:00 |
Room |
Sigurdsgade 41, room 0-11 |
Name of student(s) |
Thomas Jackson Terry |
Study Programme |
Computer Science Thesi |
Title |
Distilling Reliance from Trust: A Survey of Explainable AI Research |
Abstract |
Explainable artificial intelligence research often targets trust as a goal for XAI. But the case for trust in AI may not be so straightforward. I conducted a survey of 43 recent XAI research articles to determine their stance toward trust. Finding a lack of consensus, I propose that another concept - reliance - could unite research efforts and provide a more achievable goal. |
Supervisor(s) |
Irina Alex Shklovski |
External examiner(s) |
Niels van Berkel |
Date and time |
June 21 2024, 13:00-14:00 |
Room |
Name of student(s) |
Xuanlang Zhao |
Study Programme |
Computer Science Thesis |
Title |
Shortest Path in Three Dimensions |
Abstract |
We discuss the problem of computing shortest obstacle-avoiding paths under an Lp metric (e.g. an Euclidean metric), and we present three algorithms for this problem. Our first algorithm is a fully polynomial approximate algorithm for the problem. The second algorithm |
Supervisor(s) |
Mikkel Vind Abrahamsen |
External examiner(s) |
Nutan Limaye |
Date and time |
June 25 2024, 09:30-10:30 |
Room |
HCØ Aud. 7 |
Name of student(s) |
Aleksas Prelgauskis |
Study Programme |
Computer Science Thesis |
Title |
Investigating the Effect of Outlier Removal by Process Discovery Algorithms |
Abstract |
Many advanced process discovery algorithms have built-in mechanisms to exclude certain outlier traces from logs in order to simplify the resulting process models. The prevailing justification is that these uncommon traces are merely noise in the data, but this claim lacks concrete evidence. This could raise fairness issues, as when mining processes involve human participants, these outliers may very well represent minorities that, through their removal from the training data, are marginalized by the resulting process models. In this paper, we explore how various process discovery algorithms determine which cases are outliers and how they treat protected groups in practice. We designed an experiment to obtain outlier traces from various process discovery algorithms and examined the overlap of these outliers, including the representation of protected cases among them. Furthermore, we evaluated each model’s outliers split between protected and unprotected traces against the original split in the event logs. Our findings reveal that different algorithms do not consistently |
Supervisor(s) |
Tijs Slaats |
External examiner(s) |
Søren Debois |
Date and time |
June 26 2024, 09:00-10:00 |
Room |
SCI-DIKU-sigurdsgade-0-11 |
Name of student(s) |
Bernard Legay Halfeld Ferrari Alves |
Study Programme |
Computer Science Thesis |
Title |
Compiling Hermes to RSSA |
Abstract |
Reversible programming languages have been a focus of research for more than a decade, mostly due to the work of Glück, Yokoyama, Mogensen, and many others. In this paper, we report about our recent activities to compile code written in the reversible language Hermes to reversible static-single-assignment form RSSA. We will also discuss how we wrote an interpreter for an extended version of RSSA using a type system. Our compiler allows the execution of simple Hermes programs and provides the basis for further optimizations. |
Supervisor(s) |
Torben Ægidius Mogensen |
External examiner(s) |
Morten Rhiger |
Date and time |
June 26 2024, 10:00-12:00 |
Room |
772-01-0-S29 - PLTC meeting room |
Name of student(s) |
Stefan Kröll Rasmussen |
Study Programme |
Computer Science Thesis |
Title |
Building Mobile Robot Platform for Open Space Interaction |
Abstract |
This thesis aims to design, develop, and evaluate a mobile robot platform that aims to ensure a seamless Human-Robot Interaction (HRI) in open human-occupied spaces. The project builds on a study conducted by Cornell University in 2023 [1], which used a Wizard-of-Oz experiment to explore Human-Robot Interactions in open spaces. The main goal is to utilize commercially available low-cost components to design and build a reliable and low-cost robotic platform to address the challenge of autonomous navigation and Human-Robot Interaction in unpredictable environments. The hardware includes a hoverboard-based motor system, ODrive motor controllers, a Raspberry Pi 5, and various sensors, including the RealSense D415 camera. The robot’s software stack is built on the Robot Operating System, facilitating robust communication and control mechanisms. Key functionalities such as human detection and following are implemented using computer vision techniques, like Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) classifiers, and deep learning models like You Only Look Once version 8 (YoloV8). The robot platform has been thoroughly tested and validated in terms of basic motion, motor precision, durability, and load capacity. Using ODrive Motor Controllers allowed configurable and precise control, and the utilization of hoverboard hardware allows the robot to carry weight in excess of 80 |
Supervisor(s) |
Hang Yin |
External examiner(s) |
Jeppe Revall Frisvad |
Date and time |
June 27 2024, 15:30-16:30 |
Room |
Image Hot Room – Image Section, UP1 |
Name of student(s) |
Emil Høghsgaard Hansen and Bruce Isiah Thomas Esplago |
Study Programme |
Computer Science Thesis |
Title |
Leveraging Computer Vision for Housing Price Estimation |
Abstract |
Automated models have been widely used in house price assessment. Many models have been developed with numerical and categorical features (size, location, etc) as the primary prediction indicator. In this study, we examine whether and how images in the form of floor plans can be used as a supplement to feature-based models. In connection with this, we have developed a net-scraper that can retrieve relevant data and floor plans. Based on the collected data, we have investigated the efficiency and |
Supervisor(s) |
Bulat Ibragimov |
External examiner(s) |
Veronika Vladimirovna Cheplygina |
Date and time |
June 28 2024, 11:00-12:00 |
Room |
SCI-DIKU-UP1-1-1-N116B |
Name of student(s) |
Dong She |
Study Programme |
Computer Science Thesis |
Title |
Blood Vessel Mesh Generation |
Abstract |
This master’s thesis explores vessel mesh generation and blood Computational Fluid Dynamics (CFD) simulation in depth. Accurate modeling of blood vessels is crucial for understanding hemodynamics and diagnosing cardiovascular diseases. The primary objective of this study |
Supervisor(s) |
Kenny Erleben |
External examiner(s) |
Jakob Andreas Bærentzen |
Date and time |
June 28 2024, 13:00-14:30 |
Room |
UP1, 3:2:20 |
Name of student(s) |
Jakob Flinck Sheye and Hristo Atanasov Georgiev |
Study Programme |
Computer Science Thesis |
Title |
Generative Models for Children's Head Motion in Resting State Functional Magnetic Resonance Imaging |
Abstract |
Motion artifacts are a major obstacle in MRI image acquisition, as they |
Supervisor(s) |
Melanie Ganz-Benjaminsen |
External examiner(s) |
Kristoffer Hougaard Madsen |
Date and time |
June 28 2024, 11:00-13:00 |
Room |
SCI-DIKU-UP1-2-0-04 |
Name of student(s) |
Mads Daugaard and Emil Christoffer Riis-Jacobsen |
Study Programme |
Computer Science Thesis |
Title |
Simulating Head Motion in MRI: A Silicone Phantom Approach with Machine Learning Integration |
Abstract |
Due to long image acquisition times in magnetic resonance imaging (MRI), it is prone to image artefacts caused by patient motion, which is especially prevalent for children, potentially resulting in unsuccessful diagnoses. As a result of this, many MRI examinations need to be repeated, occasionally requiring the use of general anaesthesia to limit patient motion, both being a costly process. Consequently, research in motion correction methods has become of great interest. In this thesis, we propose the use of a 6 degrees of freedom cable-suspended parallel robotics (CSPR) system with machine learning integration for inverse kinematics, used to induce head motion of a silicone-based phantom head inside an MR scanner. The system aims to provide a reliable and reproducible method for motion simulation in MRI to facilitate training and validation of motion correction methods. Through an iterative design and manufacturing process, we develop a method for 3D printing and casting a silicone-based phantom using hard shell moulds, finding that our method shows great promise but that better equipment may be required to eliminate all MR artefacts. A CSPR system for controlling the phantom head inside an MR head coil is developed and |
Supervisor(s) |
Melanie Ganz-Benjaminsen |
External examiner(s) |
Kristoffer Hougaard Madsen |
Date and time |
June 28 2024. 08:30-09:30 |
Room |
DIKU-UP1-2-0-04 |
Name of student(s) |
Runfei Wu |
Study Programme |
Computer Science Thesis |
Title |
Towards High-Fidelity Simulation of the Human Colon Modeling A Deformable Tube |
Abstract |
Colorectal cancer is among the most prevalent cancers worldwide, with |
Supervisor(s) |
Kenny Erleben |
External examiner(s) |
Jakob Andreas Bærentzen |
Date and time |
June 28 2024, 14:30-16:00 |
Room |
UP1 3:2:20 |
Name of student(s) |
Christoph Alexander Prehn |
Study Programme |
Computer Science Thesis |
Title |
Flying drones autonomously with Hierarchical Reinforcement Learning. A Study of Hierarchical Reinforcement Learning for autonomous drones |
Abstract |
Flying drones autonomously with Hierarchical Reinforcement Learning Autonomous drone flight is one of the most complex task within robotics, while it at the same time offers a multitude of applications in the real world. From inspecting difficult or dangerous to access areas to finding people in avalanche regions, autonomous drones offer the potential to make various task safer and more efficient. Even though Reinforcement Learning and Control Theory have shown impressive performance on individual task, they require meticulous modelling of the environment or extensive training. Simultaneously they struggle to adapt to perturbations in the environment or transfer to new task. Hierarchical Reinforcement Learning enables agents to utilize temporal abstraction and hierarchical structures similarly to the thought process of humans. In previous research, this has shown improved performance and strengthened robustness against environment perturbations in simple tasks. For complex task, like autonomous drone flight, the research is limited. |
Supervisor(s) |
Stefan Sommer |
External examiner(s) |
Dan Witzner Hansen |
Date and time |
June 28 2024, 13:00-14:00 |
Room |
Mødelokale A, Østervoldgade 3 |
Name of student(s) |
Jonas Hagel and Marcus Frostholm |
Study Programme |
Computer Science Thesis |
Title |
Ensuring safety using Barrier functions for collision detection and avoidance between multiple drones |
Abstract |
We construct a barrier function and explain how to utilise the barrier function for collision detection and avoidance in a drone swarm. We focus on modifying trajectories to ensure safe flight through a hoop. The collision potentials are computed by integrating line segments of different trajectories, and gradients of barrier values are utilised for modifying trajectories. We test our implementation in the Webots simulator by mimicking the Crazyflie 2.1 platform. The results shows success with three drones, flying in collisionfree trajectories in order to guarantee safety for the drones and complete the task of flying through a hoop. This highlights the use of barrier functions in producing safe and reliable trajectories, showing their potential in achieving collision detection and avoidance. Efforts were made to implement a physical experiment, combining software and hardware, setting the foundation for future work. |
Supervisor(s) |
Kenny Erleben |
External examiner(s) |
Jakob Andreas Bærentzen |
Date and time |
June 28 2024, 10:30-12:00 |
Room |
UP1 3:2:20 |
Name of student(s) |
Marie Elkjær Rødsgaard |
Study Programme |
Computer Science Thesis |
Title |
HybridGNet: Exploring medical image segmentation and shapes |
Abstract |
The HybridGNet is a new segmentation method that explores how to improve anatomical image segmentation in the medical field. The HybridGNet uses landmark-based segmentation to output a segmentation graph, unlike a traditional segmentation U-Net which outputs a pixel-level segmentation. This project investigates what shape models neural networks such as U-Net are and how this then relates to the HybridGNet method. Several experiments have been done with the segmentation methods. The data consists of synthetic data of smileys and X-ray datasets containing lungs. The results of these experiments show that the HybridGNet is a viable alternative to the U-Net when segmenting images where shape is the defining factor. |
Supervisor(s) |
Erik Bjørnager Dam |
External examiner(s) |
Dan Witzner Hansen |
Date and time |
June 28 2024, 11:00-12:00 |
Room |
Observatoriet på Østre Voldgade (Pioneer centret) |
Name of student(s) |
Tim Ruschke |
Study Programme |
Computer Science Thesis |
Title |
Guided Synthesis of Labeled Brain MRI Data Using Latent Diffusion Models for Segmentation of Enlarged Ventricles |
Abstract |
Scarcity, inhomogeneity, and privacy are common obstacles for deep learning in a medical context. While synthetic data appears as an ostensibly easy solution, research has shown time and time again that training with synthetic data fails to perform as well as with real data. In the context of ventricular segmentation in brain MRI images, we present a proof of concept for the successful use of synthetic data in training segmentation models. State of the art segmentation models often struggle to accurately segment patients suffering from enlarged ventricles due to afflictions like normal pressure hydrocephalus. We show that synthetic data can serve to address this by customizing the distribution of ventricular volume in the training set. We employ two latent diffusion models, a mask generator and a corresponding spade image generator, to create labeled 3D brain |
Supervisor(s) |
Martin Nørgaard |
External examiner(s) |
Veronika Vladimirovna Cheplygina |
Date and time |
June 28 2024, 13:00-14:00 |
Room |
SCI-DIKU-UP1-2-0-06 og SCI-DIKU-UP1-2-0-04 |
Name of student(s) |
Nicklas Boserup |
Study Programme |
Computer Science Thesis |
Title |
Score Learning for Parameter Inference in Stochastic Shape Evolutions |
Abstract |
In the field of computational evolutionary morphometry, inference of |
Supervisor(s) |
Stefan Sommer |
External examiner(s) |
Dan Witzner Hansen |
Date and time |
June 28 2024, 14:00-15:00 |
Room |
Mødelokale A, Østervoldgade 3 |
Name of student(s) |
Ying Liu |
Study Programme |
Computer Science Thesis |
Title |
Exploration of Self-Supervised Learning Methods for Longitudinal Image Analysis |
Abstract |
The advent of self-supervised learning has alleviated the bottleneck |
Supervisor(s) |
Jens Petersen |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
June 28 2024, 09:00-10:00 |
Room |
SCI-DIKU-UP1-1-1-N116A |
Name of student(s) |
Hanwen Zhang, David Rasmussen Lolck and Shuyi Yan |
Study Programme |
Computer Science |
Title |
Correlation Clustering: The Power of Preclustering |
Abstract |
We study preclustering in the correlation clustering problem. Since Bansal, Blum, and Chawla [BBC04] introduced the correlation clustering problem, it has been playing an important role in machine learning and data mining. Recently, a new technique called preclustering has been used in many up-to-date correlation clustering algorithms and demonstrates a very |
Supervisor(s) |
Mikkel Abrahamsen & Mikkel Thorup |
External examiner(s) |
Kasper Green Larsen |
Date and time |
27.08.2024 13:30 - 15:30 |
Room |
Online |
Name of student(s) |
Zhouyang Meng |
Study Programme |
Computer Science |
Title |
Federated Learning for Wearable Sensor Analytics: A Flutter Application for Individualized Dosage Decision Support |
Abstract |
Optimizing drug dosages is critical for maximizing treatment efficacy and safety. However, traditional dosing guidelines often do not take into account the factors of the individual patient, resulting in suboptimal outcomes. The rise of wearable sensors presents an opportunity to develop personalized dosing strategies by continuously monitoring patient health |
Supervisor(s) |
Boris Düdder |
External examiner(s) |
Mads Rosendahl |
Date and time |
27.08.2024 14:00 - 15:00 |
Room |
Up1-2-0-15 |
Name of student(s) |
Janus Jonatan Hannesarson |
Study Programme |
Computer Science |
Title |
Fusing Temporal and Aggregation Operators in TimelyMon |
Abstract |
Organisations of today are subject to increasingly stricter policies in their handling of sensitive data, as are the organisations’ concerns on whether they remain in compliance. Monitors aim to solve this problem by monitoring the events of a monitored system with respect to a security policy, and by reporting any violations. In recent years several |
Supervisor(s) |
Supervisor: Dmitriy Traytel |
External examiner(s) |
Alceste Scalas |
Date and time |
28.08.2024 16:00 - 17:00 |
Room |
Sigurdsgade 41, room 2-03 |
Name of student(s) |
Emma Cathrine Liisborg Leschly |
Study Programme |
Computer Science |
Title |
Toward Interpretable Multimodal Deep Learning for Neurological Assessment |
Abstract |
Neurodegenerative conditions pose a significant and growing global health |
Supervisor(s) |
Melanie Ganz-Benjaminsen, Line Katrine Harder Clemmensen, and Vikram Ramanarayanan |
External examiner(s) |
Morten Mørup |
Date and time |
30.08.2024 9:00-10:00 |
Room |
2-00-04 at DIKU |
Name of student(s) |
Gustav Hanehøj |
Study Programme |
Computer Science |
Title |
Fast Blocked Parallel Volume Meshing of Renal Arterial Networks |
Abstract |
The renal arterial network plays an important medical role as a distribution network, and simulation of its blood flow has been blocked by the lack of a full-scale computational volume mesh model. Recent work has provided a full scale skeleton model of a rat renal network, however the currently preferred inverse skeletonization technique, convolution surfaces, has issues handling the large scale variability in the network due to a fixed resolution requirement. |
Supervisor(s) |
Kenny Erleben |
External examiner(s) |
Jakob Andreas Bærentzen |
Date and time |
30.08.2024 13:00 - 14:30 |
Room |
DIKU, up1 3.2.20 |
Name of student(s) |
Casper Lisager Frandsen |
Study Programme |
Computer Science |
Title |
CT-MRI cross-domain registration for better brain segmentation |
Abstract |
This paper investigates an extension of the SynthMorph network |
Supervisor(s) |
Mads Nielsen |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
30/8 2024 at 14:00 |
Room |
Østervoldgade 3 |
Name of student(s) |
Gavin Leo Menezes |
Study Programme |
Computer Science |
Title |
Beyond Touch: Innovating Tactile Sensations with Smart Fluids in Virtual Environments |
Abstract |
Our study explores the enhancement of tactile feedback in virtual reality |
Supervisor(s) |
Daniel Lee Ashbrook |
External examiner(s) |
Eve Hoggan Christensen |
Date and time |
13.09.2024 11:00 |
Room |
Sigurdsgade 0-11 |
Bioinformatics
Name of student(s) |
Cheng Chen |
Study Programme |
Bioinformatics |
Title |
Unsupervised learning of multi-omics and phenotype data in the UK Biobank |
Abstract |
To generate deeper understanding of complex phenotypes we need to |
Supervisor(s) |
Anders Krogh |
External examiner(s) |
Ole Lund |
Date and time |
June 11 2024, 15:30 - 16:30 |
Room |
Panum, Mærsk Tower, floor 8, room 145A. |
Name of student(s) |
Lucas Phillip Krieger |
Study Programme |
Bioinformatics |
Title |
Exploring the Capabilities of Protein Language Models at Predicting Glycosylation |
Abstract |
Glycosylation is the most abundant and diverse form of protein post-translational modification. O-glycosylation biosynthesis starts when a group of twenty partially redundant polypeptides transfer an N-acetylgalactosamine (GalNAc) to a serine or threonine in the |
Supervisor(s) |
Hiren Joshi, Wouter Boomsma |
External examiner(s) |
Henrik Nielsen |
Date and time |
June 12 2024, 14:30 - 15:20 |
Room |
07-10-143a in the Mærsk tower |
Name of student(s) |
Yifan Sun |
Study Programme |
Bioinformatics Thesis |
Title |
Extension and application of a side-chain customization protocol for receptor probe and drug discovery |
Abstract |
G protein-coupled receptors (GPCRs) are crucial in mediating the actions of approximately two-thirds of human hormones, the majority of which, around 71%, are peptides or proteins. Given their pivotal role in numerous physiological processes, GPCRs represent critical targets in drug discovery. However, designing ligands that effectively target GPCRs is particularly |
Supervisor(s) |
Wouter Boomsma |
External examiner(s) |
Jes Frellsen |
Date and time |
June 13 2024, 10:00-11:00 |
Room |
UP1-2-0-04 |
Name of student(s) |
Yan Li |
Study Programme |
Bioinformatics Thesis |
Title |
Development of a deep generative model for cancer gene expression and clinical data combined |
Abstract |
This thesis aims to handle the multi-modality metadata in the TCGA |
Supervisor(s) |
Anders Krogh |
External examiner(s) |
Jes Frellsen |
Date and time |
June 20 2024, 14:30-15:30 |
Room |
Panum, mødelokale 33.4D |
Name of student(s) |
Rasmus Alex Buntzen-Frederiksen |
Study Programme |
Bioinformatics Thesis |
Title |
Predicting contaminated DNA samples within the class of Insecta from DNA and image embeddings |
Abstract |
Contaminated DNA samples pose significant challenges in biological research. Current methods for detecting contamination are often time-consuming and lack precision, necessitating the development of more robust, automated approaches. This thesis addresses this need by exploring a multi-modal machine learning model that integrates both image and DNA data to enhance contamination detection accuracy. The primary objective of this work is to develop and evaluate the feasibility and performance of a multi-modal machine learning model for predicting DNA contamination, using specimen images and DNA sequences. The methodology for this project uses deep convolutional neural networks (VGG16, ResNet-34 and EfficientNetB0) to extract image features |
Supervisor(s) |
Kim Steenstrup Pedersen |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
June 21 2024, 10:05-11:05 |
Room |
Konferencelokalet på Zoologisk Museum, UP 15 |
IT and Cognition
Name of student(s) |
Xiangyu Lu |
Study Programme |
IT and Cognition Thesis |
Title |
Fiber Break Prediction Using 3D Generative Models |
Abstract |
Reliable failure predictions of fiber-reinforced composites (FRCs) are crucial for ensuring the safety of products, reducing costs, and optimizing performance. Investigating the underlying mechanism of tensile failure of individual fibers is an important research direction. In this thesis,we aim to predict potential fiber breaks by synthesizing computed tomography (CT) images of FRCs at higher forces based on CT scans taken under initial force. We developed and evaluated 3D conditional generative adversarial networks (c-GANs) with 3D U-Nets as generators to generate CT images of fiber structures under increased force. Our models obtained MAE and |
Supervisor(s) |
Abraham George Smith |
External examiner(s) |
Melih Kandemir |
Date and time |
June 10 2024, 09:30-10:30 |
Room |
DIKU UP1-2-0-15 |
Name of student(s) |
Ben Yao |
Study Programme |
IT and Cognition Thesis |
Title |
Self-supervised Pre-training for Quantum Natural Language Processing |
Abstract |
Quantum computing has been broadly applied to many AI fields and achieved impressive results. However, the quantum machine learning models are limited to the inherent linearity in quantum computing architecture, resulting in their constrained capabilities and adaptability. |
Supervisor(s) |
Qiuchi Li |
External examiner(s) |
Troels Andreasen |
Date and time |
June 28 2024, 10:00-11:00 |
Room |
DIKU UP1-2-0-06 |
Name of student(s) |
Adrianna Helena Klank |
Study Programme |
IT and Cognition Thesis |
Title |
Diffusion Models in 3D Medical Image Synthesis and Forecasting Radiotherapy Outcome for Lung Cancer Treatment |
Abstract |
The aim of this work is to develop a method for forecasting the progress |
Supervisor(s) |
Jens Petersen |
External examiner(s) |
Rasmus Reinhold Paulsen |
Date and time |
June 28 2024, 10:00-11:00 |
Room |
SCI-DIKU-UP1-1-1-N116A |
Name of student(s) |
Zixuan Xu |
Study Programme |
IT and Cognition |
Title |
Pose-Based Person Fall Detection |
Abstract |
As the global population ages, with a significant increase in the number of elderly individuals, the need for effective human fall detection systems becomes increasingly crucial. Existing vision-based methods, which rely on features extracted from single frames, often struggle to capture the dynamic nature of falling actions and lack generalization. In this thesis, we introduce a newly created dataset with clip-level labels, specifically designed to address these challenges. Our approach leverages state-of-the-art pose estimation to detect falling actions within clips. Our model, enhanced by data augmentation and rigorous ablation studies, demonstrates superior performance despite its simplicity, and strong generalization across diverse scenarios. We further conduct experiments to analyze the importance of different keypoints, providing valuable insights and contributing to the explainability of our model. |
Supervisor(s) |
Francois Lauze, Kamal Nasrollahi & Zenjie Li |
External examiner(s) |
Veronika Vladimirovna Cheplygina |
Date and time |
03.09.2024 15:30 - 16:30 |
Room |
Image section study office (3.2.09) |
Health Informatics
Name of student(s) |
Alexander Haderup Alsing and Felix Björklund Osmark |
Study Programme |
Speciale i Sundhed og Informatik |
Title |
Teknologisk løsning til arbejdsgangen ved supervision i Vesterbro Lægehus |
Abstract |
Background: |
Supervisor(s) |
Henriette Mabeck |
External examiner(s) |
Yutaka Yoshinaka |
Date and time |
June 18 2024. Alexander Haderup Alsing: 15:15-16:15 & Felix Björklund Osmark: 16:15-17:15 |
Room |
2.3.I.164 på NBB |
Name of student(s) |
Luca de Gobbi |
Study Programme |
Sundhed og Informatik |
Title |
The current scope of research on the use of extended reality (XR) as a tool to treat and study addictions: a Scoping Review |
Abstract |
BACKGROUND: Addiction is a global issue which represents a significant burden on health, society, and national economies. Addiction is associated with premature mortality, high economic costs due to lost productivity, increased healthcare expenses and increased crime rates. Starting from the early 2000´s, research has been exploring the use of Extended Reality (XR) applications as an alternative tool to analyse and treat addiction. |
Supervisor(s) |
Teresa Hirzle |
External examiner(s) |
Niels van Berkel |
Date and time |
22.08.2024 13:00 - 14:00 |
Room |
Sigurdsgade 41 – room 0-11 |