Prosjekt 2024

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Oppgaveforslag (356)

“Designing a Next Generation Patient-Centric Electronic Health Record (EHR) Platform Based on Decentralised Technologies”

Introduction:

Faglærer: Abha Pokharel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

“Empowering Patient Privacy and Security with Self-Sovereign Identity in Electronic Health Records”

 

Faglærer: Abha Pokharel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[2023/2024 Collaboration with Fürst] Deep Learning for Analysis of patients' time-series: prediction and interpretability - Thyroid disease / Prostate Cancer Detection

Introduction of the company

Faglærer: Massimiliano Ruocco     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[2023/2024 Collaboration with Refinitiv AS] - Deep Learning Models for detecting wind turbines’ curtailment

[More information here]

Faglærer: Massimiliano Ruocco     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[2023/2024 Collaboration with Telenor Research] Domain Adaptation in Telecom Traffic Data: Investigating Representation Learning Techniques for Handling Varying Features and Data Distributions

Key concepts: domain adaptation, representation learning, transformers, self-supervision.

Faglærer: Massimiliano Ruocco     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[2023/2024 Collaboration with Telenor Research] Domain Adaptation in Telecom Traffic Data: Investigating Representation Learning Techniques for Handling Varying Features and Data Distributions

Key concepts: domain adaptation, representation learning, transformers, self-supervision.

Faglærer: Massimiliano Ruocco     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[2023/2024] - Approximating computational fluid dynamics (CFD) simulations using Deep Neural Network

More details here. (thesis in collaboration with NablaFLow AS)

Faglærer: Massimiliano Ruocco     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[2023/2024] Algorithmic trading for crypto: building robots with ML so we can chill

Problem description #1: “Position sizing for statistical arbitrage: Cut your losses short and let your profits run”

Faglærer: Massimiliano Ruocco     Status: Valgbart     Egnet for: En student     Lenke: plink

[2023/2024] Exploring the Effectiveness of Synthetic Data Generation in the Air Traffic Management Domain

[Collaboration between NTNU, Sintef Digital and TU Delft]

Faglærer: Massimiliano Ruocco     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[BitPet] AR Game to Motivation Socialisation and Physical Activity 2024/2025

This project aims to develop game mechanics that will motivate users to socialize and be physically active using Augmented Reality. It is part of the BitPet project, which aims for commercialization. Developers in BitPet will provide technical support.

Faglærer: Alf Inge Wang     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[ExerGames] Multi-player pedal-game 2024/2025

The goal of this project is to design and develop new game concepts for a game where an exercise bike is used as a game controller in addition to traditional game input through multiple buttons. In addition to input from buttons, the player should control the game by using her/his fit to move the pedals. The goal of the game is to have fun that can last over time and get physical exercise. The game should be implemented in Unity using an API provided for the exercise bike controller.

Faglærer: Alf Inge Wang     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[ExerGames] Play to get fit 2024/2025

In this project, the goal is to develop new game concepts and technologies for exergames - games where the player performs physical exercise. There are several approaches to exergames, and the challenge is to find the balance between something that is fun to play and getting real physical exercise from playing the game.

Faglærer: Alf Inge Wang     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[Lecture Games] Collaborative classroom learning games 2024/2025

The goal of this project is the design, implementation, and evaluation of a collaborative learning game in which the students beat the game together and learn at the same time. The game will have to balance engagement and learning to make it fun and educational. Another requirement is that it must be a multiplayer game in which all the students in a call can participate at the same time.

Faglærer: Alf Inge Wang     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Automatic Reading of Traffic Announcements

The objective of this project is to develop an automatic system for reading traffic announcements that can broadcast messages to the public through the "traffic announce" channel or VMA in Sweden. The system will use speech recognition and text-to-speech technology to automatically read out traffic announcements and other important messages to drivers and passengers, regardless of whether they are listening to the radio.

Faglærer: Lemei Zhang     Status: Valgbart     Egnet for: En student     Lenke: plink

[NorwAI] Automatic sign language detection

Description in which company/unit the thesis will be placed:
The Norwegian Research Center for AI Innovation (NorwAI).

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Benchmarking Graph-based Recommendation for Reproducible Evaluation and Fair Comparison

Description in which company/unit the thesis will be placed:
This master thesis will be carried out at the Norwegian Research Center for AI Innovation (NorwAI), NTNU.

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Card usage patterns for automatic problem detection (with DNB)

DNB is Norway's largest bank and one of the country's largest financial institutions. In the section PM Digital Cards, we have responsibility for all IT system for Cards, including the monitoring of the cards usage. Our systems handle over 16 million cards transactions weekly, and is thus part of Norway’s critical financial infrastructure.

Faglærer: Kjetil Nørvåg     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Conversational agents in the Norwegian financial sector

Description in which company/unit the thesis will be placed:
The Norwegian Research Center for AI Innovation (NorwAI).

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Exploring the use of AI for automated political speech analysis

Description in which company/unit the thesis will be placed:
The Norwegian Research Center for AI Innovation (NorwAI).

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Mobility Data with Telenor Research

This project is connected to the Norwegian Research Center for AI Innovation (NorwAI) and the Norwegian Open AI Lab (NAIL). 

Faglærer: Heri Ramampiaro     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

[NorwAI] Neural Machine Translation in Written Norwegian - Bokmål and Nynorsk - with Generative Pre-trained Transformer

Description in which company/unit the thesis will be placed:
This master thesis will be carried out under the project "Generation of Large-Scale Norwegian GPT-2 Language Models" supported by the Norwegian Research Center for AI Innovation (NorwAI).

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Operator learning for surrogate models of safety critical systems

 

Faglærer: Boye Annfelt Høverstad     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Pre-trained Language Models for Session-based Recommendation

Description in which company/unit the thesis will be placed:
This master thesis will be carried out under the project "Generation of Large-Scale Norwegian GPT-2 Language Models" supported by the Norwegian Research Center for AI Innovation (NorwAI).

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: En student     Lenke: plink

[NorwAI] Real-time Subtitling Service for Radio Streams and Podcasts

This master project will be conducted in the Norwegian Research Center for AI Innovation (NorwAI).

Faglærer: Lemei Zhang     Status: Valgbart     Egnet for: En student     Lenke: plink

[NorwAI] Sentiment Analysis Based on Brain Signal Feedback

This is a joint project between NorwAI and Kavli Institute for Systems Neuroscience, NTNU.

Faglærer: Lemei Zhang     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI] Value of AI Technologies

AI and Big Data technologies are central in many digitalization or digital transformation projects. They are sometimes used to cut costs of existing services, but the technology is often adopted to offer improved or brand new services. There are today best practices from many industry sectors, and practitioners are researchers have worked out guidelines for the integration of AI in organizations. However, many projects suffer from a lack of understanding how the nature of AI affects technical infrastructures and business strategies, and social and ethical implications are often underestimated and not sufficiently addressed.

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI/Cognite] Hybrid Approaches for Blocking

Entity Matching/Resolution is the task of identifying which records refer to the same real-world entities across one or more data sources. It is a key data integration task with decades of research behind it. One of the main challenges is the balance between computational efficiency and precision. Since the number of potential matches is quadratic in the number of records it is infeasible to compare all pairs, but at the same time it is necessary to compare records explicitly to achieve high precision. Therefore, the task is often performed in two separate steps. First, we generate a reasonable (linear in number of records) number of candidate pairs in a step called blocking - hopefully removing most obvious non-matches. Then, secondly, we explicitly compare all candidate pairs and classify them as either match or non-match.

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI/Cognite] Point Cloud Instance Segmentation Large Scale Process Plants

The purpose of this document is to propose a research topic for master-level students interested in 3D deep learning that can have huge values for Cognite. The end goal of the project should be a publication as well as the solution.

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI/DNB] Conversational robots

Conversational agents (chatbots) are systems that can enter into dialogues with real users.  Most current chatbots are rule-based, but some newer systems are based on generative models and machine learning. 

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

[NorwAI/NTB] Real estate robot

Description in which company/unit the thesis will be placed:
NTB (Norsk Telegrambyrå AS) is the national news agency in Norway, owned by the media industry. NTB is a member of international organizations of news agencies, and is considered one of the most innovative agencies in the business.
A daily production of at least 150 news articles, 50.000 pictures added daily to our picture service and several services on sports data, picture storage, press release and other services, has been the basis of NTB during the last years.
A groundbreaking work on picture recognition with AI has made us the leading actor in Scandinavia, with 15.000 persons now automatically recognized. Our work on automated article production has been ahead of the rest of the business in Norway. Our automated translation service between Bokmål and Nynorsk has changed the way we produce news and is a service used by media companies, governmental institutions and private businesses.
We are using technology to change the way NTB can continue being the closest partner to our customers.

Faglærer: Jon Atle Gulla     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

3D oriented data augmentation for Deep Object Detection

As it is well known, recent deep learning methods allow unprecedented performance for image classification, semantic segmentation, and object detection tasks. When building object detectors for marine applications, it is particularly important to have training sets which do not only contain a sufficient number of relevant object classes (ships, piers, buoys, ...), but also show the different variations in lighting conditions, and weather conditions (fog, rain, snow, ...). As it is difficult, if practically possible at all, to record a sufficient number of such images or videos for training object detector networks, this project aims at artificially generating these variations of image conditions, in particular different illumination and strongly different weather conditions.
Image augmentation is a well-known technique for synthetically expanding limited or imbalanced training datasets used in computer vision tasks to improve generalization performance and to avoid overfitting. Likewise, augmentations can be used for testing network generalization performance, by imposing varying augmentations, e.g. two-dimensional augmentations such as shifts, rotations, etc., as well as natural phenomena, e.g. snow, fog, lighting conditions, etc., or even adding synthetic objects to the scene. The former is straightforward using simple transformations in two dimensions, whereas the latter should imitate realistic, and highly stochastic, scenarios that in reality depends on the depth information of the scene. Nonetheless, this is often done by basic image manipulations imposed uniformly over the whole scene without considering depth information and may result in unrealistic augmentations not representing reality and could possibly produce erroneous testing performance. As an example, fog or rain may be observed only at a certain distance and/or area and affects only specific far-away objects, rather than all objects in the scene. The proposed research topic is therefore to investigate techniques for extracting depth information from images or video and taking advantage of this information to apply augmentations of natural phenomena in 3-Dimensions.
For the extraction of this 3D information, one can use recent methods of single image depth estimation by deep neural networks, or "motion stereo" from a moving camera (in case of video sequences). Both approaches can also be combined.
Therefore, the first partial goal of this project is to explore methods for depth estimation for given training images or videos.
The second goal is to use the extracted estimate of the depth structure to simulate the desired weather and illumination situation, such as fog, rain, or snow. Again, this can be done by "classical" physics-based computer graphics modeling, and/or by suitably trained deeep neural networks.
Another important aspect is to benchmark 2D and 3D synthetic augmentations against images containing actual natural phenomena’s, i.e. network performance on synthetic scenes against true scenes.
This project will be performed with the Norwegian company DNV GL which is interested in quality assurance of marine detection systems, and will also be related to the marine multi-sensro simulator project currently pursued at NTNU.
Students who are interested in this project are advised to address Prof. Rudolf Mester at IDI for more details. The co-supervisors will come from DNV and probably also from the Cybernetics Department (ITK).

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

3D visualization of ship-breaking status and location of personnel

More than 80% of our global goods are transported by ships. Like the goods they transport, ships will eventually become waste and need to be broken down properly. The process of breaking down a ship involves a lot of people working at different parts of the ship. Currently, ship-breaking workers rely on noise to detect if there are other workers nearby. This practice is unsafe, since there is a possibility that the noise is unheard, and workers may not realize there are others nearby when performing the cutting process. Therefore, there is a need for visualizations that show the ship-breaking status and the presence of ship-breaking workers on-site. Such visualizations would be useful to improve safety in ship-breaking activities, as workers could check before cutting anything.

Faglærer: Yngve Dahl     Status: Valgbart     Egnet for: En student     Lenke: plink

3d-to-2d Face Recognition using Machine Learning

The main goal of this Thesis is a 3D-to-2D Face Recognition approach based on a combination of classical deep learning networks and the new trend in deep learning branch, Geometric Deep Learning Networks.

Faglærer: Theoharis Theoharis     Status: Valgbart     Egnet for: En student     Lenke: plink

A game engine based simulator framework for Otter USV (in cooperation with Maritime Robotics, H2024-V2025)

Main goal:

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: En student     Lenke: plink

A qualitative study of data and value in Helsplattformen

In 2019, the healthcare region of Central Norway procured an Electronic Health Record (EHR) system from an American vendor (Epic Systems) to fulfill the national vision of “one citizen, one journal” – the project is called Helseplattformen (HP). In 2022, HP implemented an integrated EHR system connecting primary healthcare providers in Trondheim municipality and secondary providers at St. Olav’s Hospital.

Faglærer: Babak A. Farshchian     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

A qualitative study of Microsoft Copilot use by computing students at NTNU

In this project, we plan to study the use of Microsoft Copilot by computing students at the NTNU using qualitative research methods to gain a rich understanding of the phenomenon. The candidate will do initial literature studies on the topic and design a case study with data-collection methods like observations, interviews, and archival data. The collected data will be analyzed qualitatively to define a coherent concept explaining the practices of computing students using Copilot. The subject areas for this project include information systems, computer-supported cooperative work, and human-computer interaction.

Faglærer: Babak A. Farshchian     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

A shift from old-school to cutting-edge – redefined homecare services delivery platform

Introduction

Faglærer: Surya Bahadur Kathyat     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AD - AI-agents trained end-to-end by Imitation and Reinforcement learning in simulated environments (2024)

Training of AI agents in virtual environments using deep reinforcement learning.

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AD - Visual Intelligence and accurate positioning on mobile devices

Task: Develop an app for mobile devices, that can be mounted in public transportation like busses, can access the camera of the device as well as relatively cheap and accurate positioning equipment with CPos corrections (cm accuracy) and have AI models for assessing and geo-referencing the condition of all road objects visible from the road (one application, other applications could be to create and update HD-maps, match real-time images to a reference for back-up localisation, collect data for neural rendering etc.).

Faglærer: Gabriel Kiss     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AD (Autonomous Driving), AI/ML/DL and Computer Vision (CV) (2024)

Various aspects related to AVs, including the use of NAP-lab's new research platform for AD.

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Adaptive Teaching Technologies to Personalise and Monitor Learning Activities

Progresso is a programming tutoring system that provides learners with personalised courses from various domains. Currently, it offers a Java programming course with interactive third-party material. The system provides infrastructure for collecting and displaying different learning analytics, logging learners' activities, customisation, and authentication of users.

Faglærer: Boban Vesin     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Advancing Identity Security through AI

In an era marked by increasing digital transactions and online interactions, ensuring the security and integrity of personal identities has become paramount. Traditional methods of identity verification, such as passwords and biometrics, are often susceptible to fraud and exploitation. However, the integration of Artificial Intelligence (AI) offers a promising avenue for strengthening identity security measures. By harnessing AI algorithms for identity verification, organizations can enhance accuracy, efficiency, and resilience against fraudulent activities. This proposal seeks to explore the implementation of AI-driven identity security systems to fortify the protection of individuals' personal information and prevent identity theft.

Faglærer: Surya Bahadur Kathyat     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AFFINE TRANSFORMATION QUIZ

Affine transformations are in the heart of Visual Computing and a very common topic in examinations. This project will look at ways of generating random affine transformation questions for examination settings. To be more generally applicable over the internet, it is intended to implement it in WebGL.

Faglærer: Theoharis Theoharis     Status: Valgbart     Egnet for: En student     Lenke: plink

AI - NeRF and Gaussian splats for mobility applications/ digital twin visualization

The vehicle industry, as well as software and hardware providers are rapidly developing sensor systems and artificial intelligence (AI) methods for sensing the road environment. Connected and Automated Vehicles (CAVs) are argued to have a large potential for accelerating traffic safety and efficiency. Digital twins allow not only to visualize how things work, but also simulate various future scenarios. This is particularly interesting for autonomous vehicles which can be trained in a simulated environment. Furthermore, changes to the algorithm can be validated in a digital twin before deployed on the vehicle. Building a digital twin of a nordic environment allows for development of AI techniques designed for such an environment.

Faglærer: Gabriel Kiss     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI applications for social good (AI4SG)

Supervisors: Ilias Pappas, Letizia Jaccheri

Faglærer: Ilias Pappas     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI computer vision for Open Discovery of Educational Augmented Reality Experiences

Technology

AI has leapfrogged many areas, including computer vision, enabling open discovery scenarios in Augmented Reality (AR) in Education. The thesis will replicate existing examples using openAI’s CLIP service (or YOLOv5) and evaluate its applicability for open discovery processes in an AR learning application. CLIP (or YOLOv5) are used to detect real-world objects in photos of the user’s physical environment, to enable scenarios like content search.

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: En student     Lenke: plink

AI for eyesight

This project works with diagnostics of eyesight issues and spectacles. It is carried out in close copperation with an international start up working with new ways of addressing eyesight problems. 

Faglærer: Anders Kofod-Petersen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI for knowledge work – how to succeed in practice?

It is well known that producing software for knowledge work is challenging. Knowledge can be tacit, social, produced through negotiation, and emerges in practice.

Faglærer: Marius Mikalsen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI for Mobile Robotics Systems: Let us define a project after your own interests (H2024-V2025)

Besides the more or less fixed topics which are found in the list, there is always the possibility to define a project according to your own interests, as long as it is scientifically solid or a real engineering challenge.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI for sustainable business models

The green shift is high on every executive’s agenda, and with good reason. The urgency of the climate crisis and associated transition to a sustainable society changes the way firms create, capture, and deliver value. Shifting the very fabric of today's business landscape. Firms must now deliver on a triple bottom line (environmental, social, and economic) and not only meet today's needs from customers and shareholders, but also future generations' needs and opportunities for value creation. A strategic response is required, and firms must make structural changes to accommodate a fully sustainable business model (SBM). Research suggests that firms that manage and mitigate their exposure to climate-change risks while seeking new opportunities for sustainable value creation will generate a competitive advantage over rivals in a carbon-constrained future. However, transitioning towards a SBM is challenging and companies often lack the necessary data and insight to make correct and effective business decision. Artificial Intelligence (AI) offers a possible solution by establishing a basis for data-driven and fact-based decision making. This makes it easier for firms to take a systems perspective, quantify impacts, and reduce the complexity of the sustainable transition. Although real and theorized examples of AI enabling SBMs exist, a comprehensive understanding of the relationship between AI and SBM is still missing, leaving a gap in our understanding of the underlying mechanisms and inhibiting firms’ ability to accelerate their sustainable transition. Thus, this project aims to take stock of current knowledge by studying the following research questions:

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI for understanding marine ecosystems by using underwater acoustic data

Even though oceans are very important for human life and societies, we have very little understanding of marine ecosystems which are very complex systems. Ocean observatories and other underwater monitoring systems provide data streams that cover physical, chemical and biological ocean properties.

Faglærer: Özlem Özgöbek     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI in the classroom: Actively learning from student feedback during the lectures

This project utilizes data science techniques to visualize and analyze open-text responses from students. We actively aim to gain valuable insights into students' perceptions in educational contexts by employing sentiment analysis. This will enhance our understanding of student experiences and actively inform strategies for improving teaching and learning practices by exploring innovative methodologies. The project endeavors to transform raw open-text responses into actionable insights by actively utilizing data science techniques such as natural language processing and machine learning algorithms. The dataset collected from student responses during lectures is expected to be substantial, potentially ranging from a few to hundreds of responses per question. This active engagement with cutting-edge techniques empowers us to extract meaningful insights that can inform tangible strategies for enhancing teaching and learning effectiveness.

Faglærer: Özlem Özgöbek     Status: Valgbart     Egnet for: En student     Lenke: plink

AI methods for CP Diagnosis

Brain injury in newborns accounts for more than 9 million years lived with disability worldwide. The functional consequences of early brain injury in newborns are commonly seen months or even years after birth, delaying therapeutic interventions and leaving families with uncertainty about their child’s health status for months and years.

Faglærer: Heri Ramampiaro     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

AI Techniques for Playing Imperfect Information Games

Imperfect Information Games are those in which players only know some but not all aspects of the state of the game.  Texas Hold'em poker is a classic example: each player knows their own two hole cards and the public cards (face-up on the table), but does not know the opponent's two hole cards.  Today's AI poker systems typically employ a combination of tree search and neural networks, where the latter serve as mappings, for example, from probability distributions over hidden states to winning probabilities.  These networks greatly reduce the size of the search tree.

Faglærer: Keith Downing     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI-assisted software project management - empirical studies

Artificial Intelligence (AI) and Agile methodologies are two powerful trends that are transforming the field of software engineering. AI can assist engineers in automating tasks and improving the quality of software development, while Agile methodologies provide a flexible and collaborative approach to software project management. This research project aims at discovering and evaluating a variety of application areas of ChatGPT in managing an Agile software development project, including how it can assist with planning, execution, and monitoring of software projects, how it can enhance testing activities, and how it can help with user experience design.

Faglærer: Anh Nguyen Duc     Status: Valgbart     Egnet for: En student     Lenke: plink

AI-based Environment Perception for Underwater Robots

This project is offered in the context of a big NFR-funded research project which is performed as a cooperation between the cybernetics department (ITK) and computer science (IDI). The overall project AROS deals with the task to provide autonomy to the snake underwater robots developed by ITK (Prof. Kristin Y. Pettersen and colleagues). 

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI-based Fair Recruitment Systems

Co-supervisor: Ahmed Abouzeid (postdoc in AI group, at NTNU)

Faglærer: Pinar Öztürk     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI-based Video analysis for animal tracking and behavior understanding

This project involves the utilization of advanced tracking and computer vision techniques like graph neural network, convex optimization, and self-learning to monitor and analyze the behavior and interactions of multiple animals in indoor settings, with the specific aim of supporting selective breeding efforts.
Selective breeding is a process in which animals are chosen for reproduction based on desirable traits to enhance specific qualities within a population. In this context, the project seeks to enhance the selective breeding process by applying technology-driven approaches to gather data and insights about the animals' behavior and characteristics.

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI-Driven Diagnostic Assistant for Medical Applications

The intersection of artificial intelligence (AI) and healthcare presents an opportunity to enhance medical diagnostics, improve patient outcomes, and streamline the work of healthcare professionals. This master project proposal invites students to contribute to this transformative field by developing an innovative web application. This application will leverage AI technologies to analyze medical data, offering insights ranging from data visualizations to complex diagnostics. The project integrates three key areas: scintigraphy image analysis, blood analysis data, and anamnesis analysis. However, students can choose the direction of their studies and focus on the area(s) of their interest.

Faglærer: Boban Vesin     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI4Pathology - Digital pathology, self supervised learning

The emergence of whole slide imaging technology (WSI) allows for digital pathology diagnosis. The applications of digital pathology are expanding, from lesion detection and segmentation, to quality assurance and prognostication. The specific application in this project is related to lung cancer staging and is a collaboration with St Olavs Hospital and Levanger Hospital. A relevant topic will be to develop and validate ML techniques for automatic assessment of WSIs from established, well-described cohorts of lung cancer patients.

Faglærer: Gabriel Kiss     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

AI4TEE - Generative/diffusion models for ultrasound acquisitions (GE Vingmed Ultrasound)

Ultrasound is becoming the imaging modality of choice for cardiac interventions. During cardiac surgery the location of instruments, as well as anatomic landmarks is crucial information for the surgeons. Today most of these tools are localized manually or semi-automatically, however automating them would improve the accuracy and patient safety.

Faglærer: Gabriel Kiss     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

An immune system approach to fake News Classification

An immune system approach to fake news classification is currently under development . It is an exciting new approach to Fake News classification, drawing inspiration from antibody and antigen concepts from nature. This project seeks to extend and refine the current approach in various ways. The student(s) may choose their own path. 

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

An Immune System Approach to Music recommendation

In a digital age, where users are faced with a significant amount of data, the recommendation system has become an essential part of everyday life to assist users in selecting products and services that are suitable for their needs. The music industry, in particular, has seen a rapid growth in the recent years, with users now able to access a greater variety of content. The need for user-tailored recommendations is, therefore, a necessity.
An ongoing project applies the AIRS immune system algorithm to content based filtering for Automatic Playlist Continuation (APC). This project can further explore the application of AIRS or a different immunt system approach for music recommendation and can extend content-based to content-based and collabroation-based.

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Aneo: Ensambles for probabilistic forecasts of wind power production

Description in which company/unit the thesis will be placed:
Aneo is a renewable energy company with near 300 employees and headquarters in Trondheim. Aneo aims to produce more renewable energy and develop new, unique products and services to promote further electrification and efficient utilization of renewable energy. AI plays a key role in achieving this goal due to the increasing need for automation and data- driven decision making. We have extensive experience supervising students with weekly meetings and access to AI and domain experts.

Faglærer: Odd Erik Gundersen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Aneo: Predictive maintenance of cooling systems in retail stores

Description in which company/unit the thesis will be placed:
Aneo is a renewable energy company with near 300 employees and headquarters inTrondheim. Aneo aims to produce more renewable energy and develop new, unique products and services to promote further electrification and efficient utilization of renewable energy. AI plays a key role in achieving this goal due to the increasing need for automation and data- driven decision making. We have extensive experience supervising students with weekly meetings and access to AI and domain experts.

Faglærer: Odd Erik Gundersen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Aneo: Why do wind turbines fail?

Description in which company/unit the thesis will be placed:
Aneo is a power producer with both hydro and wind plants – now we will do more even on wind. Aneo is already a major player in renewable energy for a better society. As the owner and operator of the power plants, Aneo takes care of renewable energy to monitor, control,and optimize the performance of the generation or transmission system or added values through system of computer-aided tools. Among which nowadays AI plays an even more important roles in such system in order to make better decision under increased complexity and risk. AI team in Aneo is responsible for providing the innovated, reliable and robust AI energy system for both Aneo and the third parties, and coordinates Aneo’s AI activities and membership in Norwegian Open AI Lab (NAIL) and participates in research work of the recently established Centre for Research-based Innovation NorwAI.

Faglærer: Odd Erik Gundersen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Annotating Norwegian Political Texts

In the era of Open Date, many parliaments publish their proceedings digitally. The transcribed speeches represent a valuable resource for political education. Social Sciences have invested efforts to analyze these text manually. AI promises to auomate analysis. Most machine learning methods require labeled data to find relevant patterns. These annotations are lacking at the moment.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Anomaly detection in x-rays

VinDR is an X-ray dataset that contains chest xrays depicting various medical anomalies within the lungs. They are categorized into fourteen abnormal conditions. Classifying between these conditions is a challenging task. This project aims to develop a model that can detect the type of anomaly and localize it.

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: En student     Lenke: plink

Anomaly detection using foundational time-series models

This project is about anomaly detection in time series data, i.e., detecting if a data-point or a short sequence of data-points inside a data-stream should be considered as “abnormal”. Anomaly detection is interesting in many real-world applications, like monitoring machinery to ensure it is a smooth process, finding fraudulent money transactions, etc. 

Faglærer: Helge Langseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Anvendt dyp læring for opsjonsprising

Denne oppgaven tar sikte på å bruke state-of-the-art AI-metoder for å forutsi opsjonspriser. Det er relevant å utforske ulike tilnærminger, som tidsserieprognoser og generative/probabilistiske modeller, for å forbedre nøyaktigheten i prediksjonene.

Faglærer: Boye Annfelt Høverstad     Status: Valgbart     Egnet for: En student     Lenke: plink

App development for improved circular economy solutions

The thesis will draw from the literature on circular economy. The student needs to review the literature and acquire a good overview of existing apps which are used by people to give away/exchange goods and offer recommendations to users to make the exchange process more effective and efficient. The focus can be on improving the process of offering recommendations, evaluate the usage of the app to find out in what ways it can be improved and find out what characteristics make the app more acceptable among its users in society. Based on the best practices from the literature, the candidates will develop the app and then do a user study to empirically test the proposed system. Finally, the candidate will analyze the collected data and write up his / her thesis.

Faglærer: Ilias Pappas     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Application of artificial intelligence-based image soft-sensor to high temperature property study

Objective:

Faglærer: Zhirong Yang     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Applying NorLLM to enrich knowledge graphs describing municipal / public sector data-driven problems

This project is in collaboration with Trondheim Kommune and the student(s) will get a co-supervisor. The project also involves using the Norwegian large language model (NorLLM) that is developed at NTNU.

Faglærer: Özlem Özgöbek     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Architecture-based reliability evaluation of machine learning pipelines

TL;DR: There exist methods for reliability evaluation of systems based on their architecture, for example Fault Trees. We want to investigate how those methods can be applied to machine learning pipelines. This would allow comparing different architectures based on their tolerance to faults. 

Faglærer: Leonardo Montecchi     Status: Valgbart     Egnet for: En student     Lenke: plink

Arealize Layout Generation 2 - ALG2

 

Faglærer: Kerstin Bach     Status: Valgbart     Egnet for: En student     Lenke: plink

Artificial Intelligence and Machine Learning wih Geophysical Applications

This projects focuses on artificial intelligence (AI) and machine learning (ML) and its application in geophysics, typically also involving the use of high-performance computers (HPC).  Beyond the technical AI/ML/HPC and application dimensions, one or more of the factors safety, explainability and sustainability may be important, depending on the joint interest of the student(s) and the professor(s). 

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Artificial Intelligence in Business: Uncovering challenges and obstacles of adoption.

The last few years have seen an explosion in interest regarding the use of Artificial Intelligence and much talk about the potential business value. Nevertheless, there is significantly less talk about the challenge's organizations will face when implementing such solutions and how they should overcome these obstacles. Inhibiting factors are not only of a technological nature but also include organizational and human factors. This project will involve collecting and analyzing data in collaboration with the researchers from the Big Data Observatory (https://www.observatory.no).

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

Artificial Intelligence in the Healthcare sector

Advanced forms of analytics and aritificlai intelligence are becoming increasingly deployed to support the work of healthcare workers. Medical doctors, nurses, and administrative staff either use, or are aided by sophisticated technologies which are posed to radically change the nature of their work. For example, radiologists now rely increasingly more on machine learning techniques to and other applications of AI to diagnose patients, while a lot of procedural and repeptive tasks are being done by machines. The objective of this project is to understand how the nature of work for health practitioners is changing, and what positive and negative consequences they experience.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

Artificial Intelligence, Cognitive Science, and Computer Games

Description: This project is about artificial intelligence (AI), cognitive science, and computer games. In particular, the goal of the project is to improve the understanding of cognitive functions of humans by having them play computer games, measure their brain activity, and analyze their performance by means of artificial intelligence techniques.

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Assessing students’ attitudes across various learning disciplines using sentiment analysis

You'll be required to implement and evaluate different aspect-based sentiment analysis (ASBA) models on a Coursea reviews dataset. 

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Assessment of the accuracy and precision of eye trackers

"In the wild" eye tracking is an umbrella term for recording eye movements of a subject that is in movement, opposed to more traditional eye tracking which requires the subject to remain stationary during experiments. This project has the aim of developing a framework for quantifying the (in)accuracy of eye tracking in VR. The thesis will involve finding existing literature in this domain to map existing solutions (if any), and to assess whether the best course of action is to try to improve upon these, or whether developing an entirely new solution seems to be the best course of action.

Faglærer: Alexander Holt     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Astroid clocking of nanomagnetic computing systems

Introduction

Faglærer: Johannes Høydahl Jensen     Status: Valgbart     Egnet for: En student     Lenke: plink

Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation: Bridging Medical Imaging and Natural Language Processing

The project focuses on integrating medical imaging data with natural language processing (NLP) techniques to automatically generate medical reports in an unsupervised manner.
The project aims to utilize autoencoders, a type of neural network architecture used for learning efficient data representations. In this context, an autoencoder will be employed to learn a compact representation of both medical imaging data and associated information, such as diagnostic annotations or patient information.
Similarly, with the knowledge graph, a structured representation will be created that captures relationships and connections between various medical concepts, which can include image features, diagnoses, treatments, and patient details.
The project aims to bridge the gap between medical imaging data and NLP by leveraging the encoded knowledge graph to generate coherent and informative medical reports automatically. This involves transforming the structured information into natural language narratives that accurately describe the medical images, findings, and clinical insights.

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Automated indoor tasks assisted by drones

There are many tasks both for indoor and outdoor which lend themselves to being carried out by (semi)autonomous drones. In this project the aim is to develop a system that allows drones to navigate through a building and carry out specific tasks. The system involves a number of main components that allow: dynamically mapping the environment and navigating it without collisions, planning and scheduling missions, managing a fleet of such devices, self-monitor status to ensure returning to the base / charging station.

Faglærer: George Adrian Stoica     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Automated indoor tasks assisted by rover like robots (continuation)

This project will build on a previous prototype

Faglærer: George Adrian Stoica     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Automated Single-Cell Type Annotation for Salmon Skin Disease Prevention

Collaboration

Faglærer: Zhirong Yang     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Automatic Metadata Generation: Language Technology at the Interface of Spoken and Written Norwegian

Background

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Autonomous Driving (AD): NAP-lab related projects based on Visual Intelligence (AI/CV)++ (2024)

The future of Autonomous Driving (AD) is end-to-end approaches (IL, IL+RL), simulators, closing the sim to real gap using Neural Rendering/Reconstruction, TeleDrive over 5G+ if the AD-car need assistance, HD-maps / Digital Twins (DTs) of the infrastructure where you want to drive autonomously, especially in Nordic winter environments (these HD-maps/DTs can also be used in the simulators), crowdsourcing to have an updated Digital Road Twin (e.g. mobile device with accurate positioning and geo-referencing), and automated anonymization of camera data - executed in a way that make the filtered images useful for training and verification of AI models.

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Autonomous Ferry -- Projects in AI-based Perception in Cooperation with Zeabuz (H2024-V2025)

The start-up company Zeabuz, based in Trondheim, works on the development of autonomous passenger ferries. Such systems are strongly dependent on reliable and powerful AI methods for environment perception ("where am I?", "where are other ships, persons, piers, ...?") and for situation assessment.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Autonomous Ferry -- Projects in AI-based Perception in Cooperation with Zeabuz (H2024-V2025)

The start-up company Zeabuz, based in Trondheim, works on the development of autonomous passenger ferries. Such systems are strongly dependent on reliable and powerful AI methods for environment perception ("where am I?", "where are other ships, persons, piers, ...?") and for situation assessment.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Beyond contemporary AI: A neural symbolic approach for anomaly detection in medical data

The project proposes an innovative method for anomaly detection in medical data by combining neural networks with symbolic reasoning techniques. The project explores neural symbolic approach which refers to a hybrid methodology that combines neural networks, which excel at learning patterns from large amounts of data, with symbolic reasoning, which focuses on representing knowledge and making logical inferences. Neural networks can learn intricate patterns from raw data, while symbolic reasoning can help in explaining the detected anomalies and providing contextual understanding

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

BIM viewer - ekstern oppgave

BIM viewer
Ønsker du å jobbe med teknologi som har potensiale for reell verdiskaping i industrien og samfunnet generelt? Nei vi snakker ikke GPT og LLMs denne gangen, LLMs er en AI som ikke har forståelse. Vi jobber med conceptual computing, mao vi søker modeller som har forståelse.
1 Introduksjon
I4 TECHNOLOGY er et teknologiselskap som utvikler digitale løsninger for å realisere Industri 4.0 for BAE-næringen.
Vi jobber med AI bygget på semantisk web, den 3je generasjonen av internett, som separerer apper fra databaser. For bygg- og eiendomsindustrien betyr dette mulighet for å løse to sentrale produktivitetsutfordringer:
• Strukturering og Lagring av data
• Kommunikasjon mellom mennesker, systemer, organisasjoner etc
I4T vil tilby reelle digitale tvillinger for bygninger slik at man enklere kan lage løsninger for kommunikasjon og prosessoptimalisering på toppen.
For å få til dette er det er en lang rekke funksjoner som må utvikles. I4T har identisert en del grunnfunksjonalitet/generelle fuksjoner/moduler som vil være nyttig for både vår egen applikasjonsutvikling, men også være generelt nyttig for en lang rekke applikasjoner som utvikles for internet.
I4T er på leting etter dyktige personer som kan hjelpe oss å utvikle våre løsninger videre. Studenter som beviser at de kan bidra vil vi være interessert i å diskutere ansettelse med, når vi skalerer virksomheten. Utviklet kode som vi kan bruke i våre løsninger vil bli kompensert.
2 Føringer
2.1 Teknologisk plattform
I4T bygger våre løsninger på en generell plattform for semantisk web (eks. Solid og RDF), utviklet av Graphmetrix Inc.
Vår frontend utvikles i javascript ved hjelp av rammeverkene VUE.js 2 og Threejs. Disse er mye brukt og vil være nyttig for studenter å gjøre seg familiære med.
2.2 Arbeidsmetode
Agile utvikling
• Skrive brukerhistorie. Starter alltid med noe lignende:
o Som Bruker ønsker jeg å kunne rotere 3d objektet på en behaglig måte
o Som Systemeier…
• Skrive spesifikasjon:
o Krav:
 Kunne fungere på alle enheter
 Prestere uten høy cpu load
o Beskrivelse
 Eksempel: Ved “onmousedown” skal punktet til musen settes som 3d motorens senter punkt. …
• QA på spec fra I4T
• Utvikle i henhold til spec
• QA på kode fra I4T
3 BIMViewer funksjoner
Vil du være med å videreutvikle en BIMviewer? Her skal man lage mye brukte funksjoner som:
• Å se et tversnitt av en bygning.
• Måle avstander mellom steder
• Forbedre viewer navigasjonskontroller
• Se informasjon om bygningselementer
• Forbedre lyssetting av elementer

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Bio-Inspired Artificial Intelligence for Computer Games

Computational generation of video game content, often referred to as procedural content generation (PCG), holds much promise for generating character mechanics. Character mechanics refers to how characters are allowed to move and behave in a computer game, rather than aesthetics such as graphics and audio. This projects builds on existing work in which we have studied how to generate character mechanics automatically, by means of novelty search [1,2]. Our results show that some of the auto-generated characters are, by human subjects, perceived as more interesting than built-in game characters.

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Bio-inspired techniques applied to remote sensing with hyperspectral data

Our planet is changing, now more than ever before. Understanding these changes and how they impact the environment is crucial for preserving the Earth for the coming generations.

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Bio-inspirerte metoder

Oppgavene skreddersys til studenters interest.

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Biologically Plausible Artificial Intelligence

Local learning rules are believed to be more biologically plausible than training models through backpropagation, and may thus overcome model susceptibility to, for example, catastrophic forgetting and adversarial attacks. The article https://arxiv.org/abs/2205.00920 introduces a novel learning rule that learns input patterns without supervision, however, further study is needed before the presented method can be used to solve more challenging machine learning tasks.

Faglærer: Ole Christian Eidheim     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Biometrics and Neuro-conceptualization

This thesis uses Biometric data (heart rate, EEG, eye-tracking) to understand how the brain process visual conceptual models. Conceptual models are written in specific diagrammatic languages (two dimensional visual models) such as UML and BPMN. A lot of work has been done on the understanding of how humans comprehend and use such models in information systems and software development from the point of view of IT, cognitive psychology and linguistics. On the other hand, there are limited work on how the brain processes such models. Some work is done in neuro-lingustics, but primarily looking at natural language texts. Several areas of the IT-field has also used techniques from neuro-science for a while (NeuroIS where one look e.g. on the usage of IT systems and appropriate user interfaces, and NeuroSE where one in particular look on comprehension of software code)

Faglærer: John Krogstie     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Bærekraftrapportering (sustainability reporting)

 

Faglærer: Eric Monteiro     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Can a Generative AI Learn to Navigate Ships in Accordance with Regulations?

Generative AI has shown to have great potential for many tasks previously reserved for humans. The technology is trained with a large collection of sequences and learns statistical patterns.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Causal Inference for Headaches

Headaches affect many people. Today, we have access to large data sets describing patients' symptoms, medical measurements, and personal perception. It remains unclear what causes headaches and what remedy is most promising. The master project will explore the use of Artificial Intelligence, particularly Causal Inference, to estimate the causes of headaches in patient records. Causal Inference relies on a scientific model that captures existing knowledge and plausible assumptions. The candidate will get in touch with medical experts to learn more about those. The goal is to construct a causal graph and estimate the effect of random variables on the observed outcome. The outcome can either be modelled as the probability for observing a headache or the probability that a remedy will reduce the perceived pain.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: En student     Lenke: plink

Characterization of marine detectors in a situation awareness context

This topic is addressing the problem of characterizing the performance of a detector for marine objects of interest (mostly ships) that works on different sensor modailities (video, radar, Lidar). The detector is characterized on an abstract level, and its performance is expressed by statistical quantities. The core of this project is to provide an abstract representation of such a sensor which can be used in a system simulation context. So there is only little consideration of specific algorithms for video, RADAR, or Lidar sensor data processing; it is only the overall input-output relation inside a complex system which is regarded. However, the multitude of different operation conditions (weather, visibility, ...) makes the problem challenging, as this variability should be represented in the simulated detector.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

ChatGPT as a teaching assistant

The project requires assessing the role of ChatGPT as an evaluator in providing feedback qualitatively. The student will be tasked to work with chatGPT API to prompt various inputs and analyze the output with subjective experiments. The dataset in the English language will be used. The objective is to analyze the tool's effectiveness in evaluating assignments and other tasks and to which extent it could act as a teaching assistant. The student is expected to have basic Python knowledge and can work with APIs.

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Chop my Chip! Dynamic Compilation Targets for Hardware-Software Codesign

Context: RISC-V is an open-source instruction set architecture (ISA) offering a customizable framework for designing processors. RISC-V promotes the capability to define custom ISA extensions to customize processors for specific applications or performance requirements.

Problem: Silicon is fixed in atoms, and so are compiler targets. How can compilers help a designer who is still in the process of defining the capabilities of a new architecture in terms of ISA features? What if we want to explore a set of alternative designs?

Goal: We want to make the LLVM compiler a valuable tool in the early stages of hardware design by being able to dynamically define custom compilation targets. For example, if a designer must create the best accelerator for a specific domain (e.g., autonomous driving or post-quantum cryptography), what are the most useful vector instructions to accelerate? Can they automatically recompile the same program for a wide range of variations of the same ISA and assess the impact on performance?

Requirements:
Programming Languages: C/C++ (mandatory), Python (desired)
Tools: CMake, Git
OS: *nix
Compiler toolchain: LLVM (desired)
English language: working proficiency (mandatory)

Faglærer: Stefano Cherubin     Status: Valgbart     Egnet for: En student     Lenke: plink

Cities as Urban Living Labs

 

Faglærer: Sobah Abbas Petersen     Status: Tildelt     Egnet for: En student     Lenke: plink

Classifying animals in the wild

Classifying animals in the wild in complex backgrounds is a challenging and open problem. The complexity increases with natural vegetation, varying environmental conditions, half-captured animal parts in frames, lightning variations, and so on. This project aims to classify not only the animals captured in the frame (i.e., elk, rabbit, deer) but also the background surroundings into snow, grass, trees, etc.
The project entails developing deep learning models to label foreground objects (animals) and the background into distinctive categories. Elephant Expedition (EE), Snapshot Wisconsin (SW), Snapshot Serengeti (SS), and Camera catalog (CC) to identify animal species in the wild are some of the datasets that contain millions of images that can be used for training and testing deep learning models. Norwegian wildlife dataset collected by NINA is also available with us. 

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: En student     Lenke: plink

Classroom interaction tools

The project aims to study various aspects of creating interactivity in large classrooms. Potential scenarios could include individual or group work combined with discussions, ad hoc quizzes, practical activities etc. 

Faglærer: George Adrian Stoica     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

CNNs applied to reverse engineering of unknown instruction set architectures and custom virtual machines

Reverse engineering (RE) is the process of discovering features and functionality of a hardware or software system. RE of software is applied where the original source code for a program is missing, proprietary, or otherwise unavailable. Motivation for RE ranges from extending support of legacy software to discovery of security vulnerabilities to creating open source alternatives to proprietary software.

Faglærer: Donn Alexander Morrison     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Collaborative Games for IT and Sustainability

How can we help students to learn more about the negative and positive impact of technology on sustainability? This task will focus on the development of collaborative games for increasing awareness about the role of IT in reaching the UN Sustainable Goals. Students are welcome to discuss specific areas of interest, both with respect to specific Sustainability Goals that they want to address, as well as game genre and technology.

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Combining EEG and motion capture in immersive VR environment

Electroencephalography (EEG) is an electrophysiological monitoring method to measure electrical activity in the brain. From noninvasive small electrodes that are placed along the scalp, EEG record spontaneous electrical activity in our brain. Analyzing EEG signal data helps researchers to understand the cognitive process such as human emotions, perceptions, attentions and various behavioral processes.

Faglærer: Xiaomeng Su     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Comparing gaze input methods with traditional mouse input for typical tasks

The eye-tracking technology has advanced in the last decades so much so that now there are available eye-tracking based input devices (gaze interaction). The goal of this project is to compare the performance of several combinations that involve eye-tracking input devices with traditional mouse input devices for a number of typical tasks (web browsing, email, report / essay writing, code editing).

Faglærer: George Adrian Stoica     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Compilers for Approximate Computing

Approximate computing is the science that studies how to provide ‘good enough’ results -- according to an application-specific quality metric -- while at the same time improving a performance metric such as time-to-solution, energy-to-solution, area, etc.

Faglærer: Stefano Cherubin     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Compilers for Differential Algebraic Equations

Equation-based modelling and simulations languages offer a high-level interface to allow modellers to define the behaviour of a dynamic physical system in terms of Differential Algebraic Equations (DAEs), a key element in the simulation and digital twin approach to system modelling.

Faglærer: Stefano Cherubin     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Compilers for Digital Twins & Digital Simulations

Equation-based modelling and simulations languages offer a high-level interface to allow modellers to define the behaviour of a dynamic physical system in terms of Differential Algebraic Equations (DAEs), a key element in the simulation and digital twin approach to system modelling.

Faglærer: Stefano Cherubin     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Computational Creative Artform Transitions (e.g., Text to Images, Images to Music or Music to Text)

Read also: Writing a Master's Thesis in Computational Creativity

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Computational Creativity in Art, Images or Videos

Read also: Writing a Master's Thesis in Computational Creativity

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Computational Creativity with Reinforcement Learning and/or Generative Adversarial Networks

Read also: Writing a Master's Thesis in Computational Creativity

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Computational Linguistic Creativity

Read also: Writing a Master's Thesis in Language Technology or Computational Creativity

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Computational Musical Creativity

Read also: Writing a Master's Thesis in Computational Creativity

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Computational nanosystems: Toward real word tasks

 

Faglærer: Gunnar Tufte     Status: Valgbart     Egnet for: En student     Lenke: plink

Computer Vision in Bad Weather conditions / Bad Visibility

This project is a cooperation with a leading industrial partner that is one major player in the field of autonomous driving.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Constructing an AI-based, intelligent ground vehicle, level II

This project aims at building a mid scale mobile robot using a physical robot that we already have in my group, the LIMO robot by Agilex.ai

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Context-Aware Decoding using Multi-Lingual Language Models


Background

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Context-aware language model integration in speech recognition

Key concepts: natural language processing (NLP), language models, context, CTC decoding, sequence-to-sequence models.

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Continuous software delivery utilising PaaS: opportunities and challenges

To ease the migration of software applications to the cloud, more and more organisations are adopting a Platform-as-a-Service (PaaS) delivery model. A PaaS environment enables software developers to focus on application design rather than cloud infrastructure aspects, aiming to foster more frequent software deliveries. PaaS solutions can either be provided by an external provider (e.g., Red Hat OpenShift or AWS Lambda), or developed and managed in-house. For example, organisations like NAV (nais.io), Equinor (radix.equinor.com), Finn.no (opensource.finntech.no) maintains and offers their own platform as open source to the public.

Faglærer: Joakim Henrik M Klemets     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Conversational Agents for the Norwegian Banking Sector

Today, we frequently encounter conversational agents/chatbots/virtual assistants when engaging with customer services. These AI system facilitate information access. They provide a way to approach our information needs in the form of a conversation, as we would encounter with human-to-human interaction. Conversational agents can be available 24/7 and help the employees to focus on more difficult requests. The very unique language of bankingi challenges conversational agents. Organizations use special nomenclature and sometimes their own terms or brands.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Cooperative automatic driving in a simulated urban driving scenario

This project aims at using CARLA, the industry-standard simulation environment for autonomous driving with a strong emphasis on physically realistic car dynamics, realistic road networks allowing for massive multi-agent simulations.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Data science - in practice

Data-driven data science is attracting a lot of interest.

Faglærer: Eric Monteiro     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Decoding Natural Sounds with AI

This is a very open-ended project inspired by the book, “The Sounds of Life” (Bakker, 2022), which covers many different animals, plants and ecosystems, from bats to whales to trees to coral reefs.  In each case, the author mentions that AI (normally deep learning) has been used to interpret sounds, but no details are given.  The book provides a massive set of references for eager researchers who want to follow up any of the book's 10 examples.

Faglærer: Keith Downing     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Deep learning enabled IoT Devices for Intelligent decisions in Multimedia Surveillance Networks

Wireless multimedia surveillance networks produce large amounts of visual data. Lack of intelligent mechanisms for storage, indexing, and management makes these tasks comparatively, time consuming for analysts to browse and gather actionable intelligence. Taking advantage of the powerful feature of fog computing over economical hardware (i.e., Jetson Nano), the purpose of this work is to propose a mechanism to process surveillance data (i.e., multimedia contents and contextual information under uncertain environments) at visual processing hub and only transmit relevant and useful data to Base-Station securely. In result, the proposed method minimizes energy and bandwidth consumption and ensure effective surveillance while extending its potential toward the ongoing projects related to surveillance in Norway.

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: En student     Lenke: plink

Define your own project in Visual Computing

This is open to students who have an interest and an idea for a project in Visual Computing.

Faglærer: Theoharis Theoharis     Status: Valgbart     Egnet for: En student     Lenke: plink

Define your own topic in software and system reliability

If you are interested in software and system reliability/safety/security we can agree on a topic of your interest, as long as it is adequate for a Master's project. Topics may range from modeling of reliability/safety/security properties at architecture level, application of formal methods (for example model checking), fault injection, testing, etc. Just drop me a mail at leonardo.montecchi@ntnu.no to start the discussion.

Faglærer: Leonardo Montecchi     Status: Valgbart     Egnet for: En student     Lenke: plink

Demonstrasjon av utvidet virkelighet med billijard

Prosjektet går ut på å lage en demonstrant av bruk av XR-teknologi, for å gi interessenter en "hands on"-opplevelse av teknologien. Målet er å demonstrere hva XR-teknologi er og hva det ikke er. Et tentativt mål er å implementere et billijard-spill ved hjelp av AR, der man prosjiserer et billijardbord på en bordflate. Derfra er det mulig å interagere med brettet og spille spillet.

Faglærer: Dag Svanæs     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Demonstrasjon av utvidet virkelighet med IoT

Dette prosjektet går ut på å utforske hvordan IoT kan brukes i sammenkobling med XR. Det tentative målet her er å lage en responsiv kø til billijard-spillet, som blir tracket i Unity. Køen kan interagere med det prosjiserte billijard-spillet, og helst gi feedback i form av vibrasjon når den treffer en kule.

Faglærer: Dag Svanæs     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Dense Monocular Depth Estimation for Unmanned Surface Vessels (in cooperation with Maritime Robotics)

Main goal:

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Design and Evaluation: A digital tool to empower children to self-report their emotions and learning

Supervisors: Sofia Papavlasopoulou, Feiran Zhang, Boban Vesin

Faglærer: Sofia Papavlasopoulou     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Design Thinking learning with Emerging Technologies

Supervisors: Sofia Papavlasopoulou, Boban Vesin and Kshitij Sharma
Place: LCI Lab: https://lci.idi.ntnu.no/, Trondheim
Suitable for: One or Two students

Faglærer: Sofia Papavlasopoulou     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Designing a next generation user identities platform using web 3.0 technologies

In today's digital age, it's crucial to manage important documents like diplomas and licenses securely and efficiently. Traditional methods of handling these documents are either outdated i.e. paper based or fragmented and can pose privacy and security risks. However, with new web 3.0 technology like self-sovereign identity and digital wallets, there's an opportunity to improve how we manage identity documents. This proposal aims to introduce a digital wallet platform that can securely store various identity documents such as academic diplomas, driving licenses, boat licenses, flying licenses, shooting licenses, and so on. By using advanced technology, this platform will make it easier for users to access and manage their documents while ensuring their privacy and security. 

Faglærer: Surya Bahadur Kathyat     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Designing design systems for remote operations

A design system is a collection of documented user interface (UI) elements, visual guidelines, and design principles that other people can use or refer to when designing digital products. Notable examples of design systems are Google’s Material Design and Apple’s Human Interface Guidelines. The main benefits of a design system are (1) improving design consistency across different digital products since it serves as a single source of reference that other people can refer to and (2) reduce the development work since UI designers do not need to design everything from scratch. 

Faglærer: Yngve Dahl     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Designing eco-feedback interfaces for ships

The urgent need to reduce carbon emissions from maritime activities has highlighted the importance of innovative strategies in interaction design, particularly eco-feedback, which has been effective in nudging car users towards more efficient practices. Despite the acknowledged potential for significant emission reductions on ships through behavioral changes in their operation, there are currently no established standards for eco-feedback within the maritime sector.

Faglærer: Yngve Dahl     Status: Valgbart     Egnet for: En student     Lenke: plink

Designing user interfaces for drone swarm interaction

Recent advancements in technology have made it feasible to deploy multiple flying drones that can be controlled in swarms by a single operator. This innovative approach to drone management opens up new possibilities for applications ranging from surveillance to emergency response. In collaboration with scientists from the University of Oslo (UiO) who specialize in drone swarm deployment, this project aims to tackle the unique challenges associated with controlling drone swarms.

Faglærer: Yngve Dahl     Status: Valgbart     Egnet for: En student     Lenke: plink

Designing user interfaces for sail propulsion

Sails are increasingly viewed as the most significant enhancement to the current international fleet of ships, offering a promising avenue for sustainable energy with minimal infrastructure requirements. With a rising number of vessels under development featuring sail support, and existing vessels being retrofitted with sails, the interest in harnessing wind energy is evident. However, despite substantial investments in this area, the maritime industry lacks established user interface design patterns to operate this new sail-supported vessel category.

Faglærer: Yngve Dahl     Status: Valgbart     Egnet for: En student     Lenke: plink

Detecting and Predicting Antibiotic Usage

Infections and antimicrobial resistance are the leading causes of death worldwide. The most common death pathway for infectious diseases is sepsis. To improve patient outcome, correct antibiotics must be administered within one-hour of a suspected sepsis case. However, sepsis has similar signs and symptoms like other diseases which makes it easy to be misdiagnosed and mistreated. This leads to treatment delays and increases antibiotic resistance and mortality rate for patients. 

Faglærer: Melissa Yan     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Detecting and Predicting Healthcare-associated Infections and Sepsis

Sepsis is a life-threatening syndrome that contributes to 30-50% of hospitalized deaths. It occurs when the body has an extreme response to an infection that can lead to multi-organ failure and death. When an infection is acquired at a hospital, this is considered a healthcare-associated infection (HAI). Existing machine learning studies that detect or predict HAI and sepsis mainly focus on short horizons when patients are already in a critical state at the intensive care unit or emergency department. In contrast, this project aims to detect and predict HAI and sepsis development within different hospital departments for longer horizons using machine learning to prevent sepsis development.

Faglærer: Melissa Yan     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Detecting cracks in ship thanks from drone images

This is a topic originating from a cooperation between DNV GL and NTNU, addressing the task of inspecting ship tanks for cracks and similar faults, using a drone equipped with a camera.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Detecting Social Media Risk Users

Read also: Writing a Master's Thesis in Language Technology

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Detection of security vulnerabilities in source code using machine learning

Writing secure software is a challenging task and with the landscape of known security vulnerabilities changing almost daily, this task is practically unending. There has been some effort in recent years to use machine learning to automatically scan software repositories during the development lifecycle [1, 2, 3].

Faglærer: Donn Alexander Morrison     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Develop an App for improving job ads and hiring processes

The aim of this research project is twofold: First, the project tries to analyze a large number of job posts in the area of “people analytics” and identify the skills that are mentioned in the job posts. We seek to identify both hard and soft skills. Secondly, the project aims to develop an algorithm that automatically identifies the skills within any job post in the area of people analytics. For this project, databases that hold large numbers of relevant job posts will be used, such as Indeed. A long list of skills from Coursera for the fields of management and humanities that could be used for the identification of skills in the job posts will be provided if needed.

Faglærer: Ilias Pappas     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Developing support for the Regionalized Value State Dependence Graph (RVSDG) in MLIR

Conventionally, compilers for imperative languages represent code in static single assignment form organized in basic blocks. The Regionalized Value State Dependence Graph (RVSDG, https://doi.org/10.1145/3391902) is a compiler intermediate representation (IR) developed at NTNU that represents control- and dataflow in one unified representation. The RVSDG is a data-flow centric IR where nodes represent computations, edges represent computational dependencies, and regions capture the hierarchical structure of programs. It represents programs in demand dependence form, implicitly supports structured control flow, and models entire programs within a single IR.

Faglærer: Magnus Själander     Status: Valgbart     Egnet for: En student     Lenke: plink

Development and maintenance of IT-systems in Norwegian Organization

In 1993, 1998, 2003, 2008, 2013 and 2018  surveys were performed to investigate development and maintenance of IT-systems in Norwegian organizations. Comparable data for important areas where also capture in collaboration with Rambøll IT I praksis investigations. Data collection for a similar survey is carried out yearly since  2015. The assignment will be to analyze the quantitative and qualitative data from recent investigations. Together with a literature review, the survey investigations are expected to give us new knowledge about mechanisms affecting resource utilization related to information systems support in organizations. The report should be written in English and is expected to form the basis for scientific publications

Faglærer: John Krogstie     Status: Tildelt     Egnet for: En student     Lenke: plink

Development of ´Learn and Earn/Learn to Care’ like gaming platform using mix of Web3 and traditional gaming Technologies

Web3 technologies open new and interesting possibilities in games development! In this project a gaming platform will be developed that allows multiple players to play, learn and/or earn points or digital assets.

Faglærer: Surya Bahadur Kathyat     Status: Valgbart     Egnet for: En student     Lenke: plink

Development of functionality for requesting and assigning substitute teachers in the Visma Inschool Mobile App

Goals:
Our primary goal is to improve the efficiency and user-friendliness of our mobile app by developing and implementing a feature that allows teachers and principals to request and assign substitute teachers directly from their mobile devices.

Faglærer: Kshitij Sharma     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Digital Forensics

Read also: Writing a Master's Thesis in Language Technology

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Digital registrering av innemiljø og tilstedværelse på Campus

I sammenheng med Campus-prosjektet instrumenteres ulike rom med sensorerer for inneklima og tilstedeværelse. Oppgaven går ut på å utforske data fra denne type sensorer i forhold til å finne gode anvendelser av disse.

Faglærer: John Krogstie     Status: Tildelt     Egnet for: En student     Lenke: plink

Digital Twins (DTs) for Health and Infrastructure++ (2024)

Instersted in Digital Twins (DTs), for self-management of health and decision support or road infrastructure?

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Digitalisering i olje og gass: IoT

Stadig mer av oljeutvinningen på norsk sokkel foregår på svært utilgjengelige felt på store havdyp. Samtidig benyttes stadig oftere ubemannete subsea anlegg som ligger på havbunnen men fjernestyres fra land eller nærliggende rigg. Sensorbasert informasjon (bl.a. temperatur, trykk, seismikk, magnetiske og strålingsegenskaper til bergformasjonen) spiller stadig større rolle i alle fasene: leting ("exploration"), boring ("drilling"), logging og produksjon.

Faglærer: Eric Monteiro     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Digitalization in the electric power markets

Electric power is a particular type of commodity: it cannot (easily) be stored, needs to be transported over (large) distances for consumption, it is tightly regulated both nationally and within the EU

Faglærer: Eric Monteiro     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Discovery of ISA features from binary programs from unknown instruction set architectures

Reverse engineering (RE) is the process of discovering features and functionality of a hardware or software system. RE of software is applied where the original source code for a program is missing, proprietary, or otherwise unavailable. Motivation for RE ranges from extending support of legacy software to discovery of security vulnerabilities to creating open source alternatives to proprietary software.

Faglærer: Donn Alexander Morrison     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Distributed ANN algorithms with higher recall.

The Approximate Nearest Neighbor (ANN) algorithm is a popular algorithm used in machine learning and data mining. It is used to find the nearest neighbor of a given point in a high-dimensional space. However, as the size of the dataset grows, the performance of the ANN algorithm degrades. This is because the ANN algorithm requires a lot of memory and computation power to work efficiently.

To address this issue, researchers have proposed distributed versions of the ANN algorithm that can scale beyond the 1 billion vector mark. However, these distributed versions are not efficient enough to handle large datasets. Therefore, there is a need to build a distributed version of an ANN algorithm that can offer efficient scaling beyond the 1 billion vector mark.

Your thesis will explore how to build such a distributed version of an ANN algorithm that can offer efficient scaling beyond the 1 billion vector mark. You will investigate different approaches to building such an algorithm and evaluate their performance on large datasets. You will also explore how to optimize the performance of the algorithm and how to make it more efficient by using different strategies for cross machine partitioning and communication.

Faglærer: Knut Magne Risvik     Status: Valgbart     Egnet for: En student     Lenke: plink

Dronebasert sanking av sau

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Educating IT professionals who make a difference

The great challenges of the future require IT professionals who can do things differently and question the status quo. This project will look into how IT education can prepare candidates not only to perform successfully within the current practices of academia and work life, but who have the ability to take different perspectives and make a real change. The project should explore how the educational use/adaptation of relevant methodologies (for instance in software development/project work) and/or technologies (e.g. for learning and collaboration, including AI-based tools) might help students become not only competent experts in IT but also reflective, motivated and corageous agents for change.

Faglærer: Birgit Rognebakke Krogstie     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Education in the era of generative AI: Understanding the potential benefits of ChatGPT in teaching and learning

Since its maiden release into the public domain in November 2022, ChatGPT garnered more than one million subscribers within a week. The generative AI tool ⎼ChatGPT took the world by surprise with its sophisticated capacity to carry out remarkably complex tasks. The extraordinary abilities of ChatGPT to perform complex tasks within the field of education have caused mixed feelings among educators as this advancement in AI seems to revolutionize existing educational praxis. This topic will investigate the opportunities and challenges of generative AI (through the implementation of a case study) and offer recommendations on how generative AI could be leveraged to maximize teaching and learning. The goal is to identify how these evolving generative AI tools could be used safely and constructively to improve education and support students’ learning.

Faglærer: Michail Giannakos     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

EduWallet: A Blockchain-Enabled Digital Wallet for Managing University Course Credits

Overview:

The project aims to revolutionise university credit management by integrating blockchain with existing educational systems using Blackboard and Inspera APIs. EduWallet ensures secure, efficient record-keeping and easy credit transferability across institutions.

Faglærer: Surya Bahadur Kathyat     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Effects of Interpreted Languages on Microarchitectural Performance

With the rise of functions as a service (FaaS) due to the broader availability of cloud computing workloads, we face an evergrowing body of software written in so-called high-productivity languages, such as Python or Javascript. While we have a somewhat detailed understanding of how compiled software performs on modern systems, these interpreted programming languages pose unprecedented challenges. All these languages require a runtime consisting of compiled code which interprets the actual logic and translates it on the fly into an intermediary representation, similar to the bytecode representation of C# or Java.

Faglærer: Roman Kaspar Brunner     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Efficient Approximate Nearest Neighbour algorithms for immutable data

This thesis aims to explore the use of DiskANN for batch processing and searching over immutable data. The first step will be to build a good Python interface for DiskANN that can be used to experiment with the algorithm. The thesis will then investigate how to optimize the performance of DiskANN for batch processing and searching over immutable data. This will involve exploring different strategies for cross-machine partitioning and communication.
The thesis will also evaluate the performance of DiskANN on large datasets and compare it with other state-of-the-art approximate nearest neighbor algorithms. The results of this evaluation will be used to identify areas where DiskANN can be improved and to suggest directions for future research.

This project will require extensive programming, and students should have solid experience in C++ and Python to take this on. Furthermore, experience and good capabilities in performance evaluation and understanding of detailed performance characteristics of computers will be required.

Faglærer: Knut Magne Risvik     Status: Valgbart     Egnet for: En student     Lenke: plink

Egomotion estimation and map building for the Revolve Autonomous Race Car

This project is related to the Revolve student project aiming at building / improving an Autonomous Race Car that can participate in the international Formula Student competition. Revolve is participating to this yearly competition already for a number of years. Each year, a new version of this race car is developed, and of course it is a fundamental goal of the overall Revolve project to achieve a good rank in the annual competition.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Embedding Lean Startup Guideline to improve startup assistant chatbots

With recent generative AI technologies, chatbots are epected to develop to a next level. In a startup ecosystem, they offer a cost-effective solution for entrepreneurs to get their questions answered, validate their ideas, and receive feedback. Rule-based chatbots are often limited in their flexibility and scalability. Generative AI are capable of understanding and responding to natural language, however, they need to be developed in a specific domain context.

Faglærer: Anh Nguyen Duc     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Emotions and well-being in Computing education

Emotion are essential in work and learning. For instance, positive emotions can lead to more creativity, and emotions can influence memory and decision
making. In technology enhanced learning, recent years have seen a strong interest in emotions during learning. 

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Employee-driven innovation within organizations

Supervisors: Leif Erik Opland, Ilias Pappas

Faglærer: Ilias Pappas     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Empowering Children’s STEM Learning through Embodied Interaction and GenAI capabilities

Supervisors: Michail Giannakos, Giulia Cosentino

Faglærer: Michail Giannakos     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Enterprise Architectures for learning and knowledge transfer in sustainable cities

Cities are evolving rapidly due to technological advances such as digital ecosystems, sensor technologies and vast amounts of data. At the same time, the need for evolving in a sustainable manner and ensuring innovative and sustainable solutions are of utmost importance. Cities stand to gain from learning from other cities. Enterprise Architecture has been considered as a means of capturing an ICT ecosystem in a city so that it can be replicated in other cities. This project will also focus on if and how ideas from enterprise architecture could be applied to support the transfer of knowledge and learning across cities. The tasks will include the following:
- Literature review
- Enhance enterprise architecture model to support learning and knowledge transfer
- Prototype model 
- Validation of the model.
The courses in information modelling and Enterprise Architecture and Innovation (TDT4252) and the specialisation module TD20 (Smart and Sustainable cities and Enterprise Architecture) will be relevant for this project.

Faglærer: Sobah Abbas Petersen     Status: Tildelt     Egnet for: En student     Lenke: plink

Ethical aspects of AI/recommender systems

Smart and personalized systems such as recommender systems (or artificial intelligence in general) keep influencing our daily lives in an increasing rate. In the recent years, researchers became more aware of the ethical challenges in developing such systems in an ethical way such that these systems would treat everyone equal and fair, without any bias or discrimination. Even though these are the topics social scientists have been working on for a long time, defining these concepts as mathematical models, implementing them within AI systems and evaluating the success of these approaches is not an easy task.

Faglærer: Özlem Özgöbek     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Evolutionary Algorithms with Applications

Background

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Evolving personalised training programs to reduce/relieve Metabolic Syndrome

Metabolisk syndrom er et enormt helseproblem som påvirker stadig fler i den vestlige verden. De
siste tiårene har gjort oss mer stillesittende og gitt oss enklere tilgang på ultraprosessert mat samtidig
som jobbene våre har blitt mindre aktive. Resultatet er at stadig fler av oss havner under kategorien
metabolsk syndrom. Dette øker risikoen for å utvikle hjerte og kar sykdrommer samt diabetes type 2.

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Exciting topics in HPC & Parallel Computing

Prof. Elster is on research leave for the 2023/24 academic year, but will again accept master student projects for Fall 2024.

Faglærer: Anne C. Elster     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Explainable AI for testing inner misalignment in reinforcement learning models playing atari games

In this project, the student will apply explainable AI (XAI) techniques to investigate what reinforcement learning (RL) agents trained to play atari games have learned and focus on. This project is suitable for a student with experience using RL and with some preliminary knowledge in implementing XAI methods, and requires the ability to code and test own ideas. The is a challenging project with a research focus, meaning that the right solutions to the problem do not exist and must be developed by the student.

Faglærer: Inga Strümke     Status: Valgbart     Egnet for: En student     Lenke: plink

Explaining a deep reinforcement learning agent for comparison with grid cells in biological brains

In this open-ended project, the student should train a reinforcement learning agent in an evnironment, and use explanation methods to investigate what the agent has learned about navigation and the environment itself. The student will investigate whether these explanations can be compared to findings on what mice learn when navigating a corresponding environment in neuroscientific experiments.

Faglærer: Inga Strümke     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Explaining AI-based Recommendations (with Sparebank 1 SMN)

AI-technology has revolutionized many business processes. In banking, organizations increasingly rely on complicated models such as deep neural nets. These models suggest which products or services to advertise to customers.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Exploring nanomagnet properties for computation

Introduction

Faglærer: Johannes Høydahl Jensen     Status: Valgbart     Egnet for: En student     Lenke: plink

Exploring neuroevolution - DES-HyperNEAT

Neuroevolution has its advantages and disadvantages compared to different Deep learning approaches, However, much is still to be investigated so as to fully exploit the power of evolution as a tool to design more optimal topologies and sutiable weights for more compelex applications.With today's focus on sustainability, simpler networks for the same application are the key to reduced computation expense.  

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Exploring the black box of AI Governance

AI governance is a notion that is often attributed to a range of different practices and processes. From establishing a process of developing AI applications, ensuring that quality outcomes are achieved, and to decising the role and responsibilities of stakeholders. AI governance now plays an important part related to the business value that AI can deliver, and to ensuring that projects comply with ethical and regulatory frameworks. This project will seek to understand how organizations develop AI governance practices, what aspects they take intio accoutn when doing so, how they deploy them, and what the outcomes of them are at the business and project performance levels.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

FACIAL LANDMARK DETECTION

Detecting facial landmarks in 2D facial images is a prerequisite for expression transfer, among other applications. This project will survey existing approaches for 2D facial landmark detection and implement a selected technique. Applications will be considered, such as expression transfer.

Faglærer: Theoharis Theoharis     Status: Valgbart     Egnet for: En student     Lenke: plink

Fake News (Disinformation) Detection

The impact of news articles on the society can not be underestimated and as the number of online news are increasing, distinguishing the fake news from real news is becoming a challenge for people. This project focuses on analyzing and/or tracking news articles from different news sources or social media channels, in order to find an efficient way of detecting fake news.

Faglærer: Özlem Özgöbek     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Federated Learning for Privacy-Preserving Medical Image Analysis: A Collaborative Approach

In this project, the aim is to address the challenges of analyzing medical images while maintaining patient privacy. In this approach, instead of centralizing all medical data in a single location, the project employs federated learning, a decentralized machine learning technique.
In a federated learning setup, multiple medical institutions or entities collaborate to train a shared machine learning model without sharing raw data. Each institution retains control over its data, and only model updates are exchanged and aggregated among the participants. This approach ensures that sensitive patient information remains localized, minimizing the risk of data breaches and privacy violations.
The project's focus on medical image analysis indicates that it's aimed at tasks like diagnosing diseases, detecting anomalies, or segmenting regions of interest within medical images. By using a collaborative federated learning approach, the project combines the benefits of data sharing for model improvement while upholding strict privacy standards required in the medical field.

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Fighting Bias in Large Language Models

Read also: Writing a Master's Thesis in Language Technology

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

FireWatch: Advanced Techniques for Wildfire Detection and Early Warning Systems

Wildfires pose a significant threat to ecosystems, communities, and infrastructure. This project focuses on the development and enhancement of wildfire detection and early warning systems using AI and computer vision. By leveraging a combination of remote sensing data, sensor networks, machine learning, and artificial intelligence, the study aims to create a robust and proactive solution for early wildfire detection.
The research delves into the challenges of accurately identifying wildfire signatures from various data sources, optimizing real-time data processing, and designing efficient alert mechanisms to enable timely response and mitigation efforts. The outcomes of this research contribute to minimizing the impact of wildfires on lives and the environment through advanced fire detection strategies.

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

First person model - ekstern oppgave

Førsteperson (First person model)
Ønsker du å jobbe med teknologi som har potensiale for reell verdiskaping i industrien og samfunnet generelt? Nei vi snakker ikke GPT og LLMs denne gangen, LLMs er en AI som ikke har forståelse. Vi jobber med conceptual computing, mao vi søker modeller som har forståelse.
1 Introduksjon
I4 TECHNOLOGY er et teknologiselskap som utvikler digitale løsninger for å realisere Industri 4.0 for BAE-næringen.
Vi jobber med AI bygget på semantisk web, den 3je generasjonen av internett, som separerer apper fra databaser. For bygg- og eiendomsindustrien betyr dette mulighet for å løse to sentrale produktivitetsutfordringer:
• Strukturering og Lagring av data
• Kommunikasjon mellom mennesker, systemer, organisasjoner etc
I4T vil tilby reelle digitale tvillinger for bygninger slik at man enklere kan lage løsninger for kommunikasjon og prosessoptimalisering på toppen.
For å få til dette er det er en lang rekke funksjoner som må utvikles. I4T har identisert en del grunnfunksjonalitet/generelle fuksjoner/moduler som vil være nyttig for både vår egen applikasjonsutvikling, men også være generelt nyttig for en lang rekke applikasjoner som utvikles for internet.
I4T er på leting etter dyktige personer som kan hjelpe oss å utvikle våre løsninger videre. Studenter som beviser at de kan bidra vil vi være interessert i å diskutere ansettelse med, når vi skalerer virksomheten. Utviklet kode som vi kan bruke i våre løsninger vil bli kompensert.
2 Føringer
2.1 Teknologisk plattform
I4T bygger våre løsninger på en generell plattform for semantisk web (eks. Solid og RDF), utviklet av Graphmetrix Inc.
Vår frontend utvikles i javascript ved hjelp av rammeverkene VUE.js 2 og Threejs. Disse er mye brukt og vil være nyttig for studenter å gjøre seg familiære med.
2.2 Arbeidsmetode
Agile utvikling
• Skrive brukerhistorie. Starter alltid med noe lignende:
o Som Bruker ønsker jeg å kunne rotere 3d objektet på en behaglig måte
o Som Systemeier…
• Skrive spesifikasjon:
o Krav:
 Kunne fungere på alle enheter
 Prestere uten høy cpu load
o Beskrivelse
 Eksempel: Ved “onmousedown” skal punktet til musen settes som 3d motorens senter punkt. …
• QA på spec fra I4T
• Utvikle i henhold til spec
• QA på kode fra I4T
3 Førsteperson three.js
3d motor oppgave
Videreutvikle en spillmotor til bruk av såkalt førsteperson (first person model). Den skal kunne bevege seg rundt som i et spill.
Her må man lære seg å bruke biblioteker som kalkulerer tyngdekraft og kollisjoner mellom objekter.
Dette inkluderer også å rigge en 3d bygning slik at man kan bevege seg i flere plan, trapper. Man skal kunne fritt plassere personen i 3D-bygningen

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Flexible data visualization in a VR environment to aid the assessment of visuospatial neglect using eye tracking

Visuospatial neglect are commonly experienced neuropsychological
conditions affecting the contralesional side in post-stroke patients, leaving
patients with egocentric or allocentric perceptual problems. Diagnostic tools for
visual neglect include the apples test, balloons test, and bells cancellation test.
All administered on paper. While psychometrically sound, these tests are
administered in an overtly clinical setting, lacking depth as a test parameter,
only allowing for crude temporal data collection and gaze observation, and
also being limited in spatial scope to the size of the paper. By having a limited
set of test parameters, the status quo represents a barrier to advancing our
understanding of the mechanisms behind visouspatial neglect and its effect in
everyday settings of the patients. To mitigate these limitations, a VR
environment is being developed for assessment of neglect by utilizing low-cost,
off-the-shelf, VR headsets with integrated eye trackers, along with a custom-
developed and highly flexible virtual environment, where the test parameters
can be altered based on the needs of the clinician.

This master project aims to bring flexible data visualization into the
aforementioned VR environment. For starters, the students will look at using
Python to generate graphs/plots of gaze data which has previously been
collected from the VR environment, and then to display these plots directly in
the VR environment itself (i.e. the gaze plot of the person during the test).
Next, one can classify the fine-grained gaze data into more meaningful unit,
such as fixation or saccade, and come up with more relevant statical
information to the clinician. Eventually the information should be visualized in
an easy to comprehend manner to the clinician.

This work collaborates with Department of Acquired Brain Injury, St. Olav's
Hospital.
 

Faglærer: Alexander Holt     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

From semantic to Panoptic segmentation for point cloud data analysis

The project aims to advance the understanding and interpretation of three-dimensional (3D) point cloud data through segmentation techniques. Point cloud data represents objects or scenes in 3D space using individual points, often collected from technologies like LiDAR sensors or depth cameras.
Semantic segmentation in this context refers to the task of labeling each point in a point cloud with a specific class, such as "car," "tree," or "building." This enables the identification and categorization of objects within the 3D environment. Panoptic segmentation, on the other hand, extends this concept by not only labeling object instances but also recognizing 'things' (e.g., individual objects) and 'stuff' (e.g., surfaces or background).
The project aims to develop a comprehensive approach that bridges the gap between semantic and panoptic segmentation for point cloud data. This involves creating models and algorithms capable of not only recognizing object categories within the point cloud but also differentiating between individual instances of those objects and understanding the overall context in which they exist.
The potential applications of this project are significant. It could contribute to advancements in autonomous driving by enhancing the ability of self-driving vehicles to perceive and react to their surroundings accurately. Additionally, it could be useful in various fields where 3D data analysis is critical, such as robotics, augmented reality, urban planning, and environmental monitoring.

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Further development of a novel immune-inspired hybrid classification algorithm

The immune system is arguably one of nature's most highly adaptive, distributed and self-organising systems. It has the property of being able to recognise anomalies --- something that deviates from the common rule.  In 2018-2019 two masters students proposed a novel hybrid classification algorithm MAIM, combining such features of the Immune Systems with an Island Model Genetic Algorithm (IGA). The preliminary results achieved are promising and resultet in an international publication. The goal of this project is to build on this foundational work  and investigate the many avenues available to further extend/refine this novel algorithm. 

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Game for reflection on ethical aspects of IT tools

As a result of increasing digitalization, IT tools are used in multiple contexts by users with multiple backgrounds, and are intertwined in complex ways with everyday practices. The complexity of the context of use arises a number of ethical issues for software developers and users, including e.g., algorithmic biases, privacy of personal data, addictive design, …

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Gender and Diversity in Computer Science: Challenges and solutions (EUGAIN)

Supervisors:  Letizia Jaccheri and Claudia Maria Cutrupi 

Faglærer: Maria Letizia Jaccheri     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Gender-Inclusive Computer Science Education: Leadership Academy (STEM UP)

co-supervisors Anna Szlavi and Letizia Jaccheri 

Faglærer: Maria Letizia Jaccheri     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Generating simulations that match data ​

SINTEF Digital, Cognite, Kongsberg, NorwAI: In hybrid AI the aim is to combine machine learning and physical knowledge. This is particularly relevant for industrial applications of AI and machine learning on physical systems. Through the SFI NorwAI we work on hybrid AI together with partners in the energy sector. We have two use cases focused on predictive maintenance of wind turbines and on virtual flow meters in oil and gas.

Faglærer: Jon Espen Ingvaldsen     Status: Valgbart     Egnet for: En student     Lenke: plink

GENERATING SURFACES FROM FIELDS

With the advent of neural networks, signed distance fields (SDFs) and ray fields are gaining traction as object representations. But since rendering hardware is based on surface representations, it is often necessary to extract such a representation from the above fields. The classical (but costly) marching cubes approach is often used for SDFs. This project will investigate more clever ways of creating a surface from an SDF and possibly its gradient, as well as from Ray intersection fields, such as the MARF (Medial Atom ray Field). The investigation will start with genus 0 closed objects and SDFs. 

Faglærer: Theoharis Theoharis     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Generating Synthetic Clinical Adverse Event Reports

Publicly available clinical data helps promote data-driven research that can improve patient well-being. An important aspect of improving patient well-being is preventing adverse events. Adverse events are incidents that can bring harm to a patient.  The most common adverse events include hospital-acquired infections, medication errors, surgical errors, and falls. Although there are available datasets for adverse drug reactions and medical device failures, there is a lack of clinical-related adverse event datasets. This makes it difficult to develop machine learning models that can help improve patient safety and reduce future errors.

Faglærer: Melissa Yan     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

​Generating synthetic data​ (Sparebank1 SMN)

Generating synthetic data. Banking data can contain sensitive information that limits the possibility of sharing the data openly. The data is typically restrained to being analyzed on our internal hardware. This limits the ability to use large compute clusters and cloud services. It also sets a high barrier of entry for using the data in research than what an open dataset would. If synthetic data could be created based on real data, which retains the inference and statistical relationships without containing personal data, this could make it both easier to share data and to use compute resources outside of our servers.

Faglærer: Jon Espen Ingvaldsen     Status: Valgbart     Egnet for: En student     Lenke: plink

Generativ kunstig intelligens i programmering

Generativ kunstig intelligens gir oss muligheten til å være mer produktive i programmering og prosjektarbeid ved at vi kan få automatisk generert mye kode eller forslag til kode mens vi skrive. I kontekst av læring gir teknologien både utfordringer og muligheter og i denne oppgaven kan du utforske eller prøve ut forskjellige løsninger for bruk av generativ AI relatert til programmering og programvareutvikling. Tema som kan være relevante er:

Faglærer: Trond Aalberg     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Generative AI (GAI) for software architecture design flaw identification and fixing

Software architecture is critical for ensuring software quality. Design debts and flaws can result in low-performance and vulnerable systems. The existing GAI models have challenges supporting software architecture design activities because the designs are often documented as graphic models, and the traceability between requirements, design, and codes may not be documented. This project aims to summarise and address the challenges of applying GAI to detect software design debts and flaws and propose solutions for using GAI to fix software design issues responsibly and efficiently.  

Faglærer: Jingyue Li     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Generative AI (GAI) for sustainable software development

The software codes and artefacts must be sustainable for business- and mission-critical systems (e.g., IT systems in banks) because many people will use them for a long time. Those systems must be maintainable, scalable, and efficiently deployed, operated, and monitored. Although existing GAI models can generate code and artefacts using the target systems’ code as a part of their inputs, they may not understand the target systems' sustainability principle and optimisations, which are usually implicit. This project aims to identify the challenges of applying GAI to achieve software sustainability and propose approaches to incorporate the target system's sustainability requirements, principles, and optimisations as more explicit knowledge into GAI models. 

Faglærer: Jingyue Li     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Generative AI and diffusion models

Diffusion models (see e.g. these lecture notes) have gained quite some interest lately, with impressive results in particular in the image domain (now also doing high quality video from text), but also used for a lot of other things including reinforcement learning and generation of both time series data and graph data. Diffusion models have also inspired more fundamental research into alternative formulations of generative AI, including Bayesian flow networks

Faglærer: Helge Langseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Generative AI for Customer Relationship Management (with Sparebank 1 SMN)

A good relationship with their customers is essential for financial institutions. This involves reacting timely and adequately to customer messages. AI technology has the potential to improve customer services and become a competitive advantage for banks.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Generative AI for Meta Data Inference (with DNB)

Naturally, organizations collect large document archives. These feature a variety of documents, some of which lack important metadata. Examples include document type, and references to products, services, or customers.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: En student     Lenke: plink

Git dashboard

I programmerings og prosjektfag hvor studenter gjør utviklingsarbeid benyttes git for samarbeid om kode og dokumentasjon. I en utdanningskontekst ønsker fagstaben ofte overordnet innsyn i prosjektene og dette kan løses med en form for dashboard hvor en faglærer på en ryddig og informativ måte kan få oversikt over og sammenligne status og informasjon om pågående prosjekter. Noe data kan hentes fra git via API, mens andre data kan genereres ved statisk eller dynamisk analyse. Oppgaven bygger videre på andre prosjekter utført tidligere 

Faglærer: Trond Aalberg     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Gjenfinning av sau ved hjelp av drone

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Health Information systems in developing countries

Developing countries have limited resources for healthcare delivery hence need to make the most of resources available.

Faglærer: Eric Monteiro     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Human Pose Estimation for VR-exergaming


After a brain injury, patients often undergo a long and intensive rehabilitation process to regain functionality. To improve the rehabilitation process, increase its accessibility, and alleviate the high demand for clinical supervision, exergaming for rehabilitation is becoming more and more popular. Gamifying rehabilitation exercises allow for motivating training, independent from external supervision. Recently, immersive technologies such as extended reality (XR) and virtual reality (VR) are gaining traction.

Faglærer: Xiaomeng Su     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Human-Machine Symbiosis: How does decision-making change in the age of Big Data Analytics?

The nature of decision-making is changing drastically, both in personal lives and in the business sphere. An increasing amount of decisions are now based on insight that is generated through analytics. Despite this, often individuals are faced with cognitive-overload, conflicted views, or biases that result in non-adoption of insight. This project will be done in collaboration with the Big Data Observatory (https://www.observatory.no) and involve designing a study protocol and collecting and analyzing neurophysiological data (eye-tracking and electroencephalography) from study participants. This will be done with the help of an expert in such tools.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

Human-Nature Interaction

The UN Climate Panel has declared code red for humanity, but these facts have not been translated into large-scale actions. However, emotions have shown to be much more likely to spill over into engagement. Direct experience of nature has a positive influence on the wellbeing of humans, and loss of such experiences is a growing concern for humans living in urban environments.

Faglærer: Dag Svanæs     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Hybrid human‐AI learning technologies

Education is a unique area for the application of artificial intelligence (AI). In this topic, the augmentation perspective and the concept of hybrid intelligence will be used to guide this work. The candidates will engage with the design (co-design or participatory design) of learning services (e.g., interfaces or other artefacts) to showcase the challenges and opportunities of hybrid human‐AI learning technologies. The six levels of automation model will be used to identify the roles of the various AI users (e.g., learners, teachers). The transition of control between teacher or students and technology needs to be articulated at different levels and related to the augmentation perspective.

Faglærer: Michail Giannakos     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

ICT for Health & Well-being in Built Environments

 

Faglærer: Sobah Abbas Petersen     Status: Tildelt     Egnet for: En student     Lenke: plink

Identifying Online Hate Speech and Cyber Bullying

Read also: Writing a Master's Thesis in Language Technology

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Improved Ambulance Response: Optimization

Proposal for master thesis at Norwegian Open AI Lab, NTNU, supported by Norwegian National Advisory Unit for Prehospital Emergency Medicine (NAKOS), and Department of Emergency Medial Communication Centre (EMCC), Division of Prehospital Services, Oslo University Hospital.

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Improved Ambulance Response: Prediction

Proposal for master thesis at Norwegian Open AI Lab, NTNU, from Norwegian National Advisory Unit for Prehospital Emergency Medicine (NAKOS), and Department of Emergency Medial Communication Centre (EMCC), Division of Prehospital Services, Oslo University Hospital.

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Industry (and AI-lab pitched) project proposals related to Visual intelligence (AI/CV) (2024)

NB: for many of the industri proposals listed below a research assistant position can be offered in additon, in some cases even a summer job.

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Innovative education at the intersection of XR and ChatGPT

Emerging technologies such as virtual/augmented/extended reality (VR/AR/XR) and generative AI such as ChatGPT, Midjourney and Sora are already revolutionizing how we live and work. XR has already demonstrated significant potential in transforming educational practices by providing learners with realistic and highly engaging learning experiences. Generative AI is a powerful tool that can be used to quickly and efficiently create a wide range of educational content, including human-like text, videos, images, and even 3D models and software code. The goal of this master project to investigate if the combination of these technologies can contribute to creating innovative education tools for NTNU teachers and students.

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Intelligent Camera Prioritization using Violent Activity Recognition in Large Scale Surveillance Networks

Digital surveillance systems are universally installed that continuously generate a massive amount of data, which require manual monitoring to recognize uncertain human activities in public areas. Intelligent surveillance systems are highly desirable that can automatically identify normal and abnormal activities to allow efficient monitoring by selecting only those cameras, where abnormal activities occur. The purpose of this work is to proposes an energy-efficient camera prioritization framework that intelligently shares priority between cameras in a vast surveillance network using feedback of the activity recognition system. The proposed work consists of: 1) addresses the limitations of existing manual monitoring surveillance systems, 2) select the salient frames from the online video stream, 3) compute a feature map using lightweight deep learning model to rank the priorities of the cameras, 4) the probabilities predicted in step 3 and metadata of the cameras are processed to compute the priorities, 5) selection of the camera based on step 4

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: En student     Lenke: plink

Interesting AI Project

In this project, the joint interests of advisor and student(s) come together. 

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Investigating and comparing alternatives to the Eclipse Modeling Framework (EMF)

TL;DR: The Eclipse Modeling Framework it is the main open source framework for working with Model-Driven Engineering tasks (think of TDT4250). We want to investigate what are the available alternatives and what features they support.

Faglærer: Leonardo Montecchi     Status: Valgbart     Egnet for: En student     Lenke: plink

Investigating LLMs for model-to-model transformation tasks

TL;DR: Evaluate the ability of Large Language Models (LLMs) to generate code in a specific programming language that is used for manipulating graphical models in software engineering. Experience with LLMs is recommended. Will use publicly available data.

Faglærer: Leonardo Montecchi     Status: Valgbart     Egnet for: En student     Lenke: plink

Investigating LLMs for the verification of coding quality rules

TL;DR: Apply Large Language Models (LLMs) to the problem of verifying if source code satisfies given coding rules. Coding quality rules exist for different purposes, for example the SEI Cert Coding Standards focus on security. Experience with LLMs is recommended. 

Faglærer: Leonardo Montecchi     Status: Valgbart     Egnet for: En student     Lenke: plink

Investigations on Situation Awareness Systems for an Autonomous Ferry (H2024-V2025)

This is a project targeting selected AI-related aspects of a Situation Awareness System for an autonomous ferry, and will be performed in close cooperation with the Trondheim-based company Zeabuz.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Jordkloden er rund! - ekstern oppgave

Jordkloden er «rund»
Ønsker du å jobbe med teknologi som har potensiale for reell verdiskaping i industrien og samfunnet generelt? Nei vi snakker ikke GPT og LLMs denne gangen, LLMs er en AI som ikke har forståelse. Vi jobber med conceptual computing, mao vi søker modeller som har forståelse.
1 Introduksjon
I4 TECHNOLOGY er et teknologiselskap som utvikler digitale løsninger for å realisere Industri 4.0 for BAE-næringen.
Vi jobber med AI bygget på semantisk web, den 3je generasjonen av internett, som separerer apper fra databaser. For bygg- og eiendomsindustrien betyr dette mulighet for å løse to sentrale produktivitetsutfordringer:
• Strukturering og Lagring av data
• Kommunikasjon mellom mennesker, systemer, organisasjoner etc
I4T vil tilby reelle digitale tvillinger for bygninger slik at man enklere kan lage løsninger for kommunikasjon og prosessoptimalisering på toppen.
For å få til dette er det er en lang rekke funksjoner som må utvikles. I4T har identisert en del grunnfunksjonalitet/generelle fuksjoner/moduler som vil være nyttig for både vår egen applikasjonsutvikling, men også være generelt nyttig for en lang rekke applikasjoner som utvikles for internet.
I4T er på leting etter dyktige personer som kan hjelpe oss å utvikle våre løsninger videre. Studenter som beviser at de kan bidra vil vi være interessert i å diskutere ansettelse med, når vi skalerer virksomheten. Utviklet kode som vi kan bruke i våre løsninger vil bli kompensert.
2 Føringer
2.1 Teknologisk plattform
I4T bygger våre løsninger på en generell plattform for semantisk web (eks. Solid og RDF), utviklet av Graphmetrix Inc.
Vår frontend utvikles i javascript ved hjelp av rammeverkene VUE.js 2 og Threejs. Disse er mye brukt og vil være nyttig for studenter å gjøre seg familiære med.
2.2 Arbeidsmetode
Agile utvikling
• Skrive brukerhistorie. Starter alltid med noe lignende:
o Som Bruker ønsker jeg å kunne rotere 3d objektet på en behaglig måte
o Som Systemeier…
• Skrive spesifikasjon:
o Krav:
 Kunne fungere på alle enheter
 Prestere uten høy cpu load
o Beskrivelse
 Eksempel: Ved “onmousedown” skal punktet til musen settes som 3d motorens senter punkt. …
• QA på spec fra I4T
• Utvikle i henhold til spec
• QA på kode fra I4T
3 Jordkloden er “rund”
Representere kart på rund jordklode.
Oppgaven går ut på å finne en måte å vise en rund jordklode, for så å legge på et kartlag.
Idag bruker vi OpenStreetMap til å vise et kartgrunnlag i vår 3d viewer. Dette representerer ikke verden på en god måte, og vi ønsker å kunne bruke vektorer fra jordklodens sentrum til å posisjonere elementer i verden.

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Language Technology and Large Language Models with Applications

Background and Problem Description

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Large Language Models for Optimizing Search

This project aims to explore the integration of Large Language Models (LLMs) to enhance the performance of machine learning-based search engines. Traditional search engines generate ranked lists of relevant documents based on a query. LLMs have been found to better understand queries and assess the relevance and significance of a document compared to conventional methods.

Faglærer: Benjamin Uwe Kille     Status: Tildelt     Egnet for: En student     Lenke: plink

Learning about AI in schools

The task will start with a review about how AI is currently introduced to students at schools (lower and upper secondary), either as part of the curriculum or in extra-curricular activities. The task will then continue with the design of an intervention that can be used to introduce AI in a an engagement and playful way, with focus on responsability and sustainability. 

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Learning analytics and AI learning systems in Norway

Supervisors: Michail Giannakos
Place: LCI Lab: https://lci.idi.ntnu.no/
Suitable for: One or two students

Faglærer: Michail Giannakos     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Learning in ,or learning from, self-organizing growing neural network

Keywords: ANN methods and principles, bio-inspired design, evolution, development, genetic algorithm, self-organization, c/c++.

Faglærer: Gunnar Tufte     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Let the LIMO floor robot solve interesting problems

In our group, we have procured a LIMO small scale floor robot from AgileX
https://global.agilex.ai/education/4

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Leveraging Longitudinal EHR for CDSS

Problem description
Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection [1]. Annually, there are approximately 50 million sepsis cases and around 11 million sepsis-related deaths worldwide [2] representing nearly 20 % of all deaths globally.
In Norway, patient health records have been stored electronically since the late 80s [3]. This data though collected primarily from an operational standpoint provides many opportunities for research and for health informatics applications [4]. One such case is the availability of lifelong medical histories of patients that had at least one episode of suspected bloodstream infection (BSI) at St. Olav’s hospital between 2015-2020.

Faglærer: Rajeev Bopche     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

LLM-enhanced computerized adaptive testing for programming education

Develop a simple Computerized adaptive testing (CAT) that operates with multiple choice (MC) questions and provides explanations based on a conversational agent/LLM. In other words, a teacher can load MC questions into a system and create a test, and allow students to log in and test their skills on a specific topic. The system will be adaptive, in a way that it will give you questions that are appropriate to your skills (e.g. if you fail a medium-difficulty question it moves you to an easier one, if you are correct, it moves you to a more difficult one). The system will be tested using this question bank: https://web-cat.org/questionbank/ 
The goal of this topic is to investigate and evaluate the use of CAT in CS1 and CS2 topics in HE.

Faglærer: Michail Giannakos     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Low-cost bootstrapping startups with virtual assistants - a design science approach

Early stage startups face several challenges, and the lack of support often leads to failure. Entrepreneurs need to focus on core tasks such as developing their product, marketing, and customer acquisition. However, managing day-to-day tasks such as administrative tasks, managing emails, scheduling appointments, and answering frequently asked questions can consume a significant amount of time and effort. Bootstrapping is a convenient approach favored by many startup founders.

Faglærer: Anh Nguyen Duc     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Læringsopplegg om AI for politikere (requires good language skills in Norwegian)

I en verden preget av teknologisk endring er det viktig at politikere har en grunnleggende forståelse av de teknologiene de skal bestemme lover i forhold til. I samarbeid med Universitetet i Århus i Danmark så har vi startet et forskningsprosjekt for å øke IT-kompetansen blant nordiske politikere. Som del av dette prosjektet skal det lages demonstratorer av ulike IT-teknologier. I forhold til AI så skal det lages en enkel brukervennlig app som illustrerer læring av kategorier. Denne skal inngå som del av et læringsopplegg for ungdomspolitikere om digital teknologi.

Faglærer: Dag Svanæs     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Læringsteknologi

Læringsteknologi er programvare og andre teknologiske produkter som understøtter læring og undervisning. Her er det mulighet for selvvalgte oppgaver enten fra studenter eller studenter i samarbeid med fagstab, og prosjekter som kan relateres til enten Excited senter for fremragende utdanning.

Faglærer: Trond Aalberg     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Machine Learning for Black-box Safety Validation of Autonomous Marine Vessels

Background

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Machine Learning for Causal Analysis of Air Quality

This project focuses on machine learning for causal analysis with a focus on interventions and counterfactual analysis, with application to air quality in Trondheim and other municipalities. The idea is to develop a causal model that includes things like exhaust from vehicles, use of studs, wood burning, ship traffic, cleaning of roads, …, and their impact on air quality. In other words, the goal is to develop a causal model containing causes and effects related to air quality, perform machine learning from air quality and related data, and improve understanding and decision making related to air quality in many municipalities.

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Machine Learning for Effective Ocean Data Analysis

Background

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Machine Learning for Integrating Visual and Language Intelligence

Integrating multimodal data, specifically visual and language data, is the focus of this project.  Much of the progress has been due to advances in machine learning and the availability of suitable data sets consisting of images, each with multiple captions. 

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Machine learning for nanomagnetic computing systems

Introduction

Faglærer: Johannes Høydahl Jensen     Status: Valgbart     Egnet for: En student     Lenke: plink

Machine Learning wih Applications

Background

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Machine learning-based prediction of revenue for actors providing Mobility-as-a-Service (MaaS)

The goal is to develop a machine learning-based tool that can predict the revenue of an actor in a MaaS ecosystem with predicted ticket sales.

Faglærer: Xiaomeng Su     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Manuell oppfølging av sau på beite

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Measuring the content of a desired component inside a microalgae cell

Problem description: measuring the content of a desired component inside a microalgae cell

Faglærer: Massimiliano Ruocco     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Measuring the learning experience

The project aims to study various aspects of learning using biometric sensors such as EEG (brain activity), eyetracking (gaze and attention) ans GSR (galvanic skin response) sensors. Potential scenarios could be individual or group work, interactive classroom sessions, practical activities etc. 

Faglærer: George Adrian Stoica     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Memetic and Multimethod Stochastic Optimization Algorithms

Project Background

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

MIC - Medical Image Computing / Analysis and AI/CV (most organs / most modalities) (2024)

Together with MIRA, SINTEF Health, the medical faculty at NTNU and St Olav university hospital we are offering medical image computing (MIC) projects, based on Deep Learning (DL) and Computer Vision (CV), and related to:

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

MIC - Segmentation of anatomic structures in medical images

Generic 3D medical image segmentation with modern neural networks that are applicable to various clinical tasks, such as segmentation of aorta in CT images, brain segmentation in MRI slices, prostate segmentation in MRI volumes.

Faglærer: Gabriel Kiss     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Mitigation strategies for Membership inference attacks on AI algorithm

In the age of advanced computer vision models, ensuring privacy and security is paramount. This project dives into the realm of membership inference attacks in the context of computer vision. A membership inference attack is a sophisticated form of privacy breach where an adversary attempts to determine whether a specific data point was used in training a machine learning model.

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: En student     Lenke: plink

Modeling cell-cycle phase distribution in cell cultures

The cell-cycle is a fundamental molecular process, and disruptions in this process are a hallmark of cancer. When studying eukaryotic cell cycle, the most common approach involves synchronizing a cell culture. In this method, the cells, grown in a medium, are prevented to progress through the cell cycle past a certain phase, usually through use of a chemical agent. The block is then released, and the cells are allowed to progress through the cell cycle.

Faglærer: Pål Sætrom     Status: Valgbart     Egnet for: En student     Lenke: plink

Monitoring Norwegian nature loss with satellite imagery and AI

NINA contributed the map of nature loss in Norway for the NRK article Norge i rødt hvitt og GRÅTT. This map was produced from outputs of a FCNN produced by Google called Dynamic World. There are two major points for improvement on the current nature loss map: (1) building a local machine learning model trained on Norwegian reference data; and (2) instead of mapping land cover for separate points in time and then analyzing the change (like in Dynamic World), create a model which directly detects and maps change. This Master's project offers a unique challenge in the realm of machine learning, diverging significantly from traditional computer vision tasks. In contrast to regular RGB images, satellite image time series provide rich spatial, multi-spectral and multi-temporal dimensions. The student might explore cutting-edge models like 3D CNNs, RNNs (including LSTMs), and Transformer models, aiming to capture the dynamic nature of ecological transformations. NINA has, through expert annotation and crowdsourcing, gathered over 10.000 polygons defining the loss of nature over Norway in the past 5 years. We also have established pre-processing pipelines for the relevant satellite imagery to be used. The Master’s student will be able to focus on testing different model architectures to leverage the multiple dimensions of the input data. The outcome will contribute substantially to environmental conservation efforts and offer a novel perspective in applying AI to ecological monitoring.

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Multi label classification report of medical image (X-rays, MRI and CT) Translation

Huge data in the form of raw text is available in radiologists’/specialists’ clinical notes, where its rough nature makes it challenging for meaningful information extraction such as specific disease names, entity recognition, and concept extraction. The amount of data increases daily, thereby requiring efficient browsing and searching techniques for effective and useful contents extraction that is utilized in further research. Clinical Natural language processing (CNLP) is a prominent solution in this direction, dealing with concept extraction from clinical text, diseases identification and early predictions of medical outcomes based on medical history. A handsome amount of techniques is developed using CNLP strategies, but still there is a space for effective and efficient methods that are precise and trustworthy for real-world implementation.

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: En student     Lenke: plink

Multiplayer game development using Web3 Technologies

Game development is a large well-known area in traditional web development. However, it is still to be seen how the emerging web3 technology will take it a step further!

Faglærer: Surya Bahadur Kathyat     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Music from Magnets

Introduction

Faglærer: Johannes Høydahl Jensen     Status: Valgbart     Egnet for: En student     Lenke: plink

Natural Language Processing for Political Texts

In the era of Open Date, many parliaments publish transcriptions of sessions. These documents can help to advance public education about politics. Speeches reflect politicians' viewpoints on political matters. Social Sciences have invested a lot of effort into analyzing such data. AI, in particular Natural Language Processing, promises to automate the process. Specifically, recent advances in Deep Neural Networks, such as Transfomers, have pushed the capabilitites of AI systems. We seek to explore the use of AI for automated political speech analysis.

Faglærer: Benjamin Uwe Kille     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

NeRFs - Neural Rendering and photorealistic Reconstruction of complex 3D scenes (2024)

Interested in the future of real-time photorealistic reconstruction of complex 3D scenes?

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Neural methods for sequence-based taxonomic classification

Determining the taxonomic class of a given genetic sequence is an important problem in bioinformatics. Classical methods rely on summary statistics of short sub-sequences, which loses much information on distant interactions and thus have limited accuracy.

Faglærer: Zhirong Yang     Status: Valgbart     Egnet for: En student     Lenke: plink

Neural Radiance Fields (NeRF) for real time 3D-reconstruction (H2024-V2025)

Neural Radiance Fields (NeRF) [Mildenhall et al. 2020] is a state of the art technique to generate 3D models by essentially overfitting neural networks, using them as efficient storage containers of the scene information. The NeRF method is a very simple and elegant solution to the problem of 3D reconstruction of a scene, using rudimentary ray tracing techniques for comparison with ground truth images to train the model on a basic multi-layer perceptron network. Later works have expanded this to work on random image sets [Brualla et al. 2021], for dynamic scenes [Pumarola et al. 2021], and even for scenes of any scale [Tancik et al. 2022].
However, up until recently they have been quite slow methods, taking up to 2 days to train on a high-end GPU, and 1-2 minutes to reconstruct a single image of the scene. A paper just released by NVIDIA [Muller et al. 2022] changes this, going from days of training time to seconds, making real time implementation of NeRF possible.
To follow up on that work, we are interested in figuring out if it is possible to apply NeRF as a tool for 3D reconstruction in real time simultaneous localization and mapping problems, producing 3D environments of real time videos.
[Mildenhall et al. 2020]
https://www.matthewtancik.com/nerf
[Brualla et al. 2021]
https://nerf-w.github.io/
[Pumarola et al. 2021]
https://www.albertpumarola.com/research/D-NeRF/index.html
[Tancik et al. 2022]
https://waymo.com/research/block-nerf/
[Muller et al. 2022]
https://nvlabs.github.io/instant-ngp/

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

No-Code Development Platforms

The thesis will draw from the literature on no code and or low code development platforms. The student needs to review the literature and acquire a good overview of existing platforms and approaches which are used today

Faglærer: Ilias Pappas     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Omniverse, Content generation, Simulation and Digital Twins (2024)

Ultimate Visual Computing and AI project.

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

On the adoption of Generative AI in software development - the state of practice, challenges and opportunities

Software development is a complex process that requires both technical competence and the ability to communicate and collaborate effectively. One potential solution to improve the software development process is the integration of generative artificial intelligence (AI) technologies. ChatGPT, a large language model, has the ability to process natural language inputs and generate human-like responses according to given objectives. There has been evidence on the adoption of ChatGPT in project scheduling, user story writing, code generation and testing.

Faglærer: Anh Nguyen Duc     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

On-device personalization of large language models on edge devices

Large language models (LLMs) will be gradually implemented on edge systems, like mobiles, self-driving cars, etc., as a powerful AI assistant. Normally, LLMs are trained to provide broad knowledge to different users. However, as learned from lectures on convolutional neural networks (CNNs), personalizing CNNs (removing some classes never used) can reduce the model complexity and latency while in some cases improving accuracy. The goal of this project is to evaluate an on-device personalization method for LLMs on edge systems. This means that the entire personalization procedure will be conducted on edge systems without the assistance of remote servers. Thus, the proposed method should be low overhead and hardware-aware so that the limited computational power of edge systems can be fully utilized to achieve this goal.

Faglærer: Di Liu     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Óne citizen - óne digital health twin (an ecosystem of smart digital twins - towards a data-driven health service) (2024)

Denne oppgaven tilbys i samarbeid med MIA Health

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Open data - from vision to reality?

Open data involves the pooling and collecting of data across a community, industry or group of stakeholders. The motivation is the vision (aspiration, hope, belief...) that by making data openly availble, hence accessible to everyone, this will boost productivity through enhanced collaboration or create more well-functioning markets. Examples include: Open Target in pharmaceutical industry, the EU's PSD2 regulative towards open banking in finance, or HUNT research database at NTNU.

Faglærer: Eric Monteiro     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Open data and data platforms – how to succeed in practice?

Open data (together with data platforms and data spaces) involves collecting and sharing data across industries. The motivation is that by making data openly available, productivity is increased through enhanced collaboration or create more well-functioning markets. One successful example is how the pharmaceutical industry collaborated to develop and deploy Covid vaccines at record speed. Other examples include: the EU's PSD2 regulative towards open banking in finance and BarentsWatch for monitoring the seas.

Faglærer: Marius Mikalsen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Open Foundation Models / LLM (type ChatGPT), fine-tuned and adapted to Health and integrated in a Digital Twin setting (2024)

The content can be adjusted based on the interests and background of a given student. But some interesting research questions to look into could be:

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Open-ended evolution

Life on earth displays an explosion of creativity and diversity. One single run of evolution has managed to create both photosynthesis, flight and human intelligence and is still presenting us with new solutions to the problem of survival and reproduction on earth. This capability for never-ending creation of novel organisms is what the field of open-ended evolution is trying to replicate.

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Optical flow estimation with event-cameras

As computer-vision is becoming increasingly incorporated in our daily lives, such as through autonomous driving, the need for expensive and powerful computer hardware also increases. A reason for this major demand in computing power is arguably attributed to the cost for interpreting human-explainable RGB images produced by framed based cameras. Images produced by frame based cameras were historically intended to be used by humans, not robots. Whereas frame-based cameras produce dense synchronous data with lots of redundant information, event cameras produce the exact opposite sparse responses as they happen. This is far more effective from the perspective of robotics. Event-based cameras have remarkable advantages in challenging robotics conditions involving high-dynamic range and very fast motion.

Faglærer: Xiaomeng Su     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Organizational decision-making in the age of AI

Artificial Intelligence is now being used at an increasing rate to augment or automate organizational decision-making. From processes such as performing credit checks on customers of banks, aiding in forecasting of future events, and automating manual and repetitive tasks, AI is introducing a new way of making decisions for organizations. The purpose of this project is to examine through empirical methods the effects and processes of transition to AI-based decision-making structures.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

Out-of-distribution detection for time-series

Developing OOD method using deel learning and time-series characteristics.

Faglærer: Odd Erik Gundersen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Particle Swarm Optimisation as a modelling tool for Climate Change

Particle Swarm Optimisation, where each particle may be thought of as an individual in a society of particles, provides a  technique to model influences and effects in society that may prove beneficial  in the battle against Climate change.  This project is focussed on developing a PSO modelling environment that enables the study how an individual may become more inclined to increase their individual contribution/sacrifice for the benefit of future populations. 

Faglærer: Pauline Haddow     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Pedagogical chat bot

The task involves developing a domain specific conversational AI bot for a Hyper Interactive Intelligent Presenter. The bot would be trained to converse on a domain-specific topic for a course and will be useful to assist user in acquiring basic knowledge about a topic and/or for querying relevant material available on the course page. The bot is to be trained using deep learning on a variety of topics within a domain and be evaluated against different queries.

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Personalized Health

 

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: En student     Lenke: plink

Personalized rehabilitation training video generation with sensor and Generative AI

Many patients need personalized training videos to perform rehabilitation at home. The current training videos from therapists are standardized, and do not fit individual needs. 

Faglærer: Jingyue Li     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Predicting Social Media Personalities, Values and Ethics

Read also: Writing a Master's Thesis in Language Technology

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Predictive Maintenance in Building Automation & HVAC Systems

Many regular maintenance operations occur over the lifetime of a commercial building. This includes for example replacement of air filters which filter the air supplied into a building. Short maintenance cycles stay on the safe side by replacing filters too often before any efficiency loss or down-time occurs. This may lead to time and material consuming replacements before they are actually necessary.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

Predictive Maintenance in Fish Farming

Many regular maintenance operations occur over the lifetime of a fish farm. This includes for example cleaning of the feeding mechanism or the tubes through which the feed is distributed to the fish-nets. Short maintenance cycles stay on the safe side by cleaning too often before any down-time or damage occurs. This may lead to time-consuming cleaning before it is actually necessary. Many fish-farm operators develop a good intuition for when a cleaning cycle is necessary, but this is not easily reproducible or transferable across employees.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

Prediksjon av råvarepriser

Anvendelsen av maskinlæring og AI for å predikere råvarepriser i markeder som opplever eller forventes å oppleve eksponentiell vekst.

Faglærer: Anders Kofod-Petersen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Problems in bioinformatics

The bioinformatics group works on developing and using computational models to predict how changes in gene regulation can control development and cause disease. Towards this end, we develop custom algorithms, statistical simulations, and machine learning-based solutions to analyze and interpret biological data; examples of previous MSc-theses include a genetic programming (GP) approach to predict microRNA target sites, a support vector machine (SVM) approach to identify microRNA genes, and an approach that combines GP and SVMs to identify related proteins.

Faglærer: Pål Sætrom     Status: Valgbart     Egnet for: En student     Lenke: plink

Pseudo labelling of medical data

One of the problems in publically available chest x-ray datsets is the lack of annotations/labels on x-rays for various abnormalities. For instance, the ISU chest x-ray dataset contains reports describing the issues present in the image, yet it is not easy to localize the areas having abnomalities on the images. Apart from the expert radiologists, it is even hard for the doctors to identify the abnormalities in x-rays. This project's objective is to use transfer learning approach to train deep learning models for similar abnormalities present in VinDR dataset or similar datasets to ISU for generating pseudolabels. 

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

RAY TRACING DATA STRUCTURES (with ARM)

The gold standard for computer graphics is and always has been the simulation of real light dynamics through ray-tracing, but due to the high demands on compute it has seen little use in real time applications. In recent time ray traced real-time graphics has become a reality, with the last year even bringing ray-tracing capabilities to mobile devices. The advances that made this possible are several, including better process nodes for silicon, advances in neural network based denoising, novel temporal antialiasing techniques and improvements in Bounding Volume Hierarchy (BVH) construction. Ray tracing relies heavily on specialized data structures to make the intersection test between the ray and scene efficient using some kind of Acceleration Structure, with the standard approach being the use of a BVH. The BVH is a spatial data structure, and lends well to GPU warp-based execution because rays issued from nearby pixels (and scheduled on the same shader core) are likely to traverse the same part of the tree. This allows the tree nodes to be re-used for multiple threads of execution saving an order of magnitude in bandwidth. The difference between a well- and poorly constructed BVH can account for more than 50% of the ray traversal performance making quality a very sensitive topic. Another issue is that higher quality build algorithms naturally require more time, to the point where building the BVH takes too long to be feasible in a real-time environment. Due to this tradeoff between traversal performance and build time the field of acceleration structure construction is wide open and there are multiple heuristics that can be applied in attempt to get ahead in one way or another. The complexity of the problem is further increased by the fact that different hardware accelerators have different performance characteristics, meaning the same algorithm may not be the best everywhere. This all means that the construction of BVHes is not in general well understood, and there is ample room for innovation.

Faglærer: Theoharis Theoharis     Status: Valgbart     Egnet for: En student     Lenke: plink

RDF chat - ekstern oppgave

RDF Chat
Ønsker du å jobbe med teknologi som har potensiale for reell verdiskaping i industrien og samfunnet generelt? Nei vi snakker ikke GPT og LLMs denne gangen, LLMs er en AI som ikke har forståelse. Vi jobber med conceptual computing, mao vi søker modeller som har forståelse.
1 Introduksjon
I4 TECHNOLOGY er et teknologiselskap som utvikler digitale løsninger for å realisere Industri 4.0 for BAE-næringen.
Vi jobber med AI bygget på semantisk web, den 3je generasjonen av internett, som separerer apper fra databaser. For bygg- og eiendomsindustrien betyr dette mulighet for å løse to sentrale produktivitetsutfordringer:
• Strukturering og Lagring av data
• Kommunikasjon mellom mennesker, systemer, organisasjoner etc
I4T vil tilby reelle digitale tvillinger for bygninger slik at man enklere kan lage løsninger for kommunikasjon og prosessoptimalisering på toppen.
For å få til dette er det er en lang rekke funksjoner som må utvikles. I4T har identisert en del grunnfunksjonalitet/generelle fuksjoner/moduler som vil være nyttig for både vår egen applikasjonsutvikling, men også være generelt nyttig for en lang rekke applikasjoner som utvikles for internet.
I4T er på leting etter dyktige personer som kan hjelpe oss å utvikle våre løsninger videre. Studenter som beviser at de kan bidra vil vi være interessert i å diskutere ansettelse med, når vi skalerer virksomheten. Utviklet kode som vi kan bruke i våre løsninger vil bli kompensert.
2 Føringer
2.1 Teknologisk plattform
I4T bygger våre løsninger på en generell plattform for semantisk web (eks. Solid og RDF), utviklet av Graphmetrix Inc.
Vår frontend utvikles i javascript ved hjelp av rammeverkene VUE.js 2 og Threejs. Disse er mye brukt og vil være nyttig for studenter å gjøre seg familiære med.
2.2 Arbeidsmetode
Agile utvikling
• Skrive brukerhistorie. Starter alltid med noe lignende:
o Som Bruker ønsker jeg å kunne rotere 3d objektet på en behaglig måte
o Som Systemeier…
• Skrive spesifikasjon:
o Krav:
 Kunne fungere på alle enheter
 Prestere uten høy cpu load
o Beskrivelse
 Eksempel: Ved “onmousedown” skal punktet til musen settes som 3d motorens senter punkt. …
• QA på spec fra I4T
• Utvikle i henhold til spec
• QA på kode fra I4T
3 RDF Solid Chat app
Kommunisere med bruk av RDF
Lage en applikasjon der man kan kommunisere på en enkel måte ved bruk av RDF Grafdatabase.
Denne skal kunne bli brukt som et verktøy når man jobber i prosjekter og vil samhandle og dele informasjon.
Grunnen til at man trenger en ny chat app er at denne vil gjøre informasjonen søkbar ved bruk av samme søkemotor som man bruker til database og dokumenter. Hvert innlegg vil lagres i en SpaceTime grafe, og kunne finnes tilbake til.
Videre vil man kunne chatte rundt fremdrift i utvikling av dokumenter, issues, prosjekter osv .
To utforminger
• En del chat app,
• en del SoMe app.
(Eks. Facebook og messenger)

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Ready for digital transformation - a co-design card based tool

Digital transformation is influencing all the workplaces. Not always the digital transformation that is envisioned is successful, as witnessed by, for example, the challenges connected to the introduction of the Helseplatformen. One aspect that is often under-estimated is connected to the competences that are needed to workers to participate to the digital transformation in a meaningful way. 

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Ready for digital transformation - a game approach

Digital transformation is influencing all the workplaces. Not always the digital transformation that is envisioned is successful, as witnessed by, for example, the challenges connected to the introduction of the Helseplatformen. One aspect that is often under-estimated is connected to the competences that are needed to workers to participate to the digital transformation in a meaningful way. 

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Reflective and collaborative game for learning about AI

AI tools are widely spread and are used in different contexts,  in schools, in the workplace, and as support to everyday practices. As a consequence, we all need to develop the digital competences that are necessary to use, understand, and influence the development of AI tools. This task aims at designing games for learning about AI and developing these competences. 

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Reinforcement learning for multi-agent simulated autonomous driving

This topic is about creating a self-learning multi-agent scheme for steering simulated cars in an urban environment. The focus is on developing a Reinforcement Learning scheme for this application.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Reinforcement learning for multi-agent simulated autonomous driving (H2024-V2025)

This topic is about creating a self-learning multi-agent scheme for steering simulated cars in an urban environment. The focus is on developing a Reinforcement Learning scheme for this application.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: En student     Lenke: plink

Reliability evaluation of approximate computing techniques through fault injection

TL;DR: Approximate computing studies how to provide “good enough” results for a certain application. It is used in different context, for example resource constrained devices or when operating in degraded mode. We want to evaluate the impact of faults on different approximate computing techniques.

Faglærer: Leonardo Montecchi     Status: Valgbart     Egnet for: En student     Lenke: plink

Responsible AI (RAI) in Practice

Artificial intelligence (AI) applications can increase the efficiency and quality of processes across sectors including public services. However, their inner workings can be incomprehensible making it hard to explain how they transform data inputs to outputs. This is known as the “black box” problem which can impede the involvement of humans in shaping, operating and monitoring the use of AI in service delivery.

Faglærer: Ilias Pappas     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Responsible AI in organizations

The notion of responsible AI entails a large range of aspects regarding how AI applications are developed, utilized, and monitored throuhgout their lifecycle. The purpose of this project is to explore what responsbile AI means for organizations, which processes and structures they are establishing in order to attain set indicators of responsible AI, as well as what are the organizational impacts of it. Does adopting responsbiel AI result in any organizational gains? Does it influence how customers/citizens perceive the organization, or is it restricting what they can do with novel technologies?

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

Robust obfuscation of Wasm

Code obfuscators such as Binaryen are often used in security research to test detection capabilities of malware/cryptojacking code. A code obfuscator must tread the thin line between detection avoidance and efficiency (low-overhead).

Faglærer: Donn Alexander Morrison     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Safety evaluation of trajectory planners in autonomous vehicle scenarios

TL;DR: Evaluate different object detection and/or trajectory planing algorithm from the safey perspective. Need to know some machine learning for object detection. Builds on existing research and a previous Master’s thesis.

Faglærer: Leonardo Montecchi     Status: Valgbart     Egnet for: En student     Lenke: plink

Samarbeide og kommunikasjon i helsesektoren

Enhetlig pasientbehandling blant helsearbeidere i helsesektoren undergraves av ulike former for grenser - eografiske, institusjonelle og profesjonelle. Dette er til hinder for effektiv og høykvalitet pasientbehandling. Eksempler inkluderer samarbeide mellom fastlege og sykehus, eller samarbeide mellom sykehus og kommunehelsetjenesten herunder eldreomsorgen.

Faglærer: Eric Monteiro     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Sau og rovdyr

Sau slippes ut på beite på våren og går på beite i ca 16 uker. Bonden skal holde ukentlig oppsyn med de i denne perioden. Mye sau har i dag en radioenhet som daglig rapporterer hvor de er. Rovdyr kan angripe, skade og drepe sau på beite. Vi er her interessert i å detektere om det er rovdyr i sauens beiteområde basert på analyse av sauens bevegelsesmønster. Bevegelsesmønsteret i et område hvor det forekommer rovdyr vil avvike fra sauens normale bevegelsesmønster. Dette avviket er vi interessert i å detektere så tidlig som mulig slik at sauebonden har mulighet til å sette inn tiltak for å få sauene ut av området før tap oppstår. Vi har fått tilgang til bevegelsesdata gjennom hele beitesesongen for sau fra et antall bønder. Disse data vil benyttes i analysen.

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Scalable Neural Attention Models for Pattern Recognition with Application to Point Clouds

Most machine learning methods require the inputs to be ordered (e.g., vectors or matrices). However, there exist applications where the inputs have no order. That is, the input to a model is a set. Most conventional ML methods, such as MLP, convolution neural networks, and even the simplest linear model, cannot handle such inputs. Transformer is a method that admits set inputs. However, due to its quadratic computational cost, the original Transformer cannot scale up for large sets (e.g., tens of thousands of items).

Faglærer: Zhirong Yang     Status: Tildelt     Egnet for: Gruppe     Lenke: plink

Scalable Self-Supervised Pretraining with Contrastive Learning

Large-scale self-supervised pretraining is the major motor for this round of AI boost. Our research group has demonstrated circular and multiscale dilated mixing can scale up feature transformation for long sequences. However, the current autoencoder architecture has to measure the denoising loss in raw data space, which is sensitive to various data variations. The pretrained decoder is also not needed for the downstream prediction tasks. We propose to replace the autoencoder with contrastive learning to overcome the above limitations. The new model will mainly be tested on inference tasks for DNA sequences.

Faglærer: Zhirong Yang     Status: Tildelt     Egnet for: En student     Lenke: plink

Secure player data management

Football has become an enormous industry where players are bought and sold based on information about them. This information is not just collected during games, but during trainings, tests, leisure and even when sleeping. In the wrong hands, this information can be used for extortion, manipulate the market or influence betting odds.

Faglærer: Per Håkon Meland     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Security of Home Automation Systems

Home Automation Systems are commonly found in many homes and are there to make our lives easier, but like any other connected devices, are vulnerable to various security threats. Some of the common security challenges associated with home automation systems are:

Faglærer: Per Håkon Meland     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Self-supervised Transformer for sensor based Human activity recognition

Human activity recognition refers to the task of automatically identifying and classifying different activities that a person is performing, such as walking, running, sitting, or standing, based on data collected from various sensors.

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Semi-supervised learning for enhancing rare species birdsong recognition

Read also: Writing a Master's Thesis in Language Technology or Computational Creativity

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Sentiment and/or Figurative Language Analysis in Social Media

Read also: Writing a Master's Thesis in Language Technology

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Shipyard simulator for planning ship-breaking activities

More than 80% of our global goods are transported by ships. Like the goods they transport, ships will eventually become waste and need to be broken down properly. Ship breaking is a complex and dangerous job since ships contain different types of toxic materials that should be properly located, identified, and removed to prevent adverse effects on humans and the environment. 

Faglærer: Yngve Dahl     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Short-term planning for inspection drone using reinforcement learning

This project is about the inspection of a ship tank or similar "environment" using a drone, and aiming providing contributions for making this inspection process fully autonomous -- a task which of course cannot be achieved with a single student project, thus: "contributions to...". In this project we assume that we have some 3D model of a industrial tank, and a set of waypoint coordinates that an inspection drone should visit. The goal of this project is then to train a model to perform short-term path planning between these waypoints using reinforcement learning. In the current concept, a hybrid approach fusing both (deep) reinforcement learning as well as model-based predictive control (MPC) is considered; the design decisions are however made after an in-depth literature study has been performed. The resulting planning module should propose
collision-free efficient paths for the drone to execute, and these short-term plans are to be evaluated in simulations, using them in a receding horizon predictive control scheme.

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: En student     Lenke: plink

Simulating Language and Communication Evolution

Read also: Writing a Master's Thesis in Language Technology

Faglærer: Björn Gambäck     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Smart Desks

The project aims to study various aspects of creating a solution that facilitates sharing office/desk use, converting them into “smart” desks or “context-aware” desks. 

Faglærer: George Adrian Stoica     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Social Robotics: HCI in Norwegian

Robots have become part of our daily lives. Some work in factories while others vacuum our homes. The research field of Social Robotics studies how people interact with robots. Specifically, we want to advance robots to complement us and facilitate communication. The NorwAI Center has access to robots that have sensors for audio-visual input. We are excited to explore new possibilities to address real-life needs.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Sparse Neural Attention

Neural attention methods have been the new motor for artificial intelligence. However, the most popular attention model, Transformer or its variants, suffers from the quadratic computational cost and is thus expensive in practice.

Faglærer: Zhirong Yang     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Species prediction models applied to vulnerability detection in software

The goal of this project is to explore the application of models commonly used for predicting species discovery to the task of identifying vulnerabilities in software systems. Drawing parallels between the process of species discovery and software vulnerability detection, the proposal is to develop or adapt models inspired by species accumulation curves to analyse the cumulative number of software vulnerabilities discovered over time. By utilising historical vulnerability data and considering factors such as software complexity and codebase size (if available), these models will seek to predict the rate of new vulnerability discoveries and estimate the total number of vulnerabilities within a software system. Experience with statistical models and methods will be instrumental.

Faglærer: Donn Alexander Morrison     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Sporing av sau ved hjelp av enkel radioteknologi

Masteroppgave:
Sporing av sau ved hjelp av enkel radioteknologi

Faglærer: Svein-Olaf Hvasshovd     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Sports Video Analytics

One of the key chellenge we aim to address in this project is to predict the shot power of a bat hitting the ball (baseball /cricket) along with the type of the drive/stroke, and bat angle from the video clips. 

Faglærer: Ali Shariq Imran     Status: Valgbart     Egnet for: En student     Lenke: plink

Stochastic Local Search: Algorithms and Applications

In AI and ML, several methods rely on stochasticity or randomization: mutation and crossover in evolutionary algorithms; dropout and stochastic gradient descent in deep learning; stochasticity in stochastic local search (SLS); and randomization in systematic search. SLS algorithms, which we study here, are competitive in solving computationally hard problems such as satisfiability (SAT), sparse signal recovery, scheduling, and most probable explanations in Bayesian networks (BNs). Essentially, SLS algorithms are greedy optimizers that also make random moves in order to avoid getting trapped in local but non-global optima.  Further, SLS algorithms are interesting in that they can studied formally, for example by means of Markov chains.

Faglærer: Ole Jakob Mengshoel     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Supporting learning with cutting-edge XR technology: Apple Vision Pro, VR treadmill and haptic suit/gloves

Immersive technologies such as virtual/augmented/extended reality (VR/AR/XR) have demonstrated significant potential in transforming educational practices by providing learners with realistic and highly engaging learning experiences. In most cases, due to budget and practical concerns, educators use relatively unexpensive XR equipment such as Oculus Quest. While this might be sufficient for many educational situations, it is important to investigate the potentials of more advanced equipment that provides advanced spatial computing possibilities, simulates senses other that sight and hearing and facilitates walking.

Faglærer: Monica Divitini     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Sustainability by design

One of the core competence areas of professional involved in the development of IT is the identification and representation of requirements. For modern IT-solutions sustainability can be considered a key concern, thus we have to look upon how to achieve sustainability by design, and not as an afterthought. This project will investigate areas related to

Faglærer: John Krogstie     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Sustainable migration of cloud infrastructure

Cloud computing is increasingly popular due to its potential benefits in scalability, cost savings, and flexibility. However, it also poses the risk of vendor lock-in, where an organisation becomes dependent on a specific cloud provider's proprietary technology. This dependence can again limit flexibility, increase costs, and hinder migration of software defined infrastructure to more sustainable cloud solutions [1]. To enhance the robustness of their systems, businesses are also looking for multi-cloud supported solutions.

Faglærer: Joakim Henrik M Klemets     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

System for tidlig deteksjon av dysleksi

Forskning ved psykologisk institutt indikerer at en underliggende årsak for blant annet dysleksi og dyskalkuli kan være dysfunksjon i visuell prosessering. Nærmere bestemt er det nerveceller som flytter informasjon saktere hos disse gruppene enn hos de som ikke har disse utfordringene. Disse nervecellene kalles magnoceller og har i hovedsak ansvar for å oppfatte raske forandringer i omgivelsene.

Faglærer: John Krogstie     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

System for vurdering av prosjekt og kode i en læringskontekst

Vurdering og tilbakemeldinger er viktig for læring og i prosjekt og programmingsfag er det behov for mange former for tilbakemeldinger enten fra fagstab, fra medstudenter, eller basert på automatisert testing og analyse. I dette prosjektet skal studenten(e) jobbe med forskjellige elementer av et innleverings- og tilbakemeldingssystem.

Faglærer: Trond Aalberg     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Tag inference

In Norway we have a well-developed standard for naming equipment and components in buildings, TFM. However, abroad there is no such standard and many different conventions are created and used.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

test2

test

Faglærer: Jon Espen Ingvaldsen     Status: Valgbart     Egnet for: En student     Lenke: plink

Text Mining on Medical Documents

Text Mining constitute a field in Artificial Intelligence that parses large collections of documents to detect valuable patterns. Medical documents comprise a plethora of useful information. The information can help to develop better medical interventions or prevent diseases. The master project aims to develop a system that takes in medical documents and extract valuable information. For instance, the system could find relations and produce a knowledge graph.

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: En student     Lenke: plink

The potential of Artificial Intelligence for public administration

While there has been a long discussion about the potential of using Artificial Intelligence in private organizations, now more and more public organizations are implementing solutions to support their operations. From uses for fraud detection, chatbots, autonomous vehicles, or infrastructure monitoring, AI is gaining ground in applications for public administration. This project will be done in connection with SINTEF Digital and will involve data collection, analysis and reporting. The aim is to find out what is the status of AI adoption, what are the potential interesting uses, and what is the value that is realized.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

The Strategic Value of Big Data Analytics

Today more and more companies are using big data analytics to support or drive their business strategies. Yet, there is ongoing debate about whether such investments do indeed create value if so how this value can be captured. The objective of this master thesis is to perform a quantitative study on companies in Norway and examine the ways in which they are applying big data analytics to create business value. The project is in cooperation with the Big Data Observatory (https://www.observatory.no), during which you will learn how to develop research methods and analyze quantitative data.

Faglærer: Patrick Mikalef     Status: Valgbart     Egnet for: En student     Lenke: plink

Tiny Generators for Individual Health Forecasting in Child and Adolescent Mental Health

Objective

The primary objective of this project is to use large sets of health data to generate prospective futures/forecasts for individual patients. These forecasts, while not strictly evidence-based or derived from clinical best practice, outline potential future health-related events, diagnoses, interventions and contacts. Such as a data-based forecast that facilitates discussing potential choices and promoting shared clinical decision-making. A particular use case is to combine these forecasts with actual evidence-based recommendations in order to aid school pedagogical-psychiatric services and general practitioners in taking action or preparing referrals to specialist CAMHS clinics. Specifically, we aim to provide ranked forecasts and evidence-based recommendations according to patient relevance and other criteria. 

Faglærer: Dipendra Pant     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Toolkit for Technologically Rich Education

Supervisors: Sofia Papavlasopoulou, Isabella Possaghi, Boban Vesin

Faglærer: Sofia Papavlasopoulou     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Topological Data Analysis for Industrial Processes

Description in which company/unit the thesis will be placed:
SINTEF Digital, DNV, Kongsberg, NorwAI: In hybrid AI the aim is to combine machine learning and physical knowledge. This is particularly relevant for industrial applications of AI and machine learning on physical systems. Through the SFI NorwAI we work on hybrid AI together with partners in the energy sector. We have two use cases focused on predictive maintenance of wind turbines and on virtual flow meters in oil and gas.

Problem Description:
Industrial data are typically consisting of time series, 3D models, and documents where relevant features have to be identified and extracted automatically. While deep learning approaches have been successfully used to this extent, geometrical and topological methods offer a compelling alternative. These are typically robust to noise and offer a more apparent interpretation and explanation.
Thesis Description:
At first, the performance and sensitivity of topological data analysis shall be tested on industrial data. Approaches based on algebraic topology such as persistent homology shall be tested on time series data for the detection of specific patterns, associated with industrial processes or equipment faults (condition-based monitoring) for machine learning classification. TDA could be also benchmarked against deep learning methods.

Another possibility: similar topological approaches based on homology can be applied to more complex geometric data types such as CAD 3D models of industrial equipment (piping, pumps, tanks, etc.), in order to automatically identify the geometry of the equipment. As this task is computationally demanding, different sampling and averaging approaches based on Radon-measures could be tested [https://arxiv.org/abs/2212.08295] or grid sampling based on eigenfunctions of the Laplace-Beltrami operator.
The project is open-ended and flexible to accommodate the ideas and interests of the students. The project can be adapted to students from several departments such as Mathematical Sciences, Computer Science or Engineering Cybernetics

Data Description:
The project is based on a mix of public benchmark data and proprietary simulations from industrial partners (from Cognite), as well as operational data. This will require approval from the partners before publication.

Faglærer: Terje Brasethvik     Status: Valgbart     Egnet for: En student     Lenke: plink

Topological Methods for Condition Monitoring in Heavy Asset Industries (with Cognite)

Industrial assets’ condition monitoring holds immense potential for reducing downtime and operation costs. However, this task is very complicated and requires domain expertise in addition to advanced data analysis tools.  
While deep learning has been successful in several condition monitoring applications, this lacks explainability and requires a large amount of labelled training data on the many possible failure modes of each piece of equipment. As well, limited benchmarking datasets have hindered the development and validation of effective condition-monitoring systems. 

Faglærer: Benjamin Uwe Kille     Status: Valgbart     Egnet for: En student     Lenke: plink

Transforming IT operations: organisational perspectives

Traditionally, software development and operations have been handled by separate teams. In such an organisation, a central objective of the development team is to introduce new application functionality, something that conflicts with the operation team's goal of ensuring stable and reliable services. The conflict in terms of the functions and values that the two teams provide often lead to the implantation of rigid processes that delay development. Hence, recent approaches aim to break down these barriers, forming multidisciplinary teams (DevOps) that are responsible for the entire lifecycle of an application, including operations and maintenance. Organising people from both development and operations in such teams, while also automating parts of the deployment process, has showed to both enable rapid deployments of new functionality and to increase software quality [1].

Faglærer: Joakim Henrik M Klemets     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

TrønderEnergi: Explainable time-series forecasting

Problem description
TrønderEnergi has several forecasting systems utilizing machine learning methods in commercial operation. The systems are used and the forecasting is monitored by domain experts in the operating center at Berkåk. The domain experts do not have any training and knowledge in the machine learning methods, but they do have to make decision based on their output. At times the experts need to understand why a forecast has the value it has. The literature has to be reviewed, prototypes should be developed and evaluated by domain experts.

Faglærer: Odd Erik Gundersen     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Uncertainty Quantification for Language Models in Safety-Critical Systems

Problem description

This project aims to address the challenge of Uncertainty Quantification (UQ) for Language Models [1], recognizing its crucial role in ensuring confidence and reliability in the generated text. 

Faglærer: Ahmed Abdulrahem Othman Abouzeid     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Uncertainty Quantification in Detection and Classification Problems

Problem Description:

Faglærer: Rudolf Mester     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Uncertainty-aware Deep Learning a.k.a. Deep Bayesian Learning

Description: 
Deep learning has been reported to improve upon previous state of the art in many traditional machine learning tasks, like image classification, recommender systems, text-to-speech, and so on. Nevertheless, there are still fundamentally problematic issues with these systems, that invite theoretical work on extensions of deep learning towards (traditional) probabilistic reasoning. This is a topic of some interest, which has lead to nice tools that can be used for implementation/evaluation, like Tensorflow Probability and Pyro (built on top of PyTorch). For typical research trends, see Part III of "The Deep Learning Book" by Goodfellow et al. The selection of interesting research question(s) in this area will depend on the students' interest.

Faglærer: Helge Langseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Understanding an organisation's adaptive capacity to handle IT incidents

The ongoing digital transformation of organisations and businesses along with the advancing convergence of information technology (IT) and operational technology (OT), provide significant operational challenges in terms of ensuring the stability, robustness, and security of these complex systems. As critical services become reliant on Internet-facing software systems it also makes them more vulnerable for cyberattacks. Recent studies also confirms that the number of targeted attacks is increasing, and that more sophisticated methods are taken into use.

Faglærer: Joakim Henrik M Klemets     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Understanding careers of female software engineers

This thesis is assigned to Mia B.

Faglærer: Maria Letizia Jaccheri     Status: Valgbart     Egnet for: En student     Lenke: plink

Understanding the execution behavior of high-level productivity languages

The majority of modern applications are written in the so-called high-level productivity languages such as Python, NodeJS, Javascript, etc. In contrast, computer architecture and hardware research is mostly driven by software written in compiled languages such a C, C++ etc. The mismatch limits our understanding of how these applications are executed on the hardware/processors. For example, while the code written in C, C++ is handled by the “front-end” structures like instruction cache, branch predictors etc. of a processor, Python and NodeJS application code is handled by the “back-end” structures like data cache. This is because Python and NodeJS runtimes/interpreters are treated as code at hardware level, while both the application code and data are treated at data. Consequently our understanding of how to build efficient hardware/processors for the bulk of these applications is limited.

Faglærer: Rakesh Kumar     Status: Valgbart     Egnet for: En student     Lenke: plink

Underwater object detection and classification

The world beneath the waves holds a wealth of mysteries and potential discoveries. This project embarks on the journey of enhancing underwater exploration through object detection and classification techniques.

Faglærer: Mohib Ullah     Status: Valgbart     Egnet for: En student     Lenke: plink

Unsupervised learning for automatically annotating eye tracking data

Something we humans rarely notice, is how our eyes are in constant movement, and all of these constant movements can be put into four categories. Eye tracking is a technology used to capture an individual's eye movement, and is most commonly achieved by having a small infrared camera for each eye, and then use the center of the pupil as a starting point for further calculations to categorize the eye movements. Unfortunately, this categorization remains expensive, as it has to be conducted by specialists and is time consuming. An easily mistaken assumption in this regard, is that this is a task which would be trivial to automate by defining parameters for what makes a movement fall into a given category and simply use these algorithms to perform the classification. The reality is, however, different and considerably more complex. There are several reason for why this is more complicated that what one might initially assume, but inaccuracies in the captured data (e.g. due to hardware, pupil center algorithms, cornea reflections, etc.) is the most prominent one.

Faglærer: Alexander Holt     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Use of honeypots for understanding attacker methodologies in n-day vulnerable software

This project aims to investigate the efficacy and practical considerations of the use of honeypots in cybersecurity defense strategies. Honeypots can give insights into attacker methodologies and in doing so can bolster overall network security. This project will explore the practical deployment of honeypots simulating n-day vulnerable software (e.g., vulnerable software with an outstanding CVE), focusing on their ability to detect and mitigate various types of cyber threats.

Faglærer: Donn Alexander Morrison     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Using causal information to improve generative models

In this project we will consider how to use causal understanding to enhance a generative model, in order to obtain better generative processes. The initial idea, which of course is open for discussion, is something like the following:

Faglærer: Helge Langseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Using LLMs and conversational agents to support children’s science learning

Supervisors: Michail Giannakos (in collaboration with Ås Vitenparken)

Faglærer: Michail Giannakos     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Verification of coding conventions for Java using CodeQL

TL;DR: GitHub CodeQL is an analysis engine that can be used to perform queries on source code. We want to use CodeQL to write queries that verify coding rules for Java. Coding quality rules exist for different purposes, for example the SEI Cert Coding Standard focuses on security. 

Faglærer: Leonardo Montecchi     Status: Valgbart     Egnet for: En student     Lenke: plink

VIRTUAL DEVICES

Virtual devices (camera, microphone) are becoming increasingly interesting for producing visual and audio effects in digital meetings. This project will investigate the creation of a virtual camera and a virtual microphone in Windows (initially).

Faglærer: Theoharis Theoharis     Status: Valgbart     Egnet for: En student     Lenke: plink

Virtual, Augmented and Mixed Reality for learning AI etc. (2024)

Interested in Virtual, Augmented and Mixed Reality (i.e. XR) related to learning (e.g. AI/ML/DL) and training (e.g. medical cases)

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Vision Transformers (for Visual Intelligence) (2024)

Since its introduction in 2017, Transformers have revolutionised NLP and completely taken over most sequence processing.

Faglærer: Frank Lindseth     Status: Valgbart     Egnet for: Gruppe     Lenke: plink

Visual Creativity / Generative Visual AI / multi-modal foundation models (2024)

Understand and Explore the latest architectures for text to image / video / 3D  for content generation (maybe in combination with AI gen. sound and music) for various applications.