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Our research

We perform research in various areas of Machine Learning and Artificial Intelligence, including deep learning, pattern recognition, and human computer interaction; with applications in digital humanities, education, document analysis, and Industry 4.0.

Selected projects where our group or individual members of the group are involve in:

Natural Language Processing (NLP)

Our current efforts focus on various machine learning methods for NLP tasks. In a recent workshop we have realized a recognition pipeline for the ambitious task of intent classification on little data where we outperformed

LTU-ServiceDesk-Chatbot

A collaborative project with the LTU Service Desk with the aim to build a chatbot to facilitate the provision of solutions for students/staff of the university based on their requests currently handled by the university ServiceDesk. The chatbot will automatically handle the requests and redirect LTU users to possible solutions through textual conversations by using a web interface on the browser.

Partners: 
- LTU Service Desk

Nationellt Rymddatalabb

Nationellt Rymddatalabb will be a national knowledge and data hub for Swedish authorities' work on earth observation data and for the development of AI-based analysis of data, generated in space systems. The purpose of the project is to increase the use of data from space for the development of society and industry and for the benefit of the globe.

Our role: 
- Provide advanced image processing methods tailored to the needs of the pilot projects
- Pilot projects include climate adaptation as well as applications of earth observation data in forestry, fishery, and agriculture
- Organise hackathons and user workshops

Partners: 
- Swedish National Space Agency
- AI INNOVATION of Sweden
- RISE
- Luleå University of Technology

ChatPal

Current mental health service provision in Northern Sweden cannot meet the rising demand to prevent and manage mental ill-health. There is a lack of digital mental health support for tracking symptoms and for providing treatments and coping strategies at the point of need for 24/7. Traditional one-to-one mental health services supporting people with chronic mental illness as well as mild-to-moderate mental illness is expensive and resource-limited. One-to-one intervention support requires significant travel for clients living in rural areas; hence accessibility to traditional treatments are a particular concern. Given mental ill health remains a
stigma, citizens often feel embarrassed when setting up appointments with a support person. Mental ill-health, however, particularly when left untreated can decrease people's functional capacity and create obstacles for participation. Therefore our objective is to use already existing, and refined methods in developing new conversational agents (both text- and speech-based) users can communicate to without the risk of being stigmatized and that can provide support as well rudimentary assessment of emotional and mental well-being.

Drones Learning to Navigate Through World Understanding

Automated drone navigation is a difficult engineering task. This project aims to teach drones navigation using machine learning. Specifically the drones will first learn to understand the world it’s in and then use that knowledge to learn how to navigate. In collaboration with the LTU Robotics Lab.

Partners: 
- LTU Robotics Team, link

HisDoc III

In HisDoc III we target historical document classification for large amounts of uncategorized facsimiles with the intent to provide new capabilities for researchers in the Digital Humanities. In particular, we will address the task of categorizing document images with respect to content, language, script, and layout. To do so, we will leverage the expertise gained from our previous projects HisDoc and HisDoc 2.01. In HisDoc we have shown that historical Document Image Analysis (Dia) can be effectively applied to extract layout structures and textual transcriptions and in the current HisDoc 2.0 project we successfully retrieved additional paleographic information. The novel contributions of HisDoc III will be complemented by these methods to cope with large document collections.

iMuSciCA

iMuSciCA is a pioneering approach using music for fostering creativity and deeper learning. The DIVA group introduces pen- and gesture-based interaction for music co-creation and sound analytics.