Are you using Twitter? Contribute to our research!

28 September, 2020

Dear Recipient,

We study the concept of similarity on Twitter and how similarity depends on the user profile, activity, and the structure of one’s social networks. This study is multidisciplinary between computer science and the humanities.

If you have a Twitter account, we kindly ask you to go to the link below and participate in this survey.

It is noteworthy that there are no correct answers in this survey, and we are only collecting data anonymously for fundamental research purposes.

Thanks in advance,

Research Team

Call for contributions: Journal of Data and Information Science

21 April, 2020

Call for contributions to a Special Issue on Open Government Data (OGD) for Data Analytics and Knowledge Discovery

We are pleased to announce the Call for Contributions to a Special Issue of Journal of Data and Information Science (JDIS) on Open Government Data (OGD) for Data Analytics and Knowledge Discovery. JDIS, a quarterly English language research journal, aims to publish basic and applied research on data-driven analytics for knowledge discovery, is edited by an international team of experts in the fields, and is indexed by ESCI and Scopus. The special issue intends to publish as the 4th issue of 2020.

Co-Guest-Editors-in-Chief for the special issue: Koraljka Golub, Fredrik Hanell, Guangjian Li, Arwid Lund. Read the rest of this entry »

Programming languages for data-Intensive HPC applications: A systematic mapping study

18 March, 2020

Don’t miss out on this publication by Sabri Pllana and other researchers.

A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue.

Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles.

For more information about the publication see:

Workshop on Knowledge Organization for Digital Humanities, March 27th

11 March, 2020

As a satellite event to the world’s annual iConference taking place in Sweden this year, on 27 March LNU’s iInstitute will host a workshop on knowledge organization for digital humanities.

Place: K2054V
Time: 27 March, 9-13

9.00 – 9.15 Coffee available
9.15 – 09.30 Introduction to the workshop and participants
09.30 – 10.15 Shigeo Sugimoto: Metadata for Digital Humanities – An Overview
10.15 – 11.00 Atsuyuki Morishima: Combining the Power of the Crowd and AI
11.00 – 11.15 Coffee break
11.00 – 11.45 Shigeo Sugimoto: Long-term Use of Humanities Data Resources
11.45 – 12.30 Heather Moulaison-Sandy: Research Data Management in the Humanities

Please email if you plan to attend, by Monday 23 March. Thank you!


For more information contact:
Koraljka Golub
Head of the iInstitute
Digital Humanities Initiative Co-Leader
Linnaeus University


6 March, 2020

The findings, experiences, and ideas that emerge from research have traditionally been utilized through academic publication and teaching programmes. However, academic impact alone is no longer enough for a successful research career. With the growing emphasis in the research funding landscape on maximizing impact beyond academia, it is increasingly important that researchers reach wider society by embedding non-academic impact strategies in their projects, by working with a range of non-academic partners, and by using ever more innovative methods of dissemination and utilization. This course showcases a range of approaches researchers can employ to ensure that their research has impact and relevance beyond universities. It will also provide students with tools that will help them best communicate the value of their work to research funders and potential investors.

How to Apply?
Opens 2nd March at 9:00 AM. Please send your application to by 11th May. You should provide your name, department, contact details, and a short description (max. 100 words) of your research project. Please ensure that you obtain your supervisor’s approval for attending the course, and also state their name in your application email.

Eligibility and further details
The course is offered to PhD students in all disciplines from Karlstad University, Linnaeus University, Mid Sweden University and Örebro University. The course consists of three mandatory sessions in the autumnof 2020 (at Karlstad, Sundsvall, and Stockholm). The course syllabus has been evaluated and approved by the Research Education Committee at Karlstad University’s Faculty of Humanities and Social Sciences. The innovation office Fyrklövern covers the cost of your course travel and accommodation. The course is taught in English.

The syllabus at

For more information contact: Martina Lago, Innovation Advisor, or 0480-446062
PhD-course 2020

2nd Baladria Summer School on Digital Humanities, Zadar (Croatia) 15-19 June 2020

18 February, 2020

Welcome to the second Baladria Summer School on Digital Humanities will be held in Zadar, Croatia from 15 to 19 June 2020. This year’s novelty is two tracks: 1) Digital tools and 2) Introduction to programming.

  • Registration closes: 15 April
  • Registration fee: 200 EUR

For more information, please visit

New PhD-course in Statistical Learning 7,5 credits

21 January, 2020

The main objective with this course is to get an introduction into modern statistical methods for modeling and and prediction of data. After successfully completing the course, the student is anticipated to be able to

  • Demonstrate a conceptual understanding of the following fields in statistics: classification, resampling methods, linear model selection and regularization, and unsupervised learning.
  • Apply modern statistical software for classification, resampling methods, linear model selection and regularization, and unsupervised learning.

The course contains

  • Linear regression: simple and multiple linear regression with assessing the accuracy of the coefficients and the model and comparison with K-nearest neighbors
  • Classification: logistic regression, linear discriminant analysis, K-nearest neighbors
  • Resampling methods: cross-validation, bootstrap
  • Linear model selection and regularization: subset selection, shrinkage methods, dimension reduction methods, considerations in high dimensions
  • Unsupervised learning: Principal component analysis, clustering methods
  • Writing and presentation of a report where real data materials are analyzed with appropriate statistical approaches from the particular statistical field

Type of Instruction
Teaching consists of lectures, presentations, laboratory work, and tutoring.

The course is assessed with the grades A, B, C, D, E, Fx or F. The grade A constitutes the highest grade on the scale and the remaining grades follow in descending order where the grade E is the lowest grade on the scale that will result in a pass. The grade F means that the student’s performance is assessed as fail (i.e. received the grade F). The student’s knowledge is assessed in form of

  • Graded conceptual assignments (3 credits), grades A to F
  • Graded computer assignments (3 credits), grades A to F
  • Presentation of the use of statistics in the student’s research alternatively presentation of a statistical topic not covered in this course (1,5 credits), grading scale U-G.

Required reading
G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning: with applications in R, latest edition, Springer.
T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, latest edition.

The course will start on Fri March 27 and finish by the end of May/beginning of June. If convenient for participants it is suggested that we meet weekly on Fridays at, say 13:15.

Try to finalize the registration no later than Mar 17 so it will be easier to plan. Register here

1MA501 Probability Theory and Statistics 7,5 credits or an equivalent course in mathematics, mathematical statistics, or statistics.

If you have any questions please contact Roger Pettersson (

Seminar: “A Few Notes on Artificial Intelligence and Database Technology” – Thursday Jan. 23 at 13-14

21 January, 2020

Title: A Few Notes on Artificial Intelligence and Database Technology

Place: D1173
Time: 13-14
Date: January 23, 2020

Abstract: In this seminar I would like clarify the importance of the scientific theory functional object-types to approach reality. This theory help to evaluate knowledge representation formalisms, deep learning and data modelling and data transformations. Further, the theory, together with mathematical logic, is the foundation for the Match™ Technology Ecosystem that enables organisations to model, build no code applications and simplifying IT architectures.

About: Dr.Larry Lucardie has a background in Artificial Intelligence and Semantic Database modelling. At The Technical University of Eindhoven he graduated on a theory of complexity, functional classifications, that is fundamental to knowledge representation, deep learning and data modelling. Larry is the main architect of the Match™ AI & Data Technology Platform that is aligned to functional classifications. The Match™ platform enables organisations to design ISO compliant models of enterprise content, business fluid no-code applications, simplified IT architectures and smart internet portals.

As a Professor at the Uppsala University in Sweden, Larry lectured logic programming, knowledge and data modelling and E-business and supervised PHD students. He is founder and the current CEO of Knowledge Values and as such involved in improvement projects in the areas of value chain and process re-engineering and of process underlying technology as application development, IT architectures and data processing. Business areas: regulation management and compliance, E-commerce, financial processes products, incident management, compliance and Brexit.

Warm welcome,
Arianit Kurti, Associate Professor, Department of Computer Science and Media Technology

Keynote: Machine Learning for better entertainment recommendations: A Nordic perspective

22 November, 2019

During this years Big Data Conference at Linnaeus University on December 5-6 2019 we have several very interesting Keynote speakers, one of them is Antonina Danylenko, Head of Applied Machine Learning at The Nordic Entertainment Group who offers video-on-demand streaming, linear TV channels and radio broadcasting – probably best known for their Viaplay, Viafree & Viasat platforms.

She will talk about how the entertainment industry is transforming at a rapid rate. This is driven by new trends, growing customer expectations and AI technologies allowing for more innovation, disruption and opportunities for growth. At the same time, the industry is getting increasingly crowded – as the use of streaming services is on the rise, and the Nordic region spends more time online than ever before. Nearly four out of ten people watch video content on a daily basis, with three-quarters of the 16-24 year-old age bracket streaming that content from subscription-based services. We are seeing a new phenomenon emerge known as ‘stacking’ behaviour – where households typically subscribe to more than one service, just to keep their options open when it comes to deciding what to watch. With so many options out there, people can be paralysed by what’s known as the ‘paradox of choice.’ Personalising every aspect of the customer journey has become our main focus in the recommendation space, as consumers of entertainment have never been more spoilt for choice. Serving up relevant content recommendations at the right time is key to making the decision process as easy as possible. However, building and maintaining the lifecycle of recommender systems to capture customers’ behavior and use different algorithms to guide them towards something they will enjoy watching is not easy. In this presentation, I will outline the end-to-end process of building a recommender system utilising Big Data and Machine Learning to address this challenge.”

Don’t miss out on the opportunity to listen to him and take part of the conference by signing up here by November 25th.

More about Antonina Danylenko who holds a PhD in Computer Science from Linnaeus University, Sweden where she wrote a dissertation on the topic of “Decision Algebra: A General Approach to Learning and Using Classifiers”. After several years working at IKEA within Solution Architecture and Data Science domains , she joined the Nordic Entertainment Group—where she is now the Head of Applied Machine Learning. The Nordic Entertainment Group offers video-on-demand streaming, linear TV channels and radio broadcasting – probably best known for their Viaplay, Viafree & Viasat platforms. They’re responsible for connecting over 1.4 million subscribers to the content they love, with more than 1900 employees across the Nordics and the UK.