Call for presentations, Big Data Conference 2020

23 October, 2020

A fast-forward (FF) + virtual poster (VP) sessions will be organized as part of the Big Data Conference 2020. In the FF presentations, each participant gets to show a 3-minute video to briefly summarize her/his research. Directly after the FF,  participants will be redirected to breakout rooms where it will be possible to present their VP and interact with the interested public.

The FF+VP presentations can focus on either ongoing research or new ideas:

1) Ongoing research will focus on research recently published or at an advanced stage of elaboration. The main goal here is to present research results of general interest for the public of the conference and eventually receive feedback on ongoing work.

2) New ideas will focus on future research, plans, or simply new ideas. The goal here is to share with the public their own plans, receive feedback, find partners and possibly find synergies to develop future research together.

 Submission

To submit to the FF+VP session, participants should submit a 500-word abstract briefly presenting the research by November 9th, 2020 to Diana Unander, diana.unander@lnu.se  Each participant can submit at most two abstracts.

Acceptance information will be sent out by November 13th, 2020.

If accepted, a video (in videos in 720p and mp4 format) of maximum 3 minutes should be sent by November 24th, 2020.

For more information or questions, please contact:

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.

https://bit.ly/2RXhkY0

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: https://www.sciencedirect.com/science/article/pii/S0167819119301759?via%3Dihub

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

Programme:
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 koraljka.golub@lnu.se if you plan to attend, by Monday 23 March. Thank you!

Welcome!

For more information contact:
Koraljka Golub
Professor
Head of the iInstitute
Digital Humanities Initiative Co-Leader
Linnaeus University
http://lnu.se/personal/koraljka.golub

PhD course INNOVATIVE APPLICATIONS OF RESEARCH AND SCIENCE (4.5 credits)

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 fyrklovern.doktorand@kau.se 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 https://libra.sae.kau.se/course/att-nyttiggora-forskningoch-vetenskapinnovative-applications-research-andscience

For more information contact: Martina Lago, Innovation Advisor, martina.lago@lnu.se 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 http://baladria.unizd.hr

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.

Examination
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.

Timetable
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.

Registration
Try to finalize the registration no later than Mar 17 so it will be easier to plan. Register here https://forms.gle/a3zzgG5toouMFqYb7

Prerequisites
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 (https://lnu.se/personal/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