At the panel discussion called “Strategic planning of digitization of higher education in Montenegro in the post-COVID-19 era” in Chamber of Commerce of Montenegro, the project EuroCC2 – National Competence Centers for HPC, which has started with its implementation was presented today.
Dr. Sandra Tinaj presenting the EuroCC2 in the Chamber of Commerce of Montenegro
Representing the University of Donja Gorica and NCC Montenegro, the general manager of UDG Assoc. Dr. Sandra Tinaj, who was one of the panelists, along with representatives of other higher education institutions in Montenegro, addressed the present audience. She pointed out that the EuroCC2 project is of great importance precisely in the field of digitization of higher education, but also invited all interested members of the general public to contact the HPC Montenegro Center in case of providing additional information and support – in order to raise awareness of the importance of this project, but also the opportunities that it can provide to our community in general.
The panel was attended by representatives from industry, academia and public sector
In the period from 15th – 18th of February the 27th international scientific and professional conference “INFORMATION TECHNOLOGIES 2023” (IEEE IT 2023) will traditionally be hosted in Zabljak. The conference will be held in the organization of: University of Montenegro – Faculty of Electrical Engineering, University of Donja Gorica – Faculty of Information Systems and Technologies, IT Society Montenegro, University of Belgrade – Faculty of Organizational Sciences, IEEE Association and IEEE Section for Serbia and Montenegro, with full support of the company Cikom from Podgorica. The chairman of the conference is prof. dr Bozo Krstajic from the Faculty of Electrical Engineering, University of Montenegro.
IT2023 – click on image to open website
Lectures and round table discussions will be organized at the Conference on trends in the development of information and communication technologies in the world, as well as current events in this field in Montenegro. Special session dedicated to project presentations will include EuroCC2 project – National Competence Centres for HPC, two H2020-MSCA projects TRACEWINDU and ODDEA, H2020 projects NI4OS-Europe and DEMETER, Eureka TRADE, Erasmus DigNest, and others.
The conference will include: a short course for Edge AI in IoT solutions (organzed by ANSO and EuroCC Montenegro), an HPC/AI workshop with applications, a presentation of the European cloud of open science and the results of successful international projects. This year, a student workshop will also be held as part of the DigNEST project, which will focus on IT innovations in the field of health and agriculture. In addition to the above mentioned, the papers that have arrived and refer to topics from these areas will also be presented at the Conference.
Click to watch
All those interested can follow the activities at the Conference online as well, through the video conference access links available on the official website of the Conference (link).
The University of Donja Gorica (UDG), EuroCC Montenegro (national competence center for supercomputers) in cooperation with the National Center of Spain (NCC Spain) organized the course “Deep Learning and HPC”. Guest lecturers from renowned institutions such as Petar Veličković from the University of Cambridge and DeepMind (Introduction to Graph Neural Network), Borja Pavon from Universidad de Cantabria and Barcelona Supercomputing Centre (High-Performance Computing systems), Sergio Perez from Graphcore and Imperial College London (Making new AI breakthroughs with Graphcore IPU), Itana Bulatović from AI Clearing and Shanghai Jiao Tong University China (Deep Learning for Computer Vision), Andreea Deac from Montreal Institute for Learning Algorithms (Deep Reinforcement Learning), Aleksa Šuković from Max Planck Institute (Advanced NLP), Nikola Bulatović from University of Montenegro and Uhura (Introduction to NLP), were involved in the implementation of the training. These classes are organized online for all interested participants.
During this training, we had 30 to 35 active participants from the start to the end of the course. The first and last classes were organized by teachers from UDG and NCC Montenegro (Stevan Čakić and Stevan Šandi) in person for students from UDG (Introduction to Deep Learning, Deep Learning for HPC, Deep Learning with Business and Ethics in AI). The course was mandatory for students of the master’s program Artificial Intelligence as an integral part of the course Deep Learning.
HPC systemsMaking new AI breakthroughs with Graphcore IPU Deep Learning and Computer VisionIntroductionto GNNDeep Reinforcement LearningIntroduction to NLP Advanced NLP
Parallel Programming training course was organised by UDG and HPC NCC Montenegro in cooperation with NCC Germany, from 8th November to 14th of December 2022. The training was dedicated both to companies interested in the parallel programming skills and to students eager to learn on theoretical basis and practical features of parallel computing, with 54 attendees registered in total. Program course covered: Concepts of parallel computers – purpose, architecture, division; Practical guidelines for the development of parallel programs based on the architecture of shared and distributed memory as well as on the hybrid model; Analysis of the performance of parallel programs including decomposition of serial program and transformation into parallel programs. Beside the theoretical part, the training also included practical examples, use cases and hands-on exercises that allowed participants to apply and test their parallel programming knowledge on supercomputing systems/HPC-working environment. Participants learned to identify parallelization problem, analyse parallel programs complexity and efficiency, and develop simple parallel program, with dedicated support of academic professors and HPC experts dr Luka Filipovic from NCC Montenegro and lecturers from The Leibniz Computing Center, NCC Germany.
After the training, survey forms were sent out to regular participants, revealing interesting statistics on training activity, industry appeal and further expectations.
Regarding academic participants, this was an obligatory course for students of the MSc program Artificial Intelligence, but also 1/3 of students came from Engineering and IT faculty level programs. Regarding industry participants, the majority were coming from the ICT sector, with working experience up to 5 years (but also 38% with 15+ years).
Decision to attend Parallel Programming course was dominantly influenced by: 1) personal interest in developing parallel computing skills, 2) possibility to obtain practical experience and 3) engagement of international and experienced lecturers.
With regard to level of complexity, the majority of participants considered the program course demanding, but successfully managed. With regard to teaching program and course organisation, 65% and 71% of participants evaluated them with the highest grade, respectively. With regard to communication with lecturers, knowledgeable answers and useful consultations were highly appreciated. 86% of surveyed participants confirmed that the training course fully or mostly met their expectations.
Being asked what they liked the most about the course, participants stated: hands-on approach, practical examples, expertise of lecturers and open source application used by NCC in Germany. And when it comes to possible improvements, focus was on providing more practical examples and tasks. Over 70% participants would be further interested in Artificial Intelligence, Machine Learning and Deep Learning training opportunities, and all industry representatives confirmed interest in potential cooperation with UDG on project activities.
The general aim of the training course was to increase the parallel programming skills in Montenegro, but also to promote EuroCC projects and supercomputing resources, and to encourage HPC-based project ideas and partnerships.
Sanja Nikolic, representative of HPC NCC Montenegro participated at 3rdEUROCC/CASTIEL global conference, organized with an aim of presenting of main results, key achievements and NCCs successful deliverables within EuroCC1 project phase.
EUROCC/CASTIEL representatives presented key highlights of EUROCC 1 project management with regard to Competence Map building; Training/Mentoring /Twinning activities; Industrial interaction support and Awareness Creation events and actions.
In the second part of the conference NCCs Finland, Luxembourg, Montenegro, Slovenia, Sweden and Turkey presented key achievements and EUROCC1 contributions in their respective working packages and selected project activities. NCC Montenegro presented multiple activities and overall results in the project segment related to HPC/HPDA/AI Trainings and Skills development.
A hosting agreement has been signed between the EuroHPC Joint Undertaking and the National Infrastructures for Research and Technology (GRNET) in Greece where DAEDALUS, a new EuroHPC supercomputer will be located. University of Donja Gorica is one of the partners in the implementation of the project.
Once procured, DAEDALUS will be a mid-range supercomputer, able to perform more than 30 petaflops or 30 million billion calculations per second. This new supercomputer will be managed and operated by GRNET, the National Infrastructures for Research and Technology in Greece. DAEDALUS will be installed in the historical 19th century Electric Power Station building in Lavrion Technological and Cultural Park and is expected to have an overall surface area of 1500 m2.
The supercomputer will power new applications in a wide range of areas, such as engineering, chemistry, health sciences and will be used to visualise and solve scientific problems. It will advance science and boost the innovation potential of enterprises while improving the European citizens’ quality of life.
DAEDALUS, at a total acquisition cost of EUR 33 million, will be co-funded with a maximum total budget of up to EUR 10 million by the EuroHPC JU (35%) and Greece (65% of national funds). Cyprus, Montenegro and North Macedonia are also involved in the project as members of the DAEDALUS consortium. This partnership is strategic for the European Union as it will support science and technology in the wider area of South-East Europe.
DAEDALUS Partners:
GRNET – National Infrastructures for Research and Technology, Greece Owner, operator, manager, coordinator
The Cyprus Institute, Cyprus: Participation in high-level support team
University of Donja Gorica, Montenegro: Participation in high-level support team
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje Participation in high-level support team
Researchers from UDG and NCC Montenegro presented a scientific paper at the 2022 International Arab Conference on Information Technology (ACIT). The conference took place at the Al Ain University – Abu Dhabi Campus on November 22-24, 2022. The paper “Overcoming Limitations of Statistical Methods with Artificial Neural Networks” was authored by M. Grebovic, L. Filipovic, I. Katnic, M. Vukotic, and T. Popovic. More information about the conference is available here. The paper is available at IEEE Xplore at the following link.
ABSTRACT – Traditional statistical models as tools for summarizing patterns and regularities in observed data can be used for making predictions. However, statistical prediction models contain small number of important predictors, which means limited informative capability. Also, predictive statistical models that provide some type of pseudo-correct regular statistical patterns, are used without previous understanding of the used data causality. Machine Learning (ML) algorithms as area in Artificial Intelligence (AI) provide the ability to interpret and understand data in more sophisticated way. Artificial Neural Networks as kind of ML methods use non-linear algorithms, considering links and associations between parameters, while statistical use one-step-ahead linear processes to improve only short-term prediction’s accuracy by minimizing cost function. Disregarding that designing an optimal artificial neural network is very complex process, they are considered as potential solution for overcoming main flaws of statistical prediction models. However, they will not automatically improve predictions accuracy, so several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications. Based on gained results, couple of techniques for improving artificial neural networks are proposed to get better accuracy results than statistical predictive methods.
