We are a team of professionals, able to offer innovative information technology solutions to your business. We can also offer supercomputer resources, that do not have analogues in Lithuania, various specialized software, large amount data management, analysis, modelling and simulation services, as well as expertise services. Our passion is looking for solutions, therefore, if you have an idea, we will be pleased to implement it!
We aim to:
- make supercomputer resources easily accessible
- develop grid and cloud computing technologies
- create an attractive computing and service center
We can offer scientific research and experimental development in:
- Informatics engineering
Advantages for you:
Supercomputer is a very powerful computer, which can process large amounts of information and perform calculations for modelling complex processes such as nuclear reactions and biochemical processes or even customers' habits. Using these resources scientists and business representatives can shorten calculation time many times and get more accurate results.
IT Research center was established based on Senate Resolution No. S-2010-06-30 and EU structural support project VP2-1.1-MES-04-V-01-002 "Information technology research center establishment". IT Research center operates as a research laboratory and it is focused on education, research and business.
We provide the following services:
Creation of data analysis models in HPC environment. Initiation of data analysis process; data preparation for data analysis; development and experimental evaluation of algorithms suitable for data analysis. Algorithms of data filtering, discovery of outliers, classification, clustering, and association rule mining and time series. Programs used: R, Weka, Qctave and others.
Optimization and creation of analytical algorithms in HPC environment of business process data visualization and analysis. Visual analysis of generated data for business processes applying common visualization methodologies of data analysis and processes in HPC environment. Structural, visual and descriptive analysis considering data multidimensionality and complexity of the process in applied business process environment. Programs used R,Weka, Octave, Gaussian, Gamess and others.
Creation of massive data management and analysis algorithms using cloud computing. Creation, experimental usage and evaluation of “Big data” or even massive data databases in HPC and cloud computing environments. Programs used: NoSQL, Hadoop, BigTable and others.
Development of management and analytical algorithms of sensors and stream data in HPC and cloud computing. Implementation of algorithms of constantly evolving and regularly updated data models and similarity of time series. Application of essential temporal properties in research and algorithms for sensorial and geographic data. Programs used PostgreSQL, TPR indexes, streaming DB and others.
Relocation and data analysis of business processes in cloud computing. Customization and experimental evaluation of Business Systems for transition to cloud computing environments. Consulting and installation of commercial or open-source cloud computing environment. Programs used: S3, OpenNebula, OpenStack, VMware (ESXi), Hyper-V, XenServer, KVM, OpenVZ, VirtualBox and others.
Calculations and creation of algorithms for video and audio processing. Extraction of properties for conventional (photo and video) and medical images. The development of efficient algorithms for content image search and massive processing. Programs used: FFTW, WT, POV-Ray, and others.
Expert consulting in IT area. Expert consulting on IT product properties, non-trivial solutions and their evaluation. Analysis and application of new research methods involving VU MIF researchers.
Expert consulting and training strategies in Cyber Security area. Analysis of Cyber Security issues in business process and threat assessment. Tabletop and cyber range environments involving VU MIF researchers.
More information about the supercomputer resources and opportunities find here.
1. Fill in the ordering form
All customers must fill in an ordering form* and read general conditions for using IT Research center services.
- General conditions for using IT research center services
- Ordering form
- Users' list (must be included if there will be more than 3 users per one ordering form)
* Students must provide a form signed by his/her lecturer.
2. Sign and deliver
We would be very grateful if the scientists and researchers, who have used our supercomputer resources for research or experimental purposes, would mention IT Research center in the acknowledgement of the articles that will be published in scientific and popular science journals. For your convenience we offer a couple of examples:
- “The authors are thankful for the high performance computing resources provided by the Information Technology Research Center of Vilnius University.”
- “The authors are thankful for the HPC resources provided by the IT Research Center of Vilnius University.”
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Data Protection Code of Conduct
Your personal data will be protected according to the Code of Conduct for Service Providers, a common standard for the research and higher education sector to protect your privacy.
You may contact IT OAC by email at .
03 November 2020
1. Who can use IT OAC infrastructure?
IT OAC main aim is to serve Vilnius University academic society.
We also provide services for private organisations - for that, please contact us on to agree on the size and billing details of your job.
2. How to use IT OAC infrastructure?
To gain access you have register yourself by creating an account in IT OAC portal.
For private organisations or other university representatives who cannot identify themselves with VU SSO authentication system, the accounts are created by IT OAC staff.
3. When can I start using?
Access to the resources is granted through the queue system to balance the load and access between different users.
In order to get your job in the queue, it has to be created and queued by the rules specified in the user guides.
User groups might also be assigned a priority mark which can also change the order of the queue.
4. Service providers’ rights and responsibilities
- Service providers provide users with user guides (published on the website) and additional guidance (by email, phone, in person) when needed.
- Service providers give their best to help users with user’s specific software, but cannot give any guarantees.
- Service is provided as best effort and service providers are not held responsible for any data loss, leakage or damage caused by force majeure, the user activity or inactivity or a third party whose access has become possible due to (in)activity of the user.
5. Users’ rights and responsibilities
- By default users are allowed to use IT OAC infrastructure solely for research and studies. IT OAC resources can be used for other legal activities only on previous written agreement.
- Users must use IT OAC resources in a way that doesn’t cause unnecessary waste of resources or other users work or time.
- Users must keep their login data in private. Accessing resources with someone elses login credential is strictly forbidden.
- In case of any possible security threats (e.g leakage of login credentials), user must contact IT OAC staff immediately and also give his/her best to avoid potential damage to the infrastructure.
You will find the answer to the question “Why do you need a supercomputer?” by reading the reviews of Vilnius University Faculty of Mathematics and Informatics staff, students and business partners. The team of the faculty is happy to be able to overcome the accumulated experience in solving important scientific, study and business tasks that require the use of efficient computing resources. From professor to student, from business representatives to individuals, these are the supercomputer users of the Faculty of Mathematics and Informatics who thank the faculty team for their professional advice and computational resources, and we thank them for sharing their results.
Acknowledgments from scientists
The separation or suppression of background noise in a given recording to extract clear speech benefits humans with hearing aids and devices such as Automatic Speech Recognition systems. In recent years, various experiments have been conducted to develop a Speech Enhancement (SE) model or framework that would produce high-quality estimations based on classical algorithms or Deep Neural Network techniques. This work investigates the most promising SE approaches, by replicating the experiments and using accurate training and testing criteria and the same set of evaluation metrics, to determine what produces better estimates of the clean speech signal. The authors are thankful for the high-performance computing resources provided by the Information Technology Open Access Center at the Faculty of Mathematics and Informatics of Vilnius University Information Technology Research Center. Read article.
Institute of Data Science and Digital Technologies
Dr. Povilas Treigys,
Student postgraduate Dominykas Stankevičius
Early detection of COVID-19 is of vital importance. The group of scientists’ study is aimed to automatically detect COVID-19 disease from CXR (chest radiography imaging) images while maximizing the accuracy in detection. Their proposed framework involves image enhancement technique using dataset which consists of three classes: COVID-19, viral pneumonia and normal CXR imagery. To establish the robustness of the proposed model, stratified 5-fold cross-validation is carried out. The results suggest that image enhancement, segmentation and future fusion methods used in this study could indeed improve classification results. The scientists are thankful for the high-performance computing resources provided by the Information Technology Open Access Center at the Faculty of Mathematics and Informatics of Vilnius University Information Technology Research Center. Read article.
The scientists of Vilnius University
Rising numbers and sophistication of security threats in the digital domain cause an increase in the demand for skilled cybersecurity professionals. In response, cybersecurity exercises, and in particular—cyber defence exercises (CDX) are becoming ever more popular. The goal of our research is to provide a proper methodology to optimise the exercises so that every team and each participant, including a non-technical trainee, are adequately evaluated and trained using the allocated resources most effectively. The authors are thankful for the high-performance computing resources provided by the Information Technology Open Access Center at the Faculty of Mathematics and Informatics of Vilnius University Information Technology Research Center. Read article.
The scientists of Cyber Security Lab
The group of scientists researched forecasting methods and modeling techniques which are important tools for the situations caused by COVID-19 virus. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. The paper proposes data recording and machine-learning-based analysis using data to validate country forecasts in order to predict COVID-19 proliferation trends and assess risk. The authors are thankful for the high-performance computing resources provided by the Information Technology Open Access Center at the Faculty of Mathematics and Informatics of Vilnius University Information Technology Research Center. Read article.
The scientists of
Institute of Data Science and Digital Technologies and
Institute of Applied Mathematics
Defended dissertation "Development of Tumor Microenvironment-Oriented Digital Pathology Methods for Whole Slide Image Segmentation and Classification" author is thankful for the high-performance computing resources provided by the Information Technology Open Access Center at the Faculty of Mathematics and Informatics of Vilnius University Information Technology Research Center.
Dr. Mindaugas Morkūnas
Increasing intensity in maritime traffic pushes the requirement in better prevention-oriented incident management system. The scientists research results depict that both the autoencoder-based and wild bootstrapping region prediction algorithms can predict vessel trajectory and be applied for abnormal marine traffic detection by evaluating obtained prediction region in an unsupervised manner with desired prediction probability. Information about the research can be found in this article https://www.journals.vu.lt/nonlinear-analysis/article/view/23056/23620. Researchers thankful for the Information Technology Open Access Center at the Faculty of Mathematics and Informatics of Vilnius University for the supercomputer resources provided during this research.
The scientists of
Institute of Data Science and Digital Technologies and
Institute of Applied Mathematics
Defended dissertation "Semi-supervised and Unsupervised Machine Learning Methods for Sea Traffic Anomaly Detection" author is thankful for the high-performance computing resources provided by the Information Technology Open Access Center at the Faculty of Mathematics and Informatics of Vilnius University Information Technology Research Center.
Dr. Julius Venskus
"In this research, a study of cross-linguistic speech emotion recognition is performed. For this purpose, emotional data of different languages (English, Lithuanian, German, Spanish, Serbian, and Polish) are collected, resulting in a cross-linguistic speech emotion dataset with the size of more than 10.000 emotional utterances. Despite the bi-modal character of the databases gathered, our focus is on the acoustic representation only. The assumption is that the speech audio signal carries sufficient emotional information to detect and retrieve it. Several two-dimensional acoustic feature spaces, such as cochleagrams, spectrograms, mel-cepstrograms, and fractal dimension-based space, are employed as the representations of speech emotional features. A convolutional neural network (CNN) is used as a classifier. The results show the superiority of cochleagrams over other feature spaces utilized. In the CNN-based speaker-independent cross-linguistic speech emotion recognition (SER) experiment, the accuracy of over 90% is achieved, which is close to the monolingual case of SER. Read article "
Institute of Data Science and Digital Technologies
Image and Signal Analysis Group
Feedback and thanks from doctoral students
Glaucoma is the second eye disease causing blindness worldwide. Optic Cup-to-Disc ratio (CDR) is a commonly applied method in glaucoma detection. The CDR is calculated based on Optic Disc (OD) and Optic Cup (OC) in eye fundus image screening. Therefore, the accurate segmentation of these two parameters is very important. Lately, Deep Neural Networks have demonstrated great effort in automated Optic Disc and Optic Cup segmentation but the overlapping between regions of OC and OD cause the challenge to obtain CDR automatically with high accuracy. In this paper, we assess the performance of CDR evaluation on three modifications of the Convolutional Neural Network (CNN) U-Net, namely Attention U-Net, Residual Attention U-Net (RAUNet), and U-Net++ applied on publicly available datasets RIM-ONE, DRISHTI, and REFUGE. We calculated the ground truth CDR value of testing eye fundus images of these datasets and compared it with the CDR value obtained by trained CNNs. Our results show that Attention U-net obtains the closest CDR to the ground truth CDR value but the identification of early-stage glaucoma needs an improvement. The authors of the artice "Deep Neural Networks application for Cup-to-Disc ratio estimation in eye fundus images" would like to thank the IT Research Center of Vilnius University for the provided HPC resources.
"At the Institute of Data Science and Digital Technologies, VU MIF's HPC service infrastructure was used for doctoral studies. The goal of my dissertation research is to investigate deep recurrent neural networks for monitoring the state of sea traffic - predicting the ship's progress. VU MIF's supercomputer resources with GPU calculations were used to create artificial intelligence-based models that solve the regression task. The models created from historical big AIS (Automatic Identification System) data are able to predict the ship's trajectory into the future and this allows to assess the collision risks of ship traffic and detect anomalies at sea. As artificial neural networks are not deterministic, the experiments were repeated several times to ensure the robustness of the results, which was well achieved with the available MIF HPC resources, which can be easily accessed and computed. A great recognition of all this work is the publication of a scientific paper in a highly-ranked EAAI journal, in which the results of the study are presented."
“We used the VU MIF supercomputer for large-scale computations to model chemical processes in electrochemical microscopy. The results have been published in high-level application journals. "
“MIF supercomputer resources are useful in interdisciplinary research with scientists at the VU GMC Institute of Biochemistry. The subject of these investigations is the electrochemical properties of damaged (defective) phospholipid membranes. The supercomputer analyzes a large number of three-dimensional models of these membranes using the finite element method (FEM). This simulation avoids the complexity of working with real membranes in the laboratory and allows you to obtain the required information on the properties of the membranes being studied more quickly."
“At VU MIF Supercomputer, we are conducting research on algorithms for electroencephalogram (EEG) analysis. A supercomputer is especially useful for such research because we can test many algorithm configurations at a time and further explore the best one. Other algorithms we can parallelize, then we get acceleration on the supercomputer. We are currently investigating algorithms for EEG peak search and classification based on diagnosis, and apply the results to writing a doctoral dissertation, preparing scientific publications, and giving presentations at international and national scientific conferences. ”
A.V. Misiukas Misiūnas
Acknowledgments from the final theses
The graduate of the VU Faculty of Medicine is thankful for the provided IT resources by the ITAPC team. She managed to defend her master's thesis on the topic "Investigation of eye fundus image quality on vascular segmentation using deep neural networks" successfully.
Successfully defended bachelor thesis "An investigation of semi-supervised training methods for facial feature prediction" had been written by the author, who has graduated the "Information Systems Engineering" bachelor's studies. He is grateful for the opportunity to use ITAPC supercomputer for study purposes. The aim of the thesis research was to analyse and create semi-supervised learning method which would produce valuable information about unlabelled data and help during the training phase.
The graduate, who has successfully completed the VU MIF bachelor's study program "Econometrics" and defended her bachelor's thesis on the topic "Assessing the popularity of youtube videos using publicly available metadata", thanks the Information Technology Open Access Research Center for resources. This paper analyses the video metadata and derived parameters collected by the YouTube Data Application Programming Interface. After assessing significant differences in means among popularity groups by using statistical tests, data clustering algorithms: k-means and self-organising neural networks were applied, to search for trends in the formation of popularity groups. Classification of video popularity was also performed using three machine learning methods: support vector machine, random forest and multinomial logistic regression classifiers. A comparative analysis of the algorithms was carried out and the feature sets that most accurately classify the popularity level were selected. The aim of the final work was to contribute to the development of exception identification algorithms that are able to efficiently process traffic data.
The graduate, who has successfully completed the VU MIF master's study program "Modelling and Data Analysis" and defended his master's thesis on the topic "Outlier detection in multidimensional streaming data", thanks the Information Technology Open Access Research Center. The aim of the final work was to contribute to the development of exception identification algorithms that are able to efficiently process traffic data. VU MIF IT OARC supercomputer resources were used for the research of the master's thesis.
Business and other reviews
The company ZIVE has developed an electrocardiogram device that can be worn on a person's chest and used at home during sports or any other daily activities. Heart activity can be monitored 24x7. When connected to a smartphone or tablet, the app displays detailed heart activity data in real time. After analyzing the data with an AI model, early diagnosis of heart disease can be made, thus providing added value to the user. With the help of MIF scientists and usage of VU MIF HPC resources the AI algorithm was extended with new parameters and their impact on the results was investigated. The developed AI solution is being deployed in medical technology, in collaboration between business and science, using the latest high-performance computing techniques to bring early cardiac diagnostics to the user. Read more
At the Faculty of Mathematics and Informatics of Vilnius University, with the help of HPC resources, Novian Technologies performed the design of another supercomputer for solving the problems of hydrometeorology in one of the 10 largest countries in the world. Based on the computational tasks provided by the customer, Novian Technologies has designed an individual HPC infrastructure (computing nodes, storage, network bandwidth, etc.) capable of solving specific hydrometeorological / climate change challenges in a given time. We are pleased to be able to present the customer with highly accurate results from calculations performed in a real-world HPC environment and using real-world computing power rather than assumptions. Accurate calculation results allowed the customer to safely plan the required capacity of equipment without a large margin, and thus save money.
Ltd. Novian Technologies
“The IT Research center team is characterized by promptness and goodwill. Effective communication allows for productive time planning, which is especially important during events, and goodwill collaboration ensures the quality of our organization's goals. ”
PI "Turing School"
"It's easy to work with the IT Research center team. There are rules in mature business where a verbal agreement equates to a written commitment. This way of communicating allows results to be achieved very quickly without bursting into the bureaucratic jungle."