Category: Employee (student, worker)

The Two Wheels cycling

The Two Wheels cycling

The Two Wheels cycling is an annual event organised by the University of Bielsko-Biala, and in particular by the scientific group ‘Reset’, whose supervisor  is Dr. inż  Tomasz Gancarczyk.

The rally is open to university employees as well as students and guests.

Participants cycle along a pre-planned route.

One of the attractions of the rally is the prize draw. The main prize is a bicycle, which is drawn among the participants.

Pictured below presents the Project Team ready for the Two Wheels cycling.

Erasmus+ project FAAI: 16th Edition of the International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS’2023) in Niš (Serbia)

Erasmus+ project FAAI: 16th Edition of the International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS’2023) in Niš (Serbia)

The 16th Edition of the International Conference on Advanced Technologies, Systems, and Services in Telecommunications (TELSIKS’2023) unfolded at the distinguished Faculty of Electronic Engineering (FEE), University of Nis, Serbia, from October 25-26, 2023. This conference marked a significant milestone in the exploration of cutting-edge technologies and services in the field of telecommunications.
The FAAI project consortium was represented by delegations from University of Bielsko-Biala (UBB), University of Library Studies and IT (ULSIT), University of Nis (UNI), University of Ss. Cyril and Methodius in Trnava (UCMT), and University of Montenegro (UoM), collectively showcasing the collaborative efforts and expertise of these esteemed institutions.
Opening Ceremony and Plenary Session: A Glimpse of Our Delegation’s Engagements
Before delving into the special session on Applied Artificial Intelligence, our delegations had the privilege of participating in the distinguished Opening Ceremony and Plenary Session of TELSIKS’2023.

The Opening Ceremony, held in Room 457, FEE, was a momentous occasion marked by an atmosphere of camaraderie and shared purpose. It provided a fitting commencement to the conference, setting the stage for the intellectually stimulating interactions that followed.

Following this, the Plenary Session, also convened in Room 457, FEE, showcased a lineup of eminent speakers who expounded upon the latest trends and advancements in telecommunications. Our delegations actively engaged in these enriching discussions, contributing to the collective discourse on the future of the field.


Special Session: Setting up the Applied Artificial Intelligence Learning Requirements (SS3)

A significant highlight of the conference was the special session on Applied Artificial Intelligence (AI) and learning environments. This session delved into the development of a competency framework for Applied AI, encapsulating technical, business, and ethical perspectives, in alignment with the Erasmus+ project FAAI No. 2022-1-PL01-KA220-HED-000088359.
Chaired by Vasyl Martsenyuk and Olivera Pronić-Rancic, the session played a pivotal role in fostering dynamic discussions and facilitating the exchange of innovative ideas on Applied AI.

Noteworthy Talks
1. “On Emerging Methodology for Collection of Good Practices in Applied Artificial Intelligence”
• Vasyl Martsenyuk, George Dimitrov, Dejan Rancic, Iveta Dirgova Luptakova, Igor Jovancevic, Aleksandra Klos-Witkowska, Marcin Bernas, and Tomasz Gancarczyk.
2. “Research and Analysis of IT Specifications of Good Practices in Artificial Intelligence”
• Paulina Tsvetkova, George Dimitrov, Vasyl Martsenyuk, Iveta Dirgová-Luptáková, Dejan Rancic, Igor Jovancevic, Iva Kostadinova, Katia Rasheva-Yordanova, Pepa Petrova, and Pavel Petrov.
3. “Research and Analysis of Different Real Cases, with use of AAI”
• Iva Kostadinova, George Dimitrov, Vasyl Martsenyuk, Dejan Rancic, Iveta Dirgova-Luptakova, Igor Jovancevic, Ivan Trenchev, Stefka Toleva-Stoimenova, and Pavel Petrov.

4. “Employer Requirements for Graduate Competencies in Applied Artificial Intelligence”
• Dragan Stojanovic, Olivera Pronić-Rancic, Natalija Stojanovic, Dejan Rancic, and Marko Milojkovic.

5. “Research and Analysis on the Labor Market in the Field of Applied AI”
• Aleksandar Plamenac, Igor Jovancevic, Georgi Dimitrov, Vasyl Martsenyuk, Dejan Rancic, and Iveta Dirgova-Luptakova.
6. “On Predicting Financial Time Series of Various Granularity as an Applied AI Problem”
• Vasyl Martsenyuk and Jacek Kafel-Kania.
7. “On Manufacturing Network Design as an Applied AI Problem”
• Vasyl Martsenyuk and Nataliia Kit.
Engaging Discourse and Valuable Insights
The ensuing discussions were characterized by a palpable enthusiasm and a profound appreciation for the depth of knowledge shared. The questions posed were astute, and the insights imparted were instrumental in augmenting our collective understanding.

Looking Ahead
The conference culminated as a resounding success, serving as an exemplary platform for the dissemination of the FAAI project’s findings. The strides made in the domain of Applied AI are nothing short of commendable, and we remain sanguine about the transformative potential of our collaborative endeavors.
We extend our gratitude to all participants for their invaluable contributions, and we eagerly anticipate the continued discourse on these vital topics.

Article: On Manufacturing Network Design as an Applied AI Problem

Article: On Manufacturing Network Design as an Applied AI Problem

The work is devoted to designing a manufacturing network incorporating logistic-production sites that are located at the nodes of the squared lattice with the help of the AI technique. We focused on qualitative analysis of the dynamic behavior of the dynamic lattice model. The model includes rate constants and initial conditions affecting the trajectories of the model which can be classified either as a stable node, limit cycle, or chaotic attractor. We aim to solve the problem of the model qualitative behavior as an AI classification problem. The training dataset is constructed with the help of Monte-Carlo simulation with high-performance computing in Julia. The AI model is built as a C5.0 decision tree. The work was fulfilled with the framework of Erasmus+ Project No. 2022-1-PL01- KA220-HED000088359 entitled “The Future is in Applied Artificial Intelligence” (FAAI) and offers a use case to be studied during the applied AI training course.

Full paper can be found at:https://ieeexplore.ieee.org/document/10316176

Audio version is available:

Article: On Predicting Financial Time Series of Various Granularity as an Applied AI Problem

Article: On Predicting Financial Time Series of Various Granularity as an Applied AI Problem

We comprehensively examine the efficacy of LSTM models in predicting financial time series. We evaluate the performance of LSTM networks based on various numbers of units determined by temporal granularity, considering aspects such as prediction accuracy. This study contributes to the ongoing discourse on the role of AI in financial markets, offeringa nuanced perspective on the practicality and limitations of LSTM models in this critical domain.

Full paper can be found at: https://ieeexplore.ieee.org/document/10316038

Audio version is available:

Article: Research and Analysis on the Labor Market in the Field of Applied AI

Article: Research and Analysis on the Labor Market in the Field of Applied AI

This article is fulfilled within the framework of Erasmus+ project “The Future is in Applied Artificial Intelligence (FAAI). It gives overview of current job market related to the field of Applied Artificial Intelligence. The data is obtained from online survey, and it gives highlights of severalaspects of labor market divided into research and analysis of the market, and specific requirements necessary. Regarding research and analysis, the data provided deals with:

– positions offered in the market.

– machine learning problems occurring.

– models being developed while resolving the realworld problem.

– machine learning tasks to be solved.

The collected data in the domain of job market requirements gives highlight about:

– required programming languages.

– educational requirements.

– required competencies.

Results given can serve as a guide to which competencies are necessary in the field of AAI and provide information for both professionals and curriculum creators.

Full paper can be found at: https://ieeexplore.ieee.org/document/10316155

Audio version is available:

Article: Employer Requirements for Graduate Competencies in Applied Artificial Intelligence

Article: Employer Requirements for Graduate Competencies in Applied Artificial Intelligence

ERASMUS+ project The Future is in Applied Artificial Intelligence (FAAI) aims to increase the quality and relevance of students’ and graduates’ knowledge and skills in AI/ML-specific topics based on skills needed in the labor market. This paper presents the results of the survey that was conducted in the context of the FAAI project to assess the needs of employers in project participants’ countries in graduates’ competencies in Artificial Intelligence, Machine Learning, and Data Science in general for the purpose of training specialists in the field of Applied AI. The survey was filled in by 38 companies and consisted of 31 questions related to general required competencies, type of machine learning problems solved, AI libraries used in companies, required soft skills, employers’ satisfaction with the level of preparedness of master’s degree graduates in the field of AI.

Full paper can be found at: https://ieeexplore.ieee.org/document/10315726

Audio version is available:

Article: Research and Analysis of Different Real Cases, with use AAI

Article: Research and Analysis of Different Real Cases, with use AAI

This article is fulfilled within the framework of Erasmus+ project “The Future is in Applied Artificial Intelligence” (FAAI) and examines the study of practical solutions implemented using applied artificial intelligence. The research was done by preparing an online survey containing a total of 7 questions, open and closed. The purpose of the study is to find real working applications of applied artificial intelligence projects, describe their application in what field, and record the name of the projects found to describe their activity. The study was done by looking at cases all over the world. The analysis of the data provides insight in several directions: – in which countries are more real cases of artificial intelligence solutions used – what is the distribution of realized cases – depending on whether the country is a member of the EU or not EU. – In what category is the real case developed. – whether the country of the real case works in collaboration with other countries or implements the real case only the country. The research and analysis done provide a clear picture of the developed projects using artificial intelligence. The obtained results will guide in what areas to organize the practical training. Also, the research would help future AI application developers.

The full paper can be found at: https://ieeexplore.ieee.org/document/10316177

Audio version is available:

Article: Research and Analysis of IT Specifications of Good Practices in the Area of Artificial Intelligence

Article: Research and Analysis of IT Specifications of Good Practices in the Area of Artificial Intelligence

This article is a contribution within the Erasmus+ project titled “The Future Lies in Applied Artificial Intelligence(FAAI) and examines research of collected IT specifications of good practices in Area of Artificial Intelligence (AAI). The article describes research conducted, the purpose of which is to find IT specifications of good practices in AI and describe their characteristics, like an area of implementation of the AI solution, the result of processing the data, the source of data, Data processing, and quality, what tools are used for processing data, and others. AAI application cases and the technologies used for implementation are reviewed. The specifics of the data and the applications used are described. The examination of these technologies will provide insight into which ones are favored and provide an overview of what is commonly referred to as “best practices” in this particular domain.The research encompassed a global examination of cases. The analysis of the data offers valuable insights in various directions:

 Application area of ML/AI

 Type of machine learning problems in described good

practices in Artificial Intelligence

 Type of models were developed within the projects

 What is the area of implementation of AI solution

 Used AI libraries (frameworks).

 Source of data

 Data characteristics

 Tools are used to store data

 What platform solution is used

 What type of storage is used.

 

The full paper can be found at: https://ieeexplore.ieee.org/document/10316145

Audio version is available:

Article: On Emerging Methodology for Collection of Good Practices in the Area of Applied Artificial Intelligence

Article: On Emerging Methodology for Collection of Good Practices in the Area of Applied Artificial Intelligence

The work is fulfilled within the framework of Erasmus+ project “The Future is in Applied Artificial Intelligence” (FAAI) and devoted to the development the methodology for collecting and analyzing good practices in the field of applied artificial intelligence (AAI) regarding the competences, training, existing solutions and real cases, which can be used for developing training courses of competence based education. Here we propose the definition of good practice in the field of AAI together with the corresponding criteria and features. The offered methodology uses system research based on the data gathered from existing training courses in AAI, labor market, surveys filled in by academics, students and employers, AAI use cases in science and industry.

The full paper can be found at: https://ieeexplore.ieee.org/document/10316104

Audio version is available:

Article: Mathematical and Computer Simulation of the Response of a Potentiometric Biosensor for the Determination of α-сhaconine​

Article: Mathematical and Computer Simulation of the Response of a Potentiometric Biosensor for the Determination of α-сhaconine​

The article is devoted to the problem of developing a mathematical model of the response of a potentiometric biosensor for the determination of α-chaconine in the form of a system of seven differential equations that describe the dynamics of biochemical reactions during the full cycle of α-chaconine concentration measurement. At the same time, each of the differential equations establishes the concentration dependence of substrate, enzyme, inhibitor, enzyme-substrate, product, enzyme-inhibitor, enzyme-substrate-inhibitor complexes as a function of time. The mathematical model of the biosensor for the determination of α-chaconine was solved numerically in the R package. The input parameters of the system were used, namely, the concentrations of the enzyme, substrate, and inhibitor (5.8×10-4 M butyrylcholinesterase, 1×10-3 M butyrylcholine chloride, and 1×10−6; 2×10−6; 5×10−6; 10×10−6 M of α-chaconine, respectively), which are measured during experiments. To verify the model and compare it with the experimental response a potentiometric biosensor based on immobilized butyrylcholine chloride was used. Selection of direct and inverse rate constants of enzymatic reactions was carried out in such a way that the result of numerical modeling corresponded as much as possible to the experimental response of the studied biosensor. A comparative analysis of the experimental and simulated responses of the biosensor for the determination of αchaconine was established. It was found that the absolute error does not exceed 0.045 units. As a result of computer simullation, it was concluded that the developed kinetic model of the potentiometric biosensor makes it possible to identify all the main components that were measured this study.

The full paper can be found here: https://ceur-ws.org/Vol-3468/paper1.pdf

The audio version is available: