Category: dissemination

Report on dissemination activities

Report on dissemination activities

Below you can analyze the summary of the dissemination actions per project partners.

The summary was prepared in AdminProject platform.

In total, there are 93 actions engaging 6826 participants.

UBB (docx) – The Future is in Applied Artificial Intelligence_DISSEMINATION_P1_(UBB) (1)

UBB (xlsx) – The Future is in Applied Artificial Intelligence_DISSEMINATION_P1_(UBB)

ULSIT (docx) – The Future is in Applied Artificial Intelligence_DISSEMINATION_P2_(ULSIT)

 

A4.6: Publication “Designing a Competency-Focused Course on Applied AI Based on Advanced System Research on Business Requirements”

A4.6: Publication “Designing a Competency-Focused Course on Applied AI Based on Advanced System Research on Business Requirements”

The publication disseminating WP4 has been published in Applied Sciences.

Below is the link

https://www.mdpi.com/2076-3417/14/10/4107

Abstract

The consortium of “The Future is in Applied Artificial Intelligence” Project designed the first competency-based applied artificial intelligence curriculum at the higher-education institution level. The development was based on advanced system research on existing artificial intelligence-related resources and surveying target groups of teachers, information technology students, and employers, which should enhance the performance of implementing artificial intelligence education. A review of applied artificial intelligence was prepared in the form of keyword clustering. The initial data were collected with the help of surveying by identifying job offers, existing artificial intelligence training courses, scientific projects, and real cases. A synthetic analysis of the textual information from the studies was conducted using the word clouds technique. A tensor-based approach was used for the presentation of the competency-based course. The specific numerical requirements for the course in the form of priorities followed from the solution to decision-making problems using the analytic hierarchy process technique. Based on a comprehensive study of surveys, educational experience, scientific projects, and business requirements, and a meta-analysis of the recent references, we specified the criteria for a training course in the form of a tensor-based representation of competencies in relation to content and educational modules.

 

A5.6: Publication on advanced neural network models

A5.6: Publication on advanced neural network models

The work has been published in a top-ranked journal “IEEE Transactions on Neural Networks and Learning Systems”

The link is below

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10479223

 

The title: On Model of Recurrent Neural Network on a Time Scale: Exponential Convergence and Stability Research

Abstract:

The majority of the results on modeling recurrent neural networks (RNNs) are obtained using delayed differential equations, which imply continuous time representation. On the other hand, these models must be discrete in time, given their practical implementation in computer systems, requiring their versatile utilization across arbitrary time scales. Hence, the goal of this research is to model and investigate the architecture design of a delayed RNN using delayed differential equations on a time scale. Internal memory can be utilized to describe the calculation of the future states using discrete and distributed delays, which is a representation of the deep learning architecture for artificial RNNs. We focus on qualitative behavior and stability study of the system. Special attention is paid to taking into account the effect of the time-scale parameters on neural network dynamics. Here, we delve into the exploration of exponential stability in RNN models on a time scale that incorporates multiple discrete and distributed delays. Two approaches for constructing exponential estimates, including the Hilger and the usual exponential functions, are considered and compared. The Lyapunov–Krasovskii (L–K) functional method is employed to study stability on a time scale in both cases. The established stability criteria, resulting in an exponential-like estimate, utilizes a tuple of positive definite matrices, decay rate, and graininess of the time scale. The models of RNNs for the two-neuron network with four discrete and distributed delays, as well as the ring lattice delayed network of seven identical neurons, are numerically investigated. The results indicate how the time scale (graininess) and model characteristics (weights) influence the qualitative behavior, leading to a transition from stable focus to quasiperiodic limit cycles.

This work was supported in part by the Erasmus + Program for Education of the European Union through the Key Action 2 Grant (the Future Is in Applied Artificial Intelligence) under Grant 2022-1-PL01-KA220-HED000088359 (work package 5: “Piloting,” activity A5.6 “Project deliverables”)

A5.7: Final multiplier event

A5.7: Final multiplier event

This year’s 12th edition of the Beskid Academic Day conference, organized by the RESET scientific club operating at the University of Bielsko-Bialski, was devoted to topics related to Artificial Intelligence. The event has also become an official event of  The Future Is In Applied Artificial Intelligence project, No. 2022-1-PL01-KA220-HED-000088359. The agenda, which included nine AI-related lectures, included the following topics:

1. Inaugural lecture – The Future is in Applied Artificial Intelligence: the outputs of the project FAAI.  – prof. dr hab. Vasyl Martsenyuk – UBB
2. AI, a threat or… Is it high time to change jobs? Arcadiusz Benedict – Pitney Bowes.
3. A brief history of money. Will AI change the monetary system? Roman Majewski Crypto Mines.
4. When will AI create a video game? Artur Loska – Fool’s Theory
5. Malware vs antimalware. Michał Komendera UBB
6. The role of Cryptocurrency Miners in building decentralized AI. Konrad Duszyński – Crypto Mines.
7. Does IT have a future in Podbeskidzie? Wojciech Bachta – Da Vinci Studio.
8. Artificial intelligence and game design. Marcin Bernaś – UBB
9. Copyright and artificial intelligence through the eyes of a lawyer. Marek Nowicki – law firm.

 

The conference was officially inaugurated by the Rector of the University of Bielsko-Biala, Prof. Jacek Nowakowski. He familiarized the participants with the structure of the university, individual faculties and faculties. In his speech, he drew attention to the accelerating development of artificial intelligence and its rapidly growing capabilities. Finally, he wished all participants of the conference fruitful and knowledgeable lectures.

During the inaugural lecture, Prof. Vasyl Martsenyuk presented the current state of use of artificial intelligence in various areas of life and familiarized the conference participants with the main assumptions and goals of the FAAI project.

During the lecture entitled Artificial Intelligence and Game Design, Dr. Marcin Bernaś presented the student projects related to AI implemented at UBB and talked about the practical applications of artificial intelligence in game design based on the FAAI project materials on the real applications of AI in IT companies in Europe.

During the conference – in the lobby in front of the conference room – IT companies using AI in their IT products set up their stands:

  1. SI Record
  2. Vattenfall from Poland
  3. Pitney Bowes
  4. Regional Development Agency
  5. Infonet Project
  6. NEXT!

 

 

 

 

 

 

 

One of the blocks of the conference was a discussion panel on aspects of the FAAI project in the process of teaching AI and its use in the daily work of companies. The panel was attended by: Dr. Tomasz Gancarczyk – UBB (FAAI), Filip Wątorek – Pitney Bowes, Robert Gawlak – SI Record, Przemysław Dana – Vattenfall Polska.

The conference was attended by over 300 participants, who could listen to many interesting lectures related to AI and learn about aspects of its use in the educational process and in projects carried out on a daily basis in IT companies.

 

 

 

 

In addition to the lectures, participants could also take part in 4 workshops on AI:

1. Process Automation – ChatGPT in the work of a programmer. Filip Wątorek as Pitney Bowes
2. Build AI solutions with select Azure AI Services. Krzysztof Burejza – Data Community Podbeskidzie.
3. Scrum in practice. Mikołaj Kukurba – Vattenfall Poland.
4. Introduction to the world of cyber and pentesting. Michał Kłaput – Foundation 17 53c

 

 

 

 

Lectures:

Inaugural lecture – Areas of application of artificial intelligence. FAAI Project – Prof. Vasyl Martsenyuk – UBB

 

 

 

AI, a threat or… Is it high time to change jobs? Arcadiusz Benedict – Pitney Bowes.

 

 

A brief history of money. Will AI change the monetary system? Roman Majewski Crypto Mines.

 

When will AI create a video game? Artur Loska – Fool’s Theory

 

 

 

Malware vs antimalware. Michał Komendera UBB

 

The role of Cryptocurrency Miners in building decentralized AI. Konrad Duszyński – Crypto Mines.

 

Does IT have a future in Podbeskidzie? Wojciech Bachta – Da Vinci Studio.

 

 

 

Artificial intelligence and game design. Marcin Bernaś – UBB (FAAI)

 

 

Copyright and artificial intelligence through the eyes of a lawyer. Marek Nowicki – law firm.

 

All companies whose representatives took part in the conference

 Beskid IT Academic day

 

  1. SI Record
  2. Vattenfall from Poland
  3. Pitney Bowes
  4. Regional Development Agency
  5. Infonet Project
  6. NEXT!
  7. Fool’s Theory
  8. Crypto Mining Pools
  9. DaVinci Studio
  10. FabLab Bielsko-Biała
  11. BDY PSA
  12. Selleo
  13. Iteo
  14. BlackFrog
  15. JD Sport Fashion
  16. UX community
  17. Data Community Poland
  18. Kaczmarski Group
  19. District Road Administration
  20. WeNet Group S.A.
  21. inEwi
  22. MakoLab
  23. WASKO
  24. Marek Nowicki Law Firm
  25. conTeyor
Article of UCM Team on Motion Recognition Using a Transformer Model and LIDAR Sensor

Article of UCM Team on Motion Recognition Using a Transformer Model and LIDAR Sensor

Within framework of WP4, the UCM team of the project FAAI has published the paper “Playing Flappy Bird Based on Motion Recognition Using a Transformer Model and LIDAR Sensor” in Sensors MDPI.  The authors study a good practice using a transformer neural network which is employed in order to predict Q-values in a simulated environment using reinforcement learning techniques. The goal is to teach an agent to navigate and excel in the Flappy Bird game, which became a popular model for control in machine learning approaches. Unlike most top existing approaches that use the game’s rendered image as input, our main contribution lies in using sensory input from LIDAR, which is represented by the ray casting method.
Going forward, the authors aim to apply this approach in real-world scenarios.

The link to the paper is https://doi.org/10.3390/s24061905

Iva Kostadinova,Georgi Dimitrov,Paulina Tsvetkova,Katia Rasheva-Yordanova,Pepa Petrova, Research and Analysis of IT Specifications of Good Practices in the Area of Artificial Intelligence, 2023, 2023 International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023Pages 284 – 2902016 16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023Nis25 October 2023through 27 October 2023. ISBN 979-835034702-9, DOI 10.1109/TELSIKS57806.2023.10316145

Iva Kostadinova,Georgi Dimitrov,Paulina Tsvetkova,Katia Rasheva-Yordanova,Pepa Petrova, Research and Analysis of IT Specifications of Good Practices in the Area of Artificial Intelligence, 2023, 2023 International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023Pages 284 – 2902016 16th International Conference on Advanced Technologies, Systems and Services in Telecommunications, TELSIKS 2023Nis25 October 2023through 27 October 2023. ISBN 979-835034702-9, DOI 10.1109/TELSIKS57806.2023.10316145

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.

Best practices in applied artificial intelligence, 2023 – Research and Analysis of IT Specifications of Good Practices in the Area of Artificial Intelligence

Best practices in applied artificial intelligence, 2023 – 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 is 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.

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: