HEPTECH AIME ML&VA on Clouds

Contribution List

38. Opening

Peter Levai (WIGNER RCP)

29/10/2018, 13:00

Lecture

48. Supporting Decision Making through Interactive Visualisation

John Isaacs

29/10/2018, 13:10

Lecture

Data from a variety of sources is often presented in an abstract way that makes it hard for decision makers or non-expert stakeholders to understand the full context of the situation or problem illustrated. This talk discusses different approaches that the research team has taken in the presentation of data in a number of application areas including oil and gas logistics, environmental...

34. Artificial Intelligence and Smart Interaction Research at BME TMIT Smartlabs

Géza Németh

29/10/2018, 13:35

27. Life beyond the pixels: image analysis and machine learning for single-cell analysis

Peter Horvath

29/10/2018, 14:00

Lecture

In this talk I will give an overview of the computational steps in the analysis of a single cell-based high-content screen. First, I will present a novel microscopic image correction method designed to eliminate vignetting and uneven background effects which, left uncorrected, corrupt intensity-based measurements. I will discuss methods capable of identifying cellular phenotypes based on...

31. Getting Artificial Intelligence into industry

Andrew Gargett

29/10/2018, 14:25

Lecture

Artificial Intelligence is in the headlines a lot these days. Encouragingly, despite the hype, it also seems that some progress is being made with completing the circuit from arcane research topics to real world applications, at least for larger organisations geared up to better absorb any risks involved. However, what is not so clear is whether this is translating into widespread uptake...

1. Turning software into computer chips – Hastlayer

Mr Lehóczky Zoltán (Lombiq Technologies Ltd.)

29/10/2018, 15:10

Lecture

Software is flexible, specialized hardware is extremely fast. So why not write software, then turn it into a computer chip? This is what Hastlayer (https://hastlayer.com) does by transforming .NET software into electronic circuits. The result is faster and uses less power while you simply keep on writing software. You may not be able to tell just by looking at it but behind some function calls...

8. Online Machine Learning in Big Data Streams - Theory and Practice

Andras Benczur (Institute for Computer Science and Control, Hungarian Academy of Sciences)

29/10/2018, 15:35

Lecture

The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical restrictions on the modeling methods: In the data stream model, older data...

9. Federated Learning on the Edge

Mark Jelasity (University of Szeged)

29/10/2018, 16:00

Lecture

Federated learning is aimed at implementing machine learning based on training data stored by many personal devices. The key idea is that data is never transferred to a central location, instead, machine learning models are trained locally and then aggregated centrally. Our research aims at reducing the burden on the central cloud component by using local communication on the Edge. Devices...

47. From exploratory big data analysis towards run-time verification

Dezső Burján (Ericsson Hungary)

29/10/2018, 16:25

Lecture

Dimensioning and validating large-scale highly-available computing and communication systems necessitate extensive benchmarking campaigns, which generate vast amounts of measurement data. Moreover, models derived from the evaluations of these campaigns should be scalable and portable in the sense that derived conclusions have to be applicable in a variety of deployment configurations of...

3. modAL: A modular active learning framework for Python

Dr Danka Tivadar (Hungarian Academy of Sciences)

29/10/2018, 17:10

Lecture

With the recent explosion of available data, you have can have millions of unlabelled examples with a high cost to obtain labels. Active learning is a machine learning technique which aims to find the potentially most informative instances in unlabeled datasets, allowing the you to label it and improve the performance of classification.

[modAL][1] is a new active learning framework for...

28. Machine learning: learning from reverse engineering human intelligence and engineering artificial intelligence

Dr Gergo Orban (MTA Wigner RCP)

29/10/2018, 17:35

Lecture

Biological and artificial agents commit errors. These errors are fundamentally different and reveal something about the types of computations these agent are performing. Immense advances in machine learning help us understand what underlies human behavior and understanding human behavior provide insights into challenges machine learning are maced with. In this talk I will present how our lab...

10. How hidden knowledge in series of temporal events can be extracted and utilized?

Gyula Dörgő (MTA PE Lendület Complex Systems Monitoring Research Group)

29/10/2018, 18:00

Lecture

Temporal events are inherent parts of every industrial, business or generalized processes. The operational characteristic of these often high complexity processes is nicely represented by the generated temporal events. However, extracting useful knowledge from these large datasets and the process of model building using the extracted knowledge is by no means an easy task. Therefore, in our...

20. NIPGboard: An analysing, visualization and interacting AI tool

Kinga Faragó (Eötvös University)

29/10/2018, 18:45

Poster

The ACUMOS project of the Linux Foundation aims at integrating AI tools to support managers from business plan to monitoring and customer satisfaction estimation for building the next application. However, tools for close collaboration with human intelligence has not been included yet. I introduce our tool and demonstrate its capabilities for solving basic tasks with more or less help from the...

40. International Pilot Projects for Digital Transformation of Hungary

Dr Antal Nikodémus (ITM)

30/10/2018, 09:00

39. Introduction of the Accelerate Project

Steve Welch (ESP Central)

30/10/2018, 09:15

Lecture

12. Applied Artificial Intelligence For Assisted and Autonomous Driving

Robert-Zsolt Kabai (Continental)

30/10/2018, 09:30

Lecture

There is an industry-wide shift happening in automotive technology. Traditional computer systems and software technology cannot keep up with the increasing complexity of the problems we need to solve. AI shows great potential to be a key technology for autonomous driving yet there are many challenges for taking research results to a great real world product. The complexity of the real world...

41. The ESS Control System Machine Learning Project

Karin Rathsman (ESSS)

30/10/2018, 09:55

Lecture

21. Quantum Computing, Quantum Software and AI

Gábor Vattay (Eötvös Loránd University, Department of Physics of Complex Systems)

30/10/2018, 10:20

Lecture

In the last five years, quantum computers migrated from intellectual curiosity to
the realm of technological evolution. The first commercial computer (D-Wave) does
not resemble the ideal quantum computer physicists and mathematicians dreamed
of for decades. The most dramatic difference between a Turing type machine and the new architecture is that it
not based on logical steps. While for the...

42. The Mathematical Foundations of Artificial Intelligence - about the National Excellence Program

Balazs Szegedy (Alfred Renyi Institute of Mathematics)

30/10/2018, 11:15

Lecture

26. Understanding understanding

Prof. Antal Jakovác (ELTE, Dept. of Atomic Physics)

30/10/2018, 11:50

Lecture

The goal of this talk is to give the mathematical background of what happens when we understand a phenomenon, independently that it happens in a computer or in a human mind. We give a definition for "understanding" as a special representation of the input. We prove that such a representation exists, and demonstrate that with it all AI tasks (classification, regression, compression, generation)...

7. AI Methods in Human Language Technologies

Gábor Prószéky (MTA-PPKE Hungarian Language Technology Research Group)

30/10/2018, 12:15

Lecture

In the last two-three decades researchers in human language technologies have tried to apply various statistical methods to understand what is encoded and how in large text corpora -- with limited success. The previous 5-6 years have basically changed both the basic research paradigm and the level of success in this research area as well. Continuous vector space models, neural networks, deep...

2. From Applied Deep Learning to Artificial General Intelligence

Prof. Lőrincz András (Eötvös Loránd University)

30/10/2018, 12:40

Lecture

Since about 1950, reknown researchers have claimed from time-to-time that artificial intelligence (AI) will reach human intelligence in about 10 years. It hasn't happened. On the other hand, the evolution of computational power is exponential and the exponent of Moore's Law is large. Churchland's question -- is the brain more complex than clever? -- is still here. What are we missing?
I argue...

23. Visualizing velocity field strengths in medical image data with hyper-surfaces in spacetime

Dr Bernd R. Schlei (GSI Helmholtzzentrum für Schwerionenforschung GmbH)

30/10/2018, 14:05

Lecture

One-dimensional (1D) time sequences of spatial, three-dimensional (3D) simulation or image data may implicitly carry dynamical information of their embedded subregions. Continuous hyper-surfaces can be constructed for the full 3+1D data that enclose certain spacetime regions. Here, we demonstrate that such hypersurfaces may be viewed as 3D velocity vector fields, which explicitly characterize...

5. Graph search and knowledge extraction related to communities

Jean-Marie Le Goff (CERN)

30/10/2018, 14:50

Lecture

According to Daniel Keim: "Visual analytics combines automated analysis techniques with interactive visualisations for an effective understanding, reasoning and decision making on the basis of very large and complex datasets”.

The effectiveness of Visual analytics essentially depends on the seamless interplay between automated analysis and interactive visualisation. In particular, the later...

4. The Graph of Our Mind

Prof. Vince Grolmusz (Eötvös University)

30/10/2018, 15:25

Lecture

The fast development of diffusion MRI techniques made possible the mapping of the connections of the human brain on a macroscopic level: the graph consist several hundred, anatomically identified vertices, corresponding to 1-1.5 cm^2 areas of the gray matter (called Regions of Interests, ROIs), and two such vertices are connected by an edge if axonal fiber tracts are discovered between them....

14. Network-based analysis of common genetic background of diseases

Bence Bruncsics (Department of Measurement and Information Systems, Budapest University of Technology and Economics)

30/10/2018, 15:50

Lecture

Most common diseases are polygenic; therefore, multiple even hundreds of genes out of the overall 23,000 can be responsible for a disease. The simultaneous appearance of diseases, comorbidities, like amongst neurological disorders are expected to have a common genetic background, which can be explored using network-based approaches.
Novel network-based workflows for genetic studies provide a...

17. Failure root-cause analysis by data analytics: concept and a case study

Dr László Milán Molnár

30/10/2018, 16:45

Lecture

For automotive companies, continuous improvement of the manufacturing process is a must in order to achieve optimal product quality and cost. The traditional approach for this improvement process is Model Based Engineering, where hypotheses, and cause-effect chains are discovered purely by considering the laws of physics.
At Robert Bosch, engineers and data scientists are working on a concept...

46. Towards using ML in critical applications

Andras Pataricza (Budapest University of Technology and Economics)

30/10/2018, 17:20

Lecture

Machine Learning provides highly efficient solutions for complex problems. However, the "black-box" or at most grey-box nature of the technology prohibits its use in many critical applications necessitating a throughgoing justification for the correctness of the results delivered.
One rapidly evolving approach is xAI (eXplainable AI) targeting the simultaneous delivery of a result and...

18. Federated, privacy-preserving learning of large-scale probabilistic graphical models

Dr Péter Antal (Budapest University of Technology and Economy)

30/10/2018, 17:45

Lecture

Probabilistic graphical models are successfully applied in many challenging problems of artificial intelligence and machine learning: in data and knowledge fusion, in causal inference, in trustworthy decision support systems or explanation generation. First, I summarize that their wide applicability stems from their transparent, multifaceted semantics. Second, I show that the same property...

29. ML and VA tools in support of software testing

András Lukács (Eötvös Loránd University)

30/10/2018, 18:10

Lecture

Software testing makes up a significant part of software development processes. This is especially true in the case of a complex IT system like IP Multimedia Subsystem (IMS). Our case study describes machine learning and visual analytics approaches to support a non-functional performance test, the endurance test. This test checks whether the software can continuously work without performance...

44. Best Poster Award, Closing

Andras Telcs

30/10/2018, 18:35

15. A deep convolutional neural network approach for astrocyte detection

Mr Tamas Balassa

Poster

Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images and elucidating the temporal characteristics of morphological and biochemical changes is essential to understand the role of astrocytes in...

25. Analysis of molecular electronics measurements with neural networks

Nóra Balogh (Budapest University of Technology and Economics)

Poster

Creating electrical circuit elements from one atom or molecule is one of the main issues in current molecular electronics research. Nowadays, investigation of conductance values of a single molecule can be realised at high mechanical stability by the mechanically controlled break junction (MCBJ) technique.

Among the high amount of conductance traces generated by break junction measurements,...

33. Artificial Intelligence and Smart Interaction Research at BME TMIT Smartlabs

Géza Németh

Lecture

Social robotics are a popular research topic. A strong business case is health social robotics. Our Lab has performed a viability test in a real environment and we are looking for partners for a large-scale research project. The scenario is about supporting child bone-marrow transplantation patients by a social robot. The unique feature of the approach is that it is both highly variable,...

13. Detecting nuclei with deep learning and image style transfer

Réka Hollandi (Hungarian Academy of Sciences, Biological Research Center)

Poster

A novel method for nuclei detection is proposed to process diverse microscopy images. Our method incorporates deep learning techniques such as automatic training data generation from input test images called image style transfer learning that allows adjustment to the test set prior to training even with limited data size. The proposed method was originally designed for the Kaggle Data Science...

36. Federated Learning on the Edge

Mark Jelasity (University of Szeged)

22. Graph search and knowledge extraction related to communities

Jean-Marie Le Goff (CERN)

Lecture

According to Daniel Keim: "Visual analytics combines automated analysis techniques with interactive visualisations for an effective understanding, reasoning and decision making on the basis of very large and complex datasets”.

The effectiveness of Visual analytics essentially depends on the seamless interplay between automated analysis and interactive visualisation. In particular, the later...

16. Image database generation techniques for DIC brain tissue cell segmentation

Mr Nikita Moshkov (Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Szeged, Hungary; National Research University Higher School of Economics; University of Szeged)

Poster

Deep learning algorithms became more and more popular for solving image processing tasks in the biomedical field. One of such tasks is cell detection and segmentation in differential interference contrast microscopy (DIC) brain tissue images. These algorithms require hundreds and thousands of images with ground truth segmentations for training to be highly accurate and we lack similar publicly...

6. Improving overall equipment effectiveness of manufacturing cells by process mining and machine learning

Gergely Honti

Poster

The sequences of discrete events related to machine status can
characterize the operation of production systems. A model-based
methodology for Overall Equipment Effectiveness (OEE) improvement is
proposed based on the integrated analysis of the work-instructions,
the set of product-relevant expected manufacturing times, and
information about the competencies of the operators. The process...

24. International Pilot Projects for Digital Transformation in Hungary

Dr Antal Nikodémus (ITM)

Lecture

35. Online Machine Learning in Big Data Streams - Theory and Practice

Andras Benczur (Institute for Computer Science and Control, Hungarian Academy of Sciences)

30. Opening

Peter Levai (WIGNER RCP)

Lecture

32. TBA

John Isaac

45. The ESS Control System Machine Learning Project

Karin Rathsman (ESSS)

Lecture

The European Spallation Source ERIC (ESS) is a joint European organisation committed to building and operating the world's leading facility for research using neutrons. The facility design and construction includes a powerful linear proton accelerator, a helium-cooled tungsten target wheel and two dozen state-of-the-art neutron instruments.

Rapid advances in technology around artificial...

11. The Mathematical Foundations of Artificial Intelligence - about the National Excellence Program

Dr Balazs Szegedy (Alfred Renyi Institute of Mathematics)

Lecture

This talk is an overview of a new collaboration between five institutes (Renyi, SZTAKI, PPKE, ELTE, SZTE) in the frame of the National Excellence Program. Since Hungary is traditionally strong in mathematics, an important goal of the program is to use this resource and to involve more mathematicians in the dynamically developing field of machine learning. Another goal is to explore new...