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IICST 2021 Keynote Speakers

Péter Ihász

Keynote Title: AI for Data Science in Industry

Keynote Speaker: Péter Ihász

Péter Ihász is an experienced researcher and developer with a working history in the fields of Applied and Computational Linguistics. During his studies at Ritsumeikan University and the Hungarian Academy of Sciences, Peter conducted research with a particular focus on Neural Language Modeling and Deep Language Generation. Currently he works at Deutsche Telekom IT Solutions Hungary, managing technical and architectural aspects of AI development.

Keynote Abstract:

Owing to recent technological advancements, the interest in and demand for artificial intelligence (AI) methods in data science have grown dramatically. The market has been flooded with AI gadgets targeting individual consumers. For instance, algorithms have been developed to generate realistic images and video streams (deep fake algorithms), automatically produce witty responses to user prompts (chatbots), and even predict one’s genetic heritage based on selfies (image classifiers). At the same time, there is a vast spectrum of AI applications meant for industrial use and, specifically, for the optimization of business strategies and processes. Time-series predictors forecasting the number of incoming calls to call-centers, reinforcement learning algorithms optimizing production lines, and search engines enabling quick and effective browsing on company homepages are just a few examples of such applications. The broad introduction of AI systems in the industry typically necessitates incremental developments with continual improvements and operational maintenance. The system lifecycle therefore includes additional activities besides predictive data modeling, such as data store management, data ingestion, deployment automation, etc. This talk is to provide the conference participants with insights on the lifecycles of industrial AI products by discussing several supervised and unsupervised use-cases. It will also elaborate on the DevOps and MLOps culture in a context of the AGILE framework – a set of developmental and operational practices aimed at shortening the system development time and providing for continuous delivery with high software quality.

Irina Pavlova

Keynote Title:
Developing soft skills of a technology entrepreneur: understanding your customer through your value proposition

Keynote Speaker: Irina Pavlova

Irina Pavlova is an Associate Professor at Innovation Management Department of Tomsk State University of Control Systems and Radioelectronics and an Associate Professor at School of Engineering Entrepreneurship of National Research Tomsk Polytechnic University (Tomsk, Russia). She has a scientific degree of Cand. Sci. in Economics. Her area of specialization is innovation management, innovation systems, entrepreneurial universities, triple helix model, regional dimension of innovation, innovation statistics, innovation and technology policies.

Keynote Abstract:

For technology entrepreneurs, understanding your potential customer is a critical skill today. Your unique selling proposition, more commonly referred to as a USP, manifests that your offering is better than those of your competitors’. The underlying value proposition included in the USP differentiates your products and makes them to stand out at the market. What are the values of your customers that you are addressing to? During this workshop we will learn how to formulate functional, social and emotional values to fit into your value proposition. Since the value proposition is the centerpiece of the business modelling, good business models communicate the value messages to the customer in the most efficient way. The workshop will help you to explore how a value proposition should quickly answer the most immediate question when the potential customers encounter your brand. This question is “What makes you different from the rivals?” Also, we will learn what affects the value proposition messages in different societal, cultural and industrial

Ivan Krechetov

Keynote Title: Artificial Intelligence Technology in Education

Keynote Speaker: Ivan Krechetov

Ivan Krechetov is the head of the Laboratory of e-Learning Tools and Systems (LISMO) in TUSUR University. Today, the lab provides support for the university's e-learning systems and develops e-courses, simulators, and virtual labs. Since 2010, he has been engaged in research of adaptive learning. He has developed new algorithms, models, and software that allow universities to implement adaptive learning in LMS systems and develop adaptive electronic courses. The results are now used in some of the largest universities in the country. Current activities focus on the field of learning management based on artificial intelligence and data analysis technologies.

Keynote Abstract:

Artificial Intelligence technology is actively being introduced into various fields of activity. In education, these technologies are not yet as widely used as in business, but there are already well-known practices that use the power of AI. These include adaptive learning technology, a concept designed to improve the quality of education through a personalized approach and in-depth analysis of the learner's behavior. The presentation discusses TUSUR's experience in implementing adaptive learning in the educational process, methods, models and approaches underlying this technology.

Link to IICST 2021 Program

Accepted Papers — Listed Aphabetically by Title

Authors Title Link
Takumi Fujii, Damon Chandler and Yasuhiro InazumiA method of driving video enhancement via CycleGAN for improving automated object detection
Ryota Murai, Hideyuki Takada and Maki IchimuraA Support System for Stimulating Discussions in Combination with Face-to-face and Remote Online Meetings
Michael Evan Santoso, Thariq Maulana Jauhari, Budi Darma Setiawan, Uwe Imre Serdült and Victor V. KryssanovA System To Support Planning of Donor Blood Delivery To Hospitals in Indonesia
Yuma Takei, Mehran Andalibi, Damon Chandler and Takeshi HashimotoAutomatic glare region detection using deep-learning-based semantic segmentation
Elisabeth Veronica Mess, Matthias Regner, Sabahudin Balic, Sabrina Bethge, Lisa Daufratshofer, Andreas Mahler, Alexandra Teynor and Claudia ReuterCare Transition Records: A Solution Approach towards Seamless Digital Processing
Daniel Moritz MarutschkeColor Key-Phrases Data-Mined from Twitter Analyzed Using Word-Embedding in Language Models
Kaito Nagao, Yusuf Margowadi, Mehran Andalibi and Damon ChandlerDetection and Classification of Tactile Paving for the Visually Impaired via Deep Learning
Yuto Tomita, Weitao Pan, Victor Kryssanov and Uwe SerdültGaze Tracking Technology for Smart Environments
Yushi Yamanaka, Yi Zhang and Damon ChandlerImage super-resolution via a combination of DCT coefficient estimation and SRGAN
Maria Kostina, Yulia Shulgina, Alexey Soldatov and Alexandra KudryashovaImpact of Phase Detection on Acoustic Measurements Accuracy
Maria Kostina, Yulia Shulgina, Alexey Soldatov, Ahmed Ali Abouellail and Evgeny ShulginMathematical optimization in building images of inspected objects using ellipse properties
Ngoc Anh Nguyen KhaRecommending Recipe from Food Image Based on CNN and Transformer Self-attention Model
Dongfeng Guo, Keerati Fungtammasan, Yang Bai and Mikhail SvininRobust Adaptive Multi-Agent Coverage Control For a Dynamically Changing Area
Ignasius Ian Savio Gunawan, Ivan Wanjaya, Igor Goncharenko and Yanlei GuSign Language Recognition by Machine Learning Using Multimodal Wearable Sensors and RGB Imagery Data
Annisa Dea Rachmantya, Budi Darma Setiawan, Victor Kryssanov and Uwe SerdültTraffic Anomaly Detection Based on Vehicle's Orientation
Michael Evan Santoso, Shaiful Nizam, Yang Bai and Mikhail SvininVoronoi-based Flood Area Detection Using Multi-agent Systems