- Knowledgefeed: Efficient ML for mobile devices & Segmentation in Surveys
Dear Community, We hope you had a great summer! We are very happy to announce that the VDSG is back from summer break, too, and our next meetup is happening in two weeks in A1! This time we are having two talks: Segmentation in surveys and efficient machine learning for mobile devices. Jelena Milosevic: "Efficient machine learning for mobile devices" (30-35 mins) Increased amount of data allows for better training and more accurate machine learning systems. Big part of generated data today is coming from embedded and mobile devices, whose number is constantly on the rise. In order to fully profit from the collected information, artificial intelligence should come to these devices. However, this is currently difficult to achieve, mostly due to the computational demands of machine learning systems being too high for constrained computational resources of mobile devices. One of the reasons for this is that, when designing machine learning methods, most people only focus on accuracy, without taking into account constrained computational resources of developed solutions. Many applications rely on efficient machine learning. Some examples are: vision and image processing, autonomous driving, and malware detection. In order to facilitate novel applications in these domains, it is of utmost importance to provide machine learning solutions that are not just accurate, but also suitable for constrained environments. In my talk I will discuss how we can design and develop such solutions suitable to be used in real-time, on-device, and at the same time customizable with respect to application requirements (accuracy, inference time, and power consumption). Jelena Milosevic is passionate about machine learning and cybersecurity. She is currently a postdoctoral researcher at the Institute of Telecommunications, TU Wien, where she designs and develops machine-learning-based methods for detection of cyber attacks. She obtained her PhD in 2017 from Faculty of Informatics, University of Lugano, Switzerland, where her main focus was on the malware detection systems suitable for runtime usage on resource constrained systems and based on machine learning methods of low complexity. Previously, Jelena was an intern at IBM Cyber Security Center of Excellence in Beer Sheva, Israel, where she worked on the time-series analysis for anomaly detection and in Movidius an Intel Company in Dublin, Ireland, where she worked on the design and development of deep learning methods suitable for embedded environments. https://www.linkedin.com/in/milosevicjelena/ Marcin Kosiński: "Segmentation in Surveys using NMF" (30-35mins) Working with high dimensional data? Often facing the need to group observations? This presentation is for you. Segmentation should be balanced and distinctive, the discovered over- and under-indexed features within segments should create a meaningful story, and, ideally, the amount of differentiative factors that drives segmentation should be small. The last requirement often becomes a bottleneck in a survey where respondents are asked an enormous amount of questions. One solution is the nonnegative matrix factorization that, in one attempt, segments respondents and their features! The concept of the NMF decomposition and applications in R will be presented with the explanation of diagnostic plots. Marcin has a master degree in Mathematical Statistics and Data Analysis specialty. Challenges seeker and devoted R language enthusiast. In the past, keen on the field of large-scale online learning and various approaches to personalized news article recommendation. Community events host: organizer of Why R? conferences whyr.pl. Interested in R packages development and survival analysis models. Currently explores and improves methods for quantitative marketing analyses and global surveys at Gradient Metrics. https://www.linkedin.com/in/marcin-kosi%C5%84ski-81435aab/
- Big data-Industrial IoT Meet-Up Linz
Dear Community, Everyone is talking about Big-Data and its increasing importance. You are interested in this topic and want to learn more about integrating such solutions into your business? You want to listen to experts and think that afternoon coffee is the best time to discuss? Vienna Data Science Group is pleased to invite you to the first Meet-Up presented together with the Uni software plus GmbHin Linz. Agenda: Talks: "The Evolution of Machine Data Pipelines at ENGEL" by Christoph Schönegger (ENGEL AUSTRIA GmbH), Stefan Janecek (uni software plus GmbH) "Building 1st Data Science Community in Slovakia" by Radovan Kavicky (Vienna Data Science Group) Afterwards: Discussion with drinks & snacks Registration: [masked] (open until[masked]) The Meet-Up will be in English language. Further Information "The Evolution of Machine Data Pipelines at ENGEL": According to Forbes , 90 percent of the data humanity owns today has been generated in the past two years. One of the main drivers behind this breath-taking world-wide data growth is industrial IoT (IIoT), the extension of the "internet of things" (IoT) to the industrial sector. In their talk, Christoph Schönegger (Engel Austria) and Stefan Janecek (uni software plus) will share their story of developing the "Engel Edge Device", a secure solution for collecting, cleaning, aggregating and processing IIoT data "on the edge", i.e., close to the source of the data. The Edge Device System allows to centrally manage containerized applications, and deploy them to small hardware appliances running "on the edge". These applications range from simple ETL (extract transform load) processes to algorithms that make decisions based on data and feed those decisions back into production machines. Since security is paramount in industrial applications, the Edge Devices are protected by strong cryptography, using the on-board TPM 2.0 chip as a trust anchor. In addition to the security considerations on the Edge Device itself, the talk will also describe the secure data logistics solution to transfer data from the Edge Devices to a central Hadoop data lake. https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#18f7e27660ba We are looking forward to welcome you to an informative evening!
- MyData Austria Meetup #3
In this meetup we will shed light on the topic of data as a property: is it possible to own data, how can data or usage rights for that data be sold and what are the implications? This is a co-hosted event with the MyData Austria Meetup group. Agenda: 1. Clara Landler: Welcome & Introduction 2. Christoph Fabianek: Semantic Container for Data Mobility, Closing presentation of FFG funded project 3. Soheil Human, Rita Gsenger, Kemal Ozan Aybar: short talks on "End-user Empowerment: An Interdisciplinary Perspective" and "Digital Inequality: Call for Socio-technical Privacy Management Approaches". 4. Michael Shea: Fishbowl discussion about Data as a Human Right Looking forward to meeting you !
- Data Science Alliance Kickoff - Deep Learning & ML
STREAM: Please join the First stream at the start of the meetup: 1. Belgrade: https://www.youtube.com/watch?v=fx9BLHsWwns 2. Vienna: https://youtu.be/KSAzCRQE_-w 3. Zagreb: https://youtu.be/m-44Pc-ny6I Questions? Post it here: http://sli.do , channel: #dsmeetup Welcome to our first ever international Meetup, a result of VDSGs ongoing effort of internationalization. Meet the Data Science Alliance, where speakers from Belgrade, Zagreb, and Vienna will share their knowledge with the international community. Talks will be held in 3 cities in parallel on May 15th. The concept of the event is to have one talk in-live and two talks over the online streams at every location. The theme of the meetup will be the application of Machine Learning & Deep Learning on real-life examples. You will get a chance to listen to 3 distinguished speakers from Belgrade, Vienna, and Zagreb. Agenda : Opening talk - 17:30-17:45 First Talk-17:45-18:10 Speaker -Marko Knezivic Topic: Recommender systems for personalized content in video games Marko Knežević holds PhD title at the Department of Computing and Control Engineering at the Faculty of Technical Sciences, University of Novi Sad. In this talk, we’ll give light overview over recommendation techniques and how we allowed model to capture some general behavior patterns regarding buying and how we tackled common obstacles such as data dimensionality and class imbalance. Linkedin : https://www.linkedin.com/in/marko-kne%C5%BEevi%C4%87/ Second Talk- 18:15- 18:40 Speaker-Allan Hanbury Topic: Exploiting Data in Medicine Allan Hanbury is Professor for Data Intelligence at the TU Wien and faculty member of the Complexity Science Hub Vienna. He was a scientific coordinator of the EU-funded Khresmoi Integrated Project on medical and health information search and analysis and is a co-founder of contextflow, the spin-off company commercializing the radiology image search technology developed in the Khresmoi project. This talk will present examples of approaches to extracting value from medical data, in particular from research articles and radiology images, as well as paths toward more effective exploitation of data in medicine. Linkedin: https://www.linkedin.com/in/allan-hanbury-9483a26/ Third Talk- 18:50- 19:15 Speaker-Tomislav Krizan Topic: AI in FinTech service: Predicting Credit Debt Bankruptcy Tomislav plays with data for a long time on every possible and impossible way. First Big Data project was on account-tickets in a book keeping service (low tech approach). Where the most see just numbers and letters, he finds a purpose and information. For a while now he is playing on a field of DM/ML models for business purposes, with the last couple of years having a spotlight on text analytics and NLP (unstructured datasets) This talk is going to be about how we develop models which can tell us with high accuracy rate if the debt user will go into the personal bankruptcy. Our algorithms are self-learned, so with every new iteration result are showing greater accuracy. In the first part of the talk we shall speak about how we used Gradient Boosting Machines (GBM) & Random Forest on the past and current data of credit debts of the users (such as how much are the user in the minus on bank and credit cards, how long they were late with payments and cetera) to predict personal bankruptcy. We have followed short term loans (up to 60 months), which were granted in the US. Project was also done for several Europeans countries, but with a bit lesser accuracy - caused by harder access to the data on customer placements and the level of indebtedness. Linkedin: https://www.linkedin.com/in/tomislavkrizan/ Looking forward to meeting you !
- data4good Hackathon
The First data4good Hackathon! In collaboration with WeAreDevelopers::Keep-Current and R-Ladies Vienna. Are you looking to use your skills, knowledge or interest in data for good? Then join us in our first data4good Hackathon, for two days of pure coding & fun while doing something good on the way -- Be a part of the change you want to see in the world of data! We seek all data enthusiasts: aspiring data scientists or experienced data wizards; savvy solvers of business intelligence requests; machine learning pros, predictive analytics angels, data mining experts, computer vision idealists, UX maestros and ambitious developers with an interest in social causes… Let's help together GruenStattGrau, Hilfswerk Österreich, Hilfswerk International & CivesSolutions in their quest. For more details: https://viennadatasciencegroup.at/data4good/ !!! IMPORTANT !!! -------------- This time it's not enough to just click attend. To be matched in the teams, please register yourself here: https://www.eventbrite.at/e/data4good-hackathon-wien-tickets-58713887819 Schedule: -------------- Saturday: 09:00 Breakfast and open Help Desk 10:00 Opening 10:30 Project introduction by the Data Science Leads + 15min Q&A 11:15 Team-building 11:30 Hacking !!! 13:00 Lunch break 14:00 Hacking !!! 17:30 Review: Status report Sunday: 09:00 Breakfast 10:00 Hacking !!! 13:00 Lunch break 14:00 Hacking !!! 15:30 Preparing Demo & Slides 17:00 Final presentations 18:00 End We look forward to seeing you!
- VDSG Data Science Café : New volume of the DS Café
Dear community – the wait is finally over, the next DS Cafe is here! This volume of the DS Cafe is a combined effort with the data4good Hackathon organizing team, and will be hosted by A1. Everyone – whether hackathon participants or not (yet) - is invited to come, meet each other, get to know more about the data4good projects, and relax. You can participate regardless of your experience in Data Science - all you need is some curiosity, your computer, and the willingness to talk to other Data Science enthusiasts around you. In addition, if you have a project you would like to share with the DS community, please get in touch with us – we will give you 3 minutes at the beginning of the Cafe to introduce yourself and your idea. Looking forward to seeing you all there! ------------------------------------------------------------------------------------------ FINAL DETAILS If you would like to be included in the SLACK channel, please let us know your email address in the appropriate field when RSVPing. What we provide: * A nice location, refreshments and snacks, WIFI, and a friendly environment so you can connect with your colleagues. What to bring: * A laptop, headsets, writing utensils, and curiosity
- Knowledgefeed vol. 28: (Big) Data (Science) for Security
Dear Community, Get ready for our 28th Knowledgefeed featuring security research question that motivated an ambitious data collection and analysis approach. While a common opinion is that a collection of "big data" only leads to security and privacy problems - and it often does - however the analysis of large amounts of IT systems behavioral data also enables new experimental approaches to improve IT security and protect us from the cesspool of malware that the internet is. The TARGET research project at St.Pölten UAS started with an initial IT security research question that motivated an ambitious data collection and analysis approach, and that then spawned subsequent challenges in data collection, encoding, storage, processing, experimental algorithm implementation, up to production deployment. This talk reflects on the challenges encountered and experiences made. Talk (duration 45- 60 mins) By Martin Pirker who is a Senior Researcher at the Institute of IT Security Research, St. Pölten University of Applied Sciences. His current work focus is the Josef Ressel Center for Unified Threat Intelligence on Targeted Attacks (TARGET), and all kinds of weird problems that arise when IT (security) meets privacy and big data. Website: https://isf.fhstp.ac.at/en https://research.fhstp.ac.at/en/projects/josef-ressel-center-for-unified-threat-intelligence-on-targeted-attacks-target https://isf.fhstp.ac.at/en/forschende/martin-pirker
- Knowledgefeed: Deep Learning for Predictive Quality & Predictive Maintenance
Dear Community, Get ready for our 27th Knowledgefeed featuring characteristics of Industrial AI and how state of the art deep learning methods can be applied to solve complex problems and bring more value to companies. Talk (duration 60 - 90 mins): Predictive Maintenance, Predictive Quality & Visual Inspection By Simon Stiebllehner -Head of AI, craftworks & Daniel Ressi -Data Scientist, craftworks Artificial Intelligence plays a major role in Industry 4.0 and more industrial companies than ever are starting to utilize their data to gain value and insights. The industrial domain offers very promising opportunities but this potential also comes with very specific requirements and challenges. This talk gives insights into the characteristics of Industrial AI and how state of the art deep learning methods can be applied to solve complex problems and bring more value to companies. Based on real use cases, three common areas of Industrial AI and the applied modelling approaches will be presented: 1. Predictive Maintenance: Can faults of machines be predicted in advance? 2. Visual Inspection: Can computer vision automatically assess the quality of products? 3. Predictive Quality: Can product defects be predicted in advance and prevented in future? Simon is a Head of AI at craftworks and lecturerin statistics and digital marketing at WU Wien and FH Wien. After having completed his Bachelorin Information Systems, he gained diverse industry experience,ranging from Microsoft to global players of the consulting industry. Subsequently, Simon obtained his Masters degree from University College London (UCL), specializing in Machine Learning and Data Science. Afterwards, he was a doctoral candidate and research associate, conducting research at the intersection of Neural Probabilistic Language Models and Recommendation Systems in a Real-Time Bidding context. LinkedIn: https://www.linkedin.com/in/simonstiebellehner/ Website: https://craftworks.at Daniel is a Data Scientist at craftwork and develops customized deep learning solutions for industrial clients. His background is in Biomedical Engineering, where he focused his research on Recurrent Neural Networks for Brain Machine Interfaces (BSc, TU Graz) and for Computational Neuroscience (MSc, Imperial College London). Craftworks develops award-winning artificial intelligence solutions for industrial enterprises. Their customers range from the automotive to the paperindustry and everything in between. LinkedIn: https://at.linkedin.com/in/daniel-ressi Website: https://craftworks.at As always: grab the opportunity to ask our lecturers questions, discuss your ideas and of course enjoy the company of some interesting folks! Please do not hesitate to present your own projects, ideas or thoughts. We are more than gladly sharing the stage with you! Please refer to our blog-post / Discussion entry for further details: https://viennadatasciencegroup.at/2016/09/06/power-to-the-people/ Looking forward to meeting you at the Knowledgefeed!
- VDSG Data Science Café : New Volume of the DS Café
Dear community – new year, new volume of the DS Cafe! We hope you’ve had a “guter Rutsch” into 2019, and are ready for more learning and working together on Data Science! For February’s edition, we will visit Ready2Order's offices again. Their vision is to digitize all small entrepreneurs and improve their business processes, by creating a marketplace in which to offer entrepreneurs the most popular apps for a 360-degree service, including warehouse management, online shop, accounting, etc. Their team will be there to talk to the community. You will have the chance to talk about some of the work-in-progress projects, or even better, start your own idea and make it happen through the guidance and support of your Data Science network. Everyone is invited to come and share their ideas, or simply explore and decide if there is a project you want to be involved in. You can participate regardless of your experience in Data Science - all you need is some curiosity, your computer, and the willingness to talk to other Data Science enthusiasts around you. If you have a project you would like to share with the DS community, please get in touch with us – we will give you 3 minutes at the beginning of the Cafe to introduce yourself and your idea. ------------------------------------------------------------------------------------------ FINAL DETAILS Please let us know your email address in the mandatory field when RSVPing, so we can include you in the SLACK Channel. What we provide: * A nice location, refreshments and snacks, WIFI, and a friendly environment so you can connect with your colleagues.What to bring: * A laptop, headsets, writing utensils
- Knowledgefeed vol. 26: AI for Smart Cities & Privacy-Preserving Big Data Sharing
Dear Community, get ready for our 26th Knowledgefeed featuring 2 talks of how data science & AI can advance innovative solutions to our city living and what can be done to protect personal information from illegitimate use. Talk 1 (duration 45 - 60 mins): Practical implementation of AI solutions for smart cities by SK Reddy, Chief Product Officer AI for Hexagon, Silicon Valley. AI is making dramatic inroads into city living to solve problems that were earlier thought to be impossible. How to predict the sentiment of people in the mall? How to prevent a crime using regular surveillance cameras? How to predict traffic? How to use satellite images to plan for city expansion? How to predict the quality of my city services? How to auto-analyze social media feed for trigger-based city-safety actions? These are some of the problems that could be solved using Artificial Intelligence. Cities and organizations need to understand the importance of quality of data and the right models to pick. The talk covers the current state of the art in statistical machine learning, explores the paths to approach strategic AI in Cities, and discusses the ‘what’ and ‘how’ of the deep learning techniques in Smart Cities. SK is the Chief Product Officer AI in Hexagon (www.hexagon.com). He is also an AI and ML expert and a successful twice startup entrepreneur. He is a frequent speaker in conferences and an AI blogger. LinkedIn: www.linkedin.com/in/skreddy99/ Blogs: www.linkedin.com/in/skreddy/detail/recent-activity/posts/ YouTube: https://www.youtube.com/user/skreddy99/videos Talk 2 (duration 45 min): Privacy-Preserving Big Data Sharing by Michael Platzer, CEO and Co-Founder of Mostly AI, Vienna. The digitalization of our world results in an ever-increasing amount of personal data being gathered. Processing and sharing these big data assets drives scientific progress, fosters innovation and enables smarter products and services. Yet the very same data, more often than not, contain sensitive personal information that is to be protected from illegitimate use. These conflicting targets, data-driven innovation vs. data protection, pose one of the biggest challenges of today’s digitalized world. AI-generated synthetic data offers a fundamentally new solution to this long-standing issue. Agenda: - Privacy vs. Innovation - Under-Estimated Risk of Re-Identification - AI-generated Synthetic Data - Differential Privacy - Customer Story from Finance - QA & Discussions Michael Platzer is CEO and Co-Founder of Mostly AI (https://mostly.ai), a Vienna-based startup, that develops fundamentally new approaches towards safe & secure big data anonymization. He earned a master degree in Technical Mathematics at TU Wien, a doctoral degree in Marketing Science at WU Wien, and was recognized for his work on customer behavior prediction with the Global Research Award by the American Marketing Association. LinkedIn: www.linkedin.com/in/mplatzer/ www: https://mostly.ai ------------------------------------------------------------------------------------------------- As always: grab the opportunity to ask our lecturers questions, discuss your ideas and of course enjoy the company of some interesting folks! Please do not hesitate to present your own projects, ideas or thoughts. We are more than gladly sharing the stage with you! Please refer to our blog-post / Discussion entry for further details: https://viennadatasciencegroup.at/2016/09/06/power-to-the-people/ Looking forward to meeting you at the Knowledgefeed!