Data Science made in Switzerland
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The ZHAW Data Science Laboratory is the place to transform deep data science know how into innovative research projects and vibrant teaching in Switzerland.
Data Science made in Switzerland
3y ago
By Nico Ebert (ZHAW)
translated from the original German language version published at Inside IT
A common narrative in practice sounds something like this: “people claim data protection is important to them, but in reality they give away everything on the internet anyway”. There are also some science studies that seem to prove this again and again: that we are generally careless with our and other personal data and that we consider data protection important but neglect it in everyday life. For example, a “pizza experiment” with 3,000 students at a US university in 2017 concluded ..read more
Data Science made in Switzerland
3y ago
By Nico Ebert (ZHAW)
cross-posted from the author’s blog
Many Internet users inside and outside the European Union are very familiar with cookie banners: they pop up on websites, they are often annoying, and it is tedious to really deal with them. Having to state our data sharing and protection preferences over and over again is a questionable concept by itself. But even if we accept the concept of cookie banner as a matter of fact our behavior towards them seems paradox at a first glance.
It has long been known that some of the tracking techniques used are very privacy invasive (e.g. session ..read more
Data Science made in Switzerland
3y ago
By Bettina Mack (ZHAW)
ANNPR, the “International Workshop on Artificial Neural Networks in Pattern Recognition” is a biennial academic conference where researchers come together to discuss the most recent advances in the fields of neural networks, deep learning and artificial intelligence as applied to pattern recognition. Pattern recognition is the field of computer science which is concerned with making sense of data such as images (“What do we see in the picture?”), audio data (for example, to recognize spoken words) or time-dependent inputs such as weather or stock-market data. This year’s ..read more
Data Science made in Switzerland
3y ago
By Fernando Benites, Lara Leuschen, Diana Betzler and Mark Cieliebak
cross-posted from the SpinningBytes blog
Introduction
We concluded an compelling interdisciplinary project on the topic of digitalization, where we applied a selection of fundamental methods of data science: web scraping, data wrangling with elastic search/kibana juggling, data cleaning, counting, posing questions and searching for answers in the data. We would like to share some results on this blog.
The project was called “DIGITAL COMMUNICATION STRATEGIES FOR THE CULTURAL SECTOR IN THE BODENSEE REGION”, in which the data an ..read more
Data Science made in Switzerland
4y ago
By Nico Ebert (ZHAW)
cross-posted from WINsights blog
Each of us is confronted with countless privacy notices every day and agrees to the practices described. Most likely we do not even notice this because the privacy information is hidden in long and cumbersome privacy policies. In order to inform users more specifically with more relevant information about privacy, it is first necessary to understand which information is relevant to users at all. Marketing traditionally asks users about their needs, so why not ask users about their needs for privacy information?
Researchers have recently su ..read more
Data Science made in Switzerland
4y ago
By Christoph Heitz (ZHAW)
translated from original German language version published at Inside IT
Can a prisoner be released early, or released on
bail? A judge who decides this should also consider the risk of
recidivism of the person to be released. Wouldn’t it be an
advantage to be able to assess this risk objectively and reliably?
This was the idea behind the COMPAS system developed by the US
company Northpoint.
The
system makes an individual prediction of the chance of recidivism for
imprisoned offenders, based on a wide range of personal data. The
result is a risk score between ..read more
Data Science made in Switzerland
4y ago
by Fernando Benites (ZHAW and SpinningBytes)
cross-posted from github
We explain here, step by step, how to reproduce results of the approach and discuss parts of the paper. The approach was aimed at building a strong baseline for the task, which should be beaten by deep learning approaches, but we did not achieve that, so we submitted this baseline, and got second in the flat problem and 1st in the hierarchical task (subtask B). This baseline builds on strong placements in different shared tasks, and although it only is a clever way for keyword spotting, it performs a very good ..read more
Data Science made in Switzerland
5y ago
by Fernando Benites (ZHAW and SpinningBytes)
cross-posted from the SpinningBytes blog
schwiiz ja*
This year, the SpinningBytes team participated in the VarDial competition, where we achieved second place in the German Dialect Identification shared task. The task’s goal was to identify, which region the speaker of a given sentence is from, based on the dialect he or she speaks. Dialect identification is an important NLP task; for instance, it can be used for automatic processing in a speech-to-text context, where identifying dialects enables to load a specialized model. In this blog ..read more
Data Science made in Switzerland
5y ago
Kurt Stockinger was invited to contribute a blog to ACM SIGMOD – the leading world-wide community of database research. The blog discusses recent technological advances of natural language interfaces to databases. The ultimate goal is to talk to a database (almost) like to a human.
The full blog can be found on the following ACM SIGMOD link:
The Rise of Natural Language Interfaces to Databases ..read more
Data Science made in Switzerland
5y ago
By Nico Ebert (ZHAW)
The original version of this post was published in German on Privacy Bits and English on vetri.global
In a lecture for the Fair Data Forum, I dealt with the question “What value does data protection have for individuals and what are they willing to pay for it?”
The three data privacy types
As always, there is not one
“individual”, as everyone has different data protection preferences
and thus, attributes different value to having personal data safeguarded. Therefore,
in order to classify individuals, there are different “typologies”. For
example, Westin disti ..read more