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Twist Bytes @Vardial 2018

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 post, we do a step by step walkthrough how to create the model in Python, while comparing it to previous years’ approaches.

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The Rise of Natural Language Interfaces to Databases

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:

What is the value of data privacy?

By Nico Ebert (ZHAW)

The original version of this post was published in German on Privacy Bits and English on

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 distinguishes between data protection fundamentalists, data protection pragmatists and completely unconcerned individuals. In 2002, Sheehan (2002) selected 889 persons in the USA and classified them with a questionnaire. Conclusion: 16% of the respondents were completely unconcerned about data protection, 81% were classified as pragmatists, and 3% as fundamentalists.

Willingness to pay vs willingness to accept

Numerous experiments were carried out to find out which types of people are willing to pay for data privacy and which ones are willing to share their data freely with third parties. In these experiments, the “willingness to accept” or the “willingness to pay” were analyzed. Willingness to accept describes the reward that must be offered to an individual, so that she/he is willing to share personal data (usually monetary compensation or services). The willingness to pay examines how much the individual is willing to pay for data protection (compensation for data protection).

What the studies say

In 2017, an experiment with 3000 students from a US university concluded that a pizza was a sufficient incentive for them to share e-mail addresses of three fellow students. Conversely, the students were rarely willing to accept a small additional expense for better data protection.

A different result was produced by an experiment published by Tsai et al. in 2011, where the willingness to pay was investigated. In the experiment, 272 participants were recruited from the population. The individuals received a sum of money to be used to shop in a laboratory setting. By using a search engine provided by the University, participants were asked to select a suitable provider for a) batteries (a good with little privacy concerns) and b) sex toys (a good with stronger privacy concerns) and to purchase an item. The search engine displayed the available products and their prices in the form of a list. However, some participants were additionally presented with a data protection rating of the seller (e.g. 4/4 stars or 1/4 stars). Conclusion: many participants chose a seller with a better rating and were willing to pay more for products with a high data protection rating. These results could be replicated in two further studies.

A third experiment was published in 2013, in which the willingness to pay was examined. In an American women’s clothing store, 349 women were randomly interviewed for a survey on spending behavior. As a reward, the participants were offered a voucher which they could use to shop. Two types of voucher were offered: an anonymous USD 10 voucher (A) and a USD 12 voucher (B), where the purchases were not anonymous, i.e. could be traced by third parties. Groups of participants were presented the two vouchers in different orders (A first or B first). The experiment showed that the willingness to pay (i.e. to accept the USD 2 reduction from B to A for an increase in privacy) strongly depends on the sequence in which the two options are presented, suggesting the existence of a strong endowment effect.

Main findings

The main finding from the three studies is that willingness to pay depends on various factors. Summarized, it depends very much on a) who is addressed but also b) how the individual is addressed. Individuals which are more sensitive about their data are probably willing to pay more than insensitive individuals. However, many individuals will not have a clear data protection preference that holds true in all situations. Under certain circumstances, influencing factors are whether a) the added value of data protection is communicated in an understandable way (e.g. via simple ratings or a “non-traceable” USD 10 voucher) or b) whether data protection is the standard option. If data protection is the default setting, individuals may forgo compensation, which they would receive for abandoning data protection.


Athey, S., Catalini, C., & Tucker, C. (2017). The digital privacy paradox: small money, small costs, small talk (No. w23488). National Bureau of Economic Research.

Acquisti, A., Taylor, C., & Wagman, L. (2016). The economics of privacy. Journal of Economic Literature, 54(2), 442-92.

Acquisti, A., John, L. K., & Loewenstein, G. (2013). What is privacy worth?. The Journal of Legal Studies, 42(2), 249-274.

Beresford, Alastair R., Dorothea Kübler, and Sören Preibusch. 2012. Unwillingness to Pay for Privacy: A Field Experiment. Economics Letters 117:25–27.

Jentzsch, Nicola, Sören Preibusch, and Andreas Harasser. 2012. Study on Monetising Privacy: An Economic Model for Pricing Personal Information. Report for the European Network and Information Security Agency. Heraklion: European Network and Information Security Agency.

Kumaraguru, P., & Cranor, L. F. (2005). Privacy indexes: a survey of Westin’s studies (pp. 368-394). Carnegie Mellon University, School of Computer Science, Institute for Software Research International.

Sheehan, K. B. (2002). Toward a typology of Internet users and online privacy concerns. The Information Society, 18(1), 21-32.

Tsai, J. Y., Egelman, S., Cranor, L., & Acquisti, A. (2011). The effect of online privacy information on purchasing behavior: An experimental study. Information Systems Research, 22(2), 254-268.

ZHAW Datalab organizes Data Science Event in Silicon Valley

By Kurt Stockinger (ZHAW)

As part of “Zürich meets San Francisco – A Festival Of Two Cities”, the ZHAW Datalab co-organized the event Data Science and Beyond: Technical, Economic and Societal Challenges, which took place at the campus of San José State University (SJSU) – in the heart of Silicon Valley. One interesting fact about SJSU is that it has the highest number of graduates among all US universities that get jobs either at Apple or Cisco.

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PhD Network in Data Science: Website launched

The aim of the PhD Network in Data Science is to offer students with a master degree (including degrees from an university of applied sciences) the opportunity to obtain a PhD in cooperation between a university of applied sciences and a university.

The PhD Network in Data Science is supported by Swissuniversities. It is a cooperation between three departments of ZHAW Zurich University of Applied Sciences (School of Management and Law, Life Science and Facility Management, School of Engineering), three departments of the University of Zurich (Faculty of Science, Faculty of Business, Economics and Informatics, Faculty of Arts and Social Sciences), the Faculty of Science at the University of Neuchatel and the Department of Innovative Technologies at SUPSI University of Applied Sciences and Arts of Southern Switzerland.

PhD students work in applied research projects at the university of applied sciences and are supervised jointly by a supervisor at the university and a co-supervisor at the university of applied sciences. They are enrolled in the regular PhD programs of the partner universities and have to go through the standard admission procedure. After successful completion they receive the doctorate of the respective partner university (UZH or UNIBE). The PhD Network is also open to students with a master’s degree from a university of applied sciences. They, however, have to go through convergence programs (specific to the respective faculty) for admission to the partner universities.

You can find more information on our new website!

Book Review: Paul D. Ellis, The Essential Guide to Effect Sizes

Reviewed by Thoralf Mildenberger (ZHAW)

  • Paul. D. Ellis, The Essential Guide to Effect Sizes. Statistical Power, Meta-Analysis and the Interpretation of Research Results. Cambridge University Press, Cambridge 2010. Link to book on publisher’s website.

In the last few years, statistical hypothesis testing – with the p-value still being THE standard for reporting results in many fields of science – has increasingly been criticized. Many researchers have even called for abandoning the “NHST” (Null Hypothesis Significance Testing) approach all together. I think this is going too far as many problems are due to misapplication of the techniques and – perhaps even more importantly – misinterpretation of the results. There is also no consensus on how to replace hypothesis testing with a better methodology – some of the more moderate critics suggest using confidence intervals, but while these are often more informative they are essentially equivalent to hypothesis tests and share some of the problems. This makes it all the more important to highlight difficulties in the correct application and interpretation of statistical methodology. Continue reading

Study on “Quantified Self” Published: Links to Book and Summary

By Kurt Stockinger (ZHAW)

The final results of an interdisciplinary study funded by „TA Swiss“ on „Quantified Self“ with participation of the Datalab have been published. The study was performed by three ZHAW departments (School of Health Professions, School of Management and Law, School of Engineering) in cooperation with the Institute for Futures Studies and Technology Assessment, Berlin. The focus of the Datalab was on legal and Big Data aspects of quantified self.

The results are available in various forms:

Enjoy reading and maybe you get encouraged to “quantify yourself” a bit better 😉

PhD Network in Data Science

By Dirk Wilhelm (ZHAW)

Reposted from

Studierende können nun an der ZHAW in Kooperation mit der Universität Zürich oder der Universität Neuenburg im Bereich Data Science doktorieren. Continue reading

Artificial Intelligence in Industry and Finance

2nd European COST Conference on Mathematics for Industry in Switzerland
September 7, 2017
Zurich University of Applied Sciences,
Technikumstr. 71, 8400 Winterthur

By Jörg Osterrieder (ZHAW)

Below please find a short recap and an outlook for our next conference on September 6, 2018.

Aim of the conference

The aim of this conference was to bring together European academics, young researchers, students and industrial practitioners to discuss the application of Artificial Intelligence to various practical fields. In a broader context, we wanted to promote «Mathematics for Industry» in Switzerland, as part of the European COST (Cooperation in Science and Technology) Action “Mathematics for Industry”, where members of ZHAW are in the management committee for Switzerland. Continue reading

R: Reduce() Part 2 – some pitfalls using Reduce

By Matthias Templ (ZHAW), Thoralf Mildenberger (ZHAW)

By way of example, functionality of Reduce() is shown in in . It’s great to learn about how to use this function on interesting problems. If you are ready (equals if you read the first blog post on Reduce), we want to push you further on writing efficient code. Continue reading

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