To: email@example.com, firstname.lastname@example.org, email@example.com, RobertP@informationhub.biz, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, …
Dear PR professional:
With respect to our joint problem, Stanford researchers have found a solution!
Please see here for the answer: http://www.scs.stanford.edu/~dm/home/papers/remove.pdf
With kind regards,
PS: If the research paper above doesn’t load, please see this copy: http://dirkriehle.com/wp-content/uploads/2016/06/remove.pdf
So the Echo Dot seems like a good addition to a larger apartment or house. In addition, Amazon promises you can order it through your existing Alexa device. So I tried:
Me: “Alexa, order an Echo Dot.”
Echo: “I can only order product for Prime members. So I added Echo Dot to your shopping list. Please get a membership.”
Me: “Alexa, f#$%^@ you.”
Echo: “That’s not very nice to say.”
Me: “OK, how about that: Alexa, scr#$%# you.”
Echo: “Well, thanks for the feedback.”
I may be in the subscription business myself, but I generally try to avoid to be on the receiving end…
I was watching an old TV show rerun with a character in it called Alexa. My Amazon Echo (trigger word is Alexa) was also listening:
TV set: “Alexa, stop doing that!”
Echo: “Sorry, I don’t understand what you are saying.”
TV set (raised voice): “Alexa, don’t talk to me like that!”
Echo: “Sorry, I still don’t understand what you are saying.”
Despite a few more “Alexa, …” it fell quiet.
I’m amused. Ever since I have wondered what a mischievous screen writer could do given that the Echo can control a garden variety of devices in your house. Or order stuff. How about:
Mischievous character in TV show: “Alexa, open the blinds. Alexa, switch on the lights” (probably most effective a 1am or 5am)
Domino avatar on TV show: “Alexa, order 17 frutti di mare pizzas“
The possibilities seem endless.
Abstract: Inner source is an approach to collaboration across intra-organizational boundaries for the creation of shared reusable assets. Prior project reports on inner source suggest improved code reuse and better knowledge sharing. Using a multiple-case case study research approach, we analyze the problems that three major software development organizations were facing in their product line engineering efforts. We find that a root cause, the separation of product units as profit centers from a platform organization as a cost center, leads to delayed deliveries, increased defect rates, and redundant software components. All three organizations assume that inner source can help solve these problems. The article analyzes the expectations that these companies were having towards inner source and the problems they were experiencing in its adoption. Finally, the article presents our conclusions on how these organizations should adapt their existing engineering efforts.
Keywords: Inner source, inner source foundation, product-line engineering, software platforms, engineering productivity
Reference: Riehle, D., Capraro, M., Kips, D., & Horn, L. (2016). Inner Source in Platform-Based Product Engineering. IEEE Transactions on Software Engineering, to appear.
The paper is available as a PDF file.
Abstract: In qualitative research, results often emerge through an analysis process called coding. A common measure of validity of theories built through qualitative research is the agreement between different people coding the same materials. High intercoder agreement indicates that the findings are derived from the data as opposed to being relative results based on the original researcher’s bias. However, measuring such intercoder agreement incurs the high cost of having additional researchers perform seemingly redundant work. In this paper we present first results on a novel method of using students for validating theories. We find that intercoder agreement between a large number of students is almost as good as the intercoder agreement between two professionals working on the same materials.
Keywords: Qualitative Data Analysis, Theory Triangulation, Intercoder Agreement, Distributed Coding, Collective Coding
Reference: Andreas Kaufmann, Ann Barcomb and Dirk Riehle. “Using Students as a Distributed Coding Team for Validation through Intercoder Agreement.” Friedrich-Alexander-Universität Erlangen-Nürnberg, Dept. of Computer Science, Technical Reports, CS-2016–01, April 2016.
The paper is available as a local PDF file and also on FAU’s OPUS server.
Abstract: Dieses Projektkonzept schildert, wie Hochschulen mit Unternehmen Projekte mit Studierenden zu beidseitigem Gewinn durchführen können. Unternehmen profitieren durch Recruiting, Outsourcing und Innovation („ROI“), welche sich durch die Projekte ergeben. Hochschulen gewinnen neue Partner, verdienen an den Projekten und bieten attraktivere Lehre.
Keywords: Industrie-Hochschul-Kooperation, Forschungstransfer, Geschäftsmodell
Reference: Dirk Riehle. “Das Uni1 Projektkonzept (2016).” Friedrich-Alexander-Universität Erlangen-Nürnberg, Dept. of Computer Science, Technical Report, CS-2016–04. Erlangen, Germany, 2016.
See also the Uni1 website.
I’m at a loss over the recent reports on the requirement for all research publications to be open access by 2020. Open access means that the research papers are accessible openly without a fee. There are plenty of confusing if not outright wrong statements in the press, but I’m not so much concerned with poor journalism than with the actual proposed policies.
Delegations committed to open access to scientific publications as the option by default by 2020.
I’d like to understand what this means and then how this is supposed work. Specifically, I’d like to know how this is not going to either break free enterprise or make predatory publishers like Elsevier laugh all the way to the bank.