Human computation systems are often the result of extensive lengthy trial-and-error refinements. What we lack is an approach to systematically engineer solutions based on past successful patterns.In this paper we present the CrowdLang1 programming framework for engineering complex computation systems incorporating large crowds of networked humans and machines with a library of known interaction patterns. We evaluate CrowdLang by programming a German-to-English translation program incorporating machine translation and a monolingual crowd. The evaluation shows that CrowdLang is able to simply explore a large design space of possible problem-solving programs with the simple variation of the used abstractions. In an experiment involving 1918 different human actors, we show that the resulting translation program significantly outperforms a pure machine translation in terms of adequacy and fluency whilst translating more than 30 pages per hour and approximates the human-translated gold standard to 75%. Abstract. Human computation systems are often the result of extensive lengthy trial-and-error refinements. What we lack is an approach to systematically engineer solutions based on past successful patterns. In this paper we present the CrowdLang 1 programming framework for engineering complex computation systems incorporating large crowds of networked humans and machines with a library of known interaction patterns. We evaluate CrowdLang by programming a German-to-English translation program incorporating machine translation and a monolingual crowd. The evaluation shows that CrowdLang is able to simply explore a large design space of possible problem-solving programs with the simple variation of the used abstractions. In an experiment involving 1918 different human actors, we show that the resulting translation program significantly outperforms a pure machine translation in terms of adequacy and fluency whilst translating more than 30 pages per hour and approximates the human-translated gold standard to 75%.

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We demonstrate our culturally adaptive system MOCCA, which is able to automatically adapt its visual appearance to the user's national culture. Rather than only adapting to one nationality, MOCCA takes into account a person's current and previous countries of residences, and uses this information to calculate user-specific preferences. In addition, the system is able to learn new, and refine existing adaptation rules from users' manual modifications of the user interface based on a collaborative filtering mechanism, and from observing the user's interaction with the interface. MOCCA -A System That Learns and Recommends Visual Preferences Based on Cultural Similarity ABSTRACTWe demonstrate our culturally adaptive system MOCCA, which is able to automatically adapt its visual appearance to the user's national culture. Rather than only adapting to one nationality, MOCCA takes into account a person's current and previous countries of residences, and uses this information to calculate user-specific preferences. In addition, the system is able to learn new, and refine existing adaptation rules from users' manual modifications of the user interface based on a collaborative filtering mechanism, and from observing the user's interaction with the interface.

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In this paper we present the programming language and framework CrowdLang 1 for engineering complex computation systems incorporating large numbers of networked humans and machines agents. We evaluate CrowdLang by developing a text translation program incorporating human and machine agents. The evaluation shows that we are able to simply explore a large design space of possible problem solving programs with the simple variation of the used abstractions. Furthermore, an experiment, involving 1918 different human actors, shows that the developed mixed human-machine translation program significantly outperforms a pure machine translation in terms of adequacy and fluency whilst translating more than 30 pages per hour and that the program approximates the professional translated gold-standard to 75% using the automatic evaluation metric METEOR. Last but not least, our evaluation illustrates that our new human computation pattern staged-contest with pruning outperforms all other refinements in the translation task.