Tufte.pdf
3.9 MB
Про дата саенс, визуализацию и эпидемиологию в 19 веке 🙃
Вкратце — https://en.m.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak
Вкратце — https://en.m.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak
https://www.bigsurv20.org
This conference is a meeting place for computer and data scientists with an interest in social science and data collection and for social scientists, survey methodologists and statisticians with an interest in computer and data science.
This conference is a meeting place for computer and data scientists with an interest in social science and data collection and for social scientists, survey methodologists and statisticians with an interest in computer and data science.
Operations Research, also called Decision Science or Operations Analysis, is the study of applying mathematics to business questions. As a sub-field of Applied Mathematics, it has a very interesting position alongside other fields as Data Science and Machine Learning.
Tools from Google
https://developers.google.com/optimization
Tools from Google
https://developers.google.com/optimization
Google for Developers
OR-Tools | Google for Developers
The OR-Tools suite provides operations research software libraries and APIs for constraint optimization, linear optimization, and flow and graph algorithms.
WES: Agent-based User Interaction Simulation on Real Infrastructure
https://research.fb.com/publications/wes-agent-based-user-interaction-simulation-on-real-infrastructure/
https://research.fb.com/publications/wes-agent-based-user-interaction-simulation-on-real-infrastructure/
Meta Research
WES: Agent-based User Interaction Simulation on Real Infrastructure - Meta Research
We introduce the Web-Enabled Simulation (WES) research agenda, and describe FACEBOOK’s WW system. We describe the application of WW to reliability, integrity and privacy at FACEBOOK1, where it is used to simulate social media interactions on an infrastructure…
3334480.3375231.pdf
809.7 KB
Adapting User Experience Research Methods for AI-Driven Experiences
https://dl.acm.org/doi/abs/10.1145/3334480.3375231
https://dl.acm.org/doi/abs/10.1145/3334480.3375231
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This short paper describes how to adapt user experience research methods for artificial intelligence (AI)-driven applications. Presently, there is a dearth of guidance for conducting UX research on AI-driven experiences. We describe what makes this class of experiences unique, propose a preliminary foundational framework to categorize AI-driven experiences, and within the framework we show an example of methodological adaptations via a case study.