Gianluca is back! (or: PhD course on Micro-task crowdsourcing)

It is with much pleasure that I welcome Gianluca Demartini back at Udine University, Department of Mathematics and Computer Science, for a PhD course on Micro-task crowdsourcing. Gianluca got his Master's degree under my supervision some years ago (don't ask!) and since then he has obtained several research positions abroad. He is now Lecturer at the University of Sheffield.

The lecture timetable is below, together with a tentative program; everyone is welcome!

Timetable (all lectures are in the "Aula Multimediale / DIMI"):

1. Monday 15/6 10:00 - 12:00
2. Monday 15/6 15:00 - 17:00
3. Tuesday 16/6 10:00 - 12:00
4. Tuesday 16/6 15:00 - 17:00
5. Wednesday 17/6 10:00 - 12:00
6. Wednesday 17/6 15:00 - 17:00
7. Thursday 18/6 10:00 - 12:00

Preliminary/tentative program:

Lecture 1 - Introduction to Crowdsourcing
We will start with an overview of the entire module highlighting its aims and objectives. Then, we will look at fundamental definitions and different types of crowdsourcing incentives. Finally, we will present early examples of crowdsourcing such as reCAPTCHA and the ESP game.

Lecture 2 - Introduction to Micro-task Crowdsourcing Platforms
After defining the key terminology of micro-task crowdsourcing, we will introduce popular crowdsourcing platforms such as Amazon MTurk and CrowdFlower including a demonstration on how to use such systems both as a crowd worker as well as a requester.

Lecture 3 - How to Setup a Crowdsourcing Task
In this lecture we will discuss all the dimensions involved in crowdsourcing task design such as pricing, question design, and quality assurance mechanisms (e.g., honeypots). We will also design and deploy a task during the lecture and see how to collect results back from the crowdsourcing platform.

Lecture 4 - Crowdsourcing Patterns
In this lecture we will define the concept of crowdsourcing pattern (i.e., the combination of multiple crowdsourcing tasks) and present popular example patterns. We will also discuss the concept of crowdsourcing workflows where multiple tasks as well as machine processing steps are combined together.

Lecture 5 - Hybrid Human-machine Systems
In this lecture we will see some advanced example uses of crowdsourcing applied to the database, web, and biomedical domains. We will see how systems that combine both the scalability of machines over large amounts of data as well as the quality of human intelligence can be used to improve the effectiveness of Big Data systems.

Lecture 6 - Crowdsourcing Scalability
In hybrid human-machine systems the latency bottleneck lays on the side of the crowd as human intelligence is naturally slower than machine-based computation. In this lecture we will see recent research results that proposed techniques to improve the latency of crowdsourcing platforms.

Lecture 7 - Open Research Directions in Crowdsourcing
In this lecture we will give an overview on which micro-task crowdsourcing research questions different Computer Science areas focus on including database, information retrieval, semantic web, human-computer interaction, multimedia, and bio-informatics.

Prerequisites:
No prior knowledge is needed. Having at least basic programming skills is a plus.

S.