HumL 2018
The 2nd international Workshop on on Augmenting Intelligence with Humans­-in-­the-­Loop, co-located with The 17th International Semantic Web Conference http://iswc2018.semanticweb.org/
Monterey, California, October 8-12 2018

Workshop website: http://w3id.org/huml/HumL-ISWC2018
Twitter: @HumLworkshop #HumL #HumL2018

*************** Important Dates ***************
Abstract submission: 25 May 2018
Paper submission deadline: 1 June 2018
Author notification: 27 June 2018
Early-bird registrations deadline: 29 June 2018
Final version deadline: 24 July 2018
Workshop date: 9 October 2018

*************** Call for Papers ***************
Human­-in-­the-­loop is a model of interaction where a machine process and one or more humans have an iterative interaction. In this paradigm the user has the ability to heavily influence the outcome of the process by providing feedback to the system as well as the opportunity to grab different perspectives about the underlying domain and understand the step by step machine process leading to a certain outcome. Amongst the current major concerns in Artificial Intelligence research are being able to explain and understand the results as well as avoiding bias in the underlying data that might lead to unfair or unethical conclusions. Typically, computers are fast and accurate in processing vast amounts of data. People, however, are creative and bring in their perspectives and interpretation power. Bringing humans and machines together creates a natural symbiosis for accurate interpretation of data at scale. The goal of this workshop is to bring together researchers and practitioners in various areas of AI (i.e., Machine Learning, NLP, Computational Advertising, etc.) to explore new pathways of the human­-in-­the-loop paradigm.

*************** Research Topics ***************
Topics of interest include, but are not limited to:
* Human Factors:
** Human­-computer cooperative work
** Mobile crowdsourcing applications
** Human Factors in Crowdsourcing
** Social computing
** Ethics of Crowdsourcing
** Gamification techniques
* Data Collection:
** Data annotations task design
** Data collection for specific domains (e.g. with privacy constraints)
** Data privacy
** Multi­-linguality aspects
* Machine Learning:
** Dealing with sparse and noisy annotated data
** Crowdsourcing for Active Learning
** Statistics and learning theory
* Applications:
** Healthcare
** NLP technologies
** Translation
** Data quality control
** Sentiment analysis

*************** Submission ********************
All submissions must be written in English. We accept the following formats of submissions:

Full paper with a maximum of 12 pages including references.
Short paper with a maximum of 6 pages including references.
Two formats are possible for the submission: PDF and HTML. PDF submissions must be formatted according to the information for LNCS Authors (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.). We would like to encourage you to submit your paper as HTML, in which case you need to submit a zip archive containing an HTML file and all used resources. If you are new to HTML submission these are good places to start:

dokieli is a client-side editor for decentralised article publishing, annotations and social interactions. It is compliant with the Linked Research initiative. Example papers using LNCS and ACM: http://csarven.ca/dokieli-rww and on website https://dokie.li/.
Research Articles in Simplified HTML (RASH) format: documentation and stylesheets at https://github.com/essepuntato/rash
In order to check if your HTML submission is compliant with the page limit constraint, please use one of the LNCS layouts and printing/storing it as PDF. Please submit your contributions electronically in PDF or HTML format to EasyChair (https://easychair.org/conferences/?conf=humliswc2018)
Accepted papers will be published online via CEUR-WS.

*************** Workshop Chairs ***************
Lora Aroyo, VU University Amsterdam
Gianluca Demartini, University of Queensland
Anna Lisa Gentile, IBM Research Almaden, US
Chris Welty, Google