Thursday, December 21, 2017

Challenge : Pover-T Tests: Predicting Poverty

https://www.drivendata.org/competitions/50/worldbank-poverty-prediction/page/97/

Predicting Poverty - World Bank

The World Bank is aiming to end extreme poverty by 2030. Crucial to this goal are techniques for determining which poverty reduction strategies work and which ones do not. But measuring poverty reduction requires measuring poverty in the first place, and it turns out that measuring poverty is pretty hard. The World Bank helps developing countries measure poverty by conducting in-depth household surveys with a subset of the country's population. To measure poverty, most of these surveys collect detailed data on household consumption – everything from food and transportation habits to healthcare access and sporting events – in order to get a clearer picture of a household's poverty status.
Can you harness the power of these data to identify the strongest predictors of poverty? Right now measuring poverty is hard, time consuming, and expensive. By building better models, we can run surveys with fewer, more targeted questions that rapidly and cheaply measure the effectiveness of new policies and interventions. The more accurate our models, the more accurately we can target interventions and iterate on policies, maixmizing the impact and cost-effectiveness of these strategies.

Machine Tagging Challenge

https://www.innocentive.com/ar/challenge/9934063

The Seeker desires an algorithm to map customer-supplied tag names for data sources onto Seeker-defined Standard Tags. The algorithm must perform the mapping operation without human intervention and must be able to adapt to new data as it becomes available.
This is a Reduction-to-Practice Challenge that requires written documentation, output from the algorithm, and submission of working software and source code implementing the solution.


Wednesday, December 20, 2017

WWW 2018 Challenge: Learning to Recognize Musical Genre

https://www.crowdai.org/challenges/www-2018-challenge-learning-to-recognize-musical-genre

Overview

Like never before, the web has become a place for sharing creative work - such as music - among a global community of artists and art lovers. While music and music collections predate the web, the web enabled much larger scale collections. Whereas people used to own a handful of vinyls or CDs, they nowadays have instant access to the whole of published musical content via online platforms. Such dramatic increase in the size of music collections created two challenges: (i) the need to automatically organize a collection (as users and publishers cannot manage them manually anymore), and (ii) the need to automatically recommend new songs to a user knowing his listening habits. An underlying task in both those challenges is to be able to group songs in semantic categories.
Music genres are categories that have arisen through a complex interplay of cultures, artists, and market forces to characterize similarities between compositions and organize music collections. Yet, the boundaries between genres still remain fuzzy, making the problem of music genre recognition (MGR) a nontrivial task (Scaringella 2006). While its utility has been debated, mostly because of its ambiguity and cultural definition, it is widely used and understood by end-users who find it useful to discuss musical categories (McKay 2006). As such, it is one of the most researched areas in the Music Information Retrieval (MIR) field (Sturm 2012).
The task of this challenge, one of the four official challenges of the Web Conference (WWW2018) challenges track, is to recognize the musical genre of a piece of music of which only a recording is available. Genres are broad, e.g. pop or rock, and each song only has one target genre. The data for this challenge comes from the recently published FMA dataset (Defferrard 2017), which is a dump of the Free Music Archive (FMA), an interactive library of high-quality and curated audio which is freely and openly available to the public.

Friday, December 15, 2017

Visual Domain Decathlon Challenge

http://www.robots.ox.ac.uk/~vgg/decathlon/

Part of PASCAL in Detail Workshop Challenge, CVPR 2017, July 26th, Honolulu, Hawaii, USA
This taster challenge tests the ability of visual recognition algorithms to cope with (or take advantage of) many different visual domains.