Our team participated in two tasks for CLEF 2022. This repository contains the resources we used through the course of the competition. It contains the following:
Summaries of the papers we read
Exploratory Data Analysis code
Machine Learning & Deep Learning code
Output files
Images constitute a large part of the content shared on social networks. Their disclosure is often related to a particular context and users are often unaware of the fact that, depending on their privacy status, images can be accessible to third parties and be used for purposes which were initially unforeseen. For instance, it is common practice for employers to search information about their future employees online. Another example of usage is that of automatic credit scoring based on online data. Most existing approaches which propose feedback about shared data focus on inferring user characteristics and their practical utility is rather limited.
We hypothesize that user feedback would be more efficient if conveyed through the real-life effects of data sharing. The objective of the task is to automatically score user photographic profiles in a series of situations with strong impact on her/his life. Four such situations were modeled this year and refer to searching for: (1) a bank loan, (2) an accommodation, (3) a job as waitress/waiter and (4) a job in IT. The inclusion of several situations is interesting in order to make it clear to the end users of the system that the same image will be interpreted differently depending on the context.
The final objective of the task is to encourage the development of efficient user feedback, such as the YDSYO Android app.
As the “extinction capital of the world,” Hawai'i has lost 68% of its bird species, the consequences of which can harm entire food chains. Researchers use population monitoring to understand how native birds react to changes in the environment and conservation efforts. But many of the remaining birds across the islands are isolated in difficult-to-access, high-elevation habitats. With physical monitoring difficult, scientists have turned to sound recordings. Known as bioacoustic monitoring, this approach could provide a passive, low labor, and cost-effective strategy for studying endangered bird populations.
Current methods for processing large bioacoustic datasets involve manual annotation of each recording. This requires specialized training and prohibitively large amounts of time. Thankfully, recent advances in machine learning have made it possible to automatically identify bird songs for common species with ample training data. However, it remains challenging to develop such tools for rare and endangered species, such as those in Hawai'i.
The Cornell Lab of Ornithology's K. Lisa Yang Center for Conservation Bioacoustics (KLY-CCB) develops and applies innovative conservation technologies across multiple ecological scales to inspire and inform the conservation of wildlife and habitats. KLY-CCB does this by collecting and interpreting sounds in nature and they've joined forces with Google Bioacoustics Group, LifeCLEF, Listening Observatory for Hawaiian Ecosystems (LOHE) Bioacoustics Lab at the University of Hawai'i at Hilo, and Xeno-Canto for this competition.
In this competition, you’ll use your machine learning skills to identify bird species by sound. Specifically, you'll develop a model that can process continuous audio data and then acoustically recognize the species. The best entries will be able to train reliable classifiers with limited training data.
If successful, you'll help advance the science of bioacoustics and support ongoing research to protect endangered Hawaiian birds. Thanks to your innovations, it will be easier for researchers and conservation practitioners to accurately survey population trends. They'll be able to regularly and more effectively evaluate threats and adjust their conservation actions.