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291. Sowing the Seeds for More Usable Web Archives: A Usability Study of Archive-It

Author(s):
Abrams, Samantha; Antracoli, Alexis; Appel, Rachel; Caust-Ellenbogen, Celia; Dennison, Sarah; Duncan, Sumitra; Ramsay, Stefanie
Abstract:
In 2017, seven members of the Archive-It Mid-Atlantic Users Group (AITMA) conducted a study of 14 subjects representative of their stakeholder populations to assess the usability of Archive-It, a web archiving subscription service of the Internet Archive. While Archive-It is the most widely-used tool for web archiving, little is known about how users interact with the service. This study intended to teach us what users expect from web archives, which exist as another form of archival material. End-user subjects executed four search tasks using the public Archive-It interface and the Wayback Machine to access archived information on websites from the facilitators’ own harvested collections and provide feedback about their experiences. The tasks were designed to have straightforward pass or fail outcomes, and the facilitators took notes on the subjects’ behavior and commentary during the sessions. Overall, participants reported mildly positive impressions of Archive-It public user interface based on their session. The study identified several key areas of improvement for the Archive-It service pertaining to metadata options, terminology display, indexing of dates, and the site’s search box.
Type:
Dataset
Issue Date:
2019

300. Fast animal pose estimation using deep neural networks

Author(s):
Pereira, Talmo D.; Aldarondo, Diego E.; Willmore, Lindsay; Kislin, Mikhail; Wang, Samuel S.-H.; Murthy, Mala; Shaevitz, Joshua W.
Abstract:
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.
Type:
Dataset
Issue Date:
30 May 2018