EntertainmentAI: New Ways of Engaging with Video @CognitiveAI Meetup #CAIM

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Lora Aroyo

http://thepostersession.com/ http://tagasauris.com

Technology

for HBO we discover a rich set of people, objects, actions, locations, and narrations
from 7,571 minutes of programming across 175 episodes and 10 series
Movies and TV are a rich source of diverse and complex video
with people, objects, actions, locations and narrations “in the wild”
in a symbiotic pipeline with humans & machines in the loop
tagasauris.com
Entertainment-AI
SEMANTIC ENRICHMENT OF MOVIES & TV SERIES

Entertainment-AI
DISCOVER VIDEO MICRO-MOMENTS
tagasauris.com

Entertainment-AI
COMMERCIALIZING VIDEO MICRO-MOMENTS
AI Factory*
Augmented Intelligence
& Data Network
Effects. *U.S. Patent
#13/863,751
DYNAMIC SEMANTIC PUBLISHING STACK
Linked Media, technology
architecture, modeled on
state-of-the-art research,
combining both human &
artificial intelligence

Presentation at ISWC2018: http://iswc2018.semanticweb.org/sessions/the-rijksmuseum-collection-as-linked-data/ of our paper published originally in the Semantic Web Journal: http://www.semantic-web-journal.net/content/rijksmuseum-collection-linked-data-2 Many museums are currently providing online access to their collections. The state of the art research in the last decade shows that it is beneficial for institutions to provide their datasets as Linked Data in order to achieve easy cross-referencing, interlinking and integration. In this paper, we present the Rijksmuseum linked dataset (accessible at http://datahub.io/dataset/rijksmuseum), along with collection and vocabulary statistics, as well as lessons learned from the process of converting the collection to Linked Data. The version of March 2016 contains over 350,000 objects, including detailed descriptions and high-quality images released under a public domain license.

Keynote at International Conference of Art Libraries 2018 @Rijksmuseum

Lora Aroyo

Presentation at the NYC Media Lab (NYCML2018). There is a growing demand for news videos online, with more consumers preferring to watch the news than read or listen to it. On the publisher side, there is a growing effort to use video summarization technology in order to create easy-to-consume previews (trailers) for different types of broadcast programs. How can we measure the quality of video summaries and their potential to misinform? This workshop will inform participants about automatic video summarization algorithms and how to produce more “representative” video summaries. The research presented is from the FAIRview project and is supported by the Digital News Innovation Fund (DNI Fund), which is part of the Google News Initiative.

Understanding bias in video news & news filtering algorithms

Lora Aroyo

StorySourcing: Telling Stories with Humans & Machines

Lora Aroyo

Data Science with Humans in the Loop

Lora Aroyo

Digital Humanities Benelux 2017: Keynote Lora Aroyo

Lora Aroyo

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Lora Aroyo

Crowdsourcing ambiguity aware ground truth - collective intelligence 2017

Lora Aroyo

The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to the volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, this assumption often creates issues in practice. Previous experiments we performed found that inter-annotator disagreement is usually never captured, either because the number of annotators is too small to capture the full diversity of opinion, or because the crowd data is aggregated with metrics that enforce consensus, such as majority vote. These practices create artificial data that is neither general nor reflects the ambiguity inherent in the data. To address these issues, we proposed the method for crowdsourcing ground truth by harnessing inter-annotator disagreement. We present an alternative approach for crowdsourcing ground truth data that, instead of enforcing an agreement between annotators, captures the ambiguity inherent in semantic annotation through the use of disagreement-aware metrics for aggregating crowdsourcing responses. Based on this principle, we have implemented the CrowdTruth framework for machine-human computation, that first introduced the disagreement-aware metrics and built a pipeline to process crowdsourcing data with these metrics. In this paper, we apply the CrowdTruth methodology to collect data over a set of diverse tasks: medical relation extraction, Twitter event identification, news event extraction and sound interpretation. We prove that capturing disagreement is essential for acquiring a high-quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with a majority vote, a method which enforces consensus among annotators. By applying our analysis over a set of diverse tasks we show that, even though ambiguity manifests differently depending on the task, our theory of inter-annotator disagreement as a property of ambiguity is generalizable.

My ESWC 2017 keynote: Disrupting the Semantic Comfort Zone

Lora Aroyo

Ambiguity in interpreting signs is not a new idea, yet the vast majority of research in machine interpretation of signals such as speech, language, images, video, audio, etc., tend to ignore ambiguity. This is evidenced by the fact that metrics for quality of machine understanding rely on a ground truth, in which each instance (a sentence, a photo, a sound clip, etc) is assigned a discrete label, or set of labels, and the machine’s prediction for that instance is compared to the label to determine if it is correct. This determination yields the familiar precision, recall, accuracy, and f-measure metrics, but clearly presupposes that this determination can be made. CrowdTruth is a form of collective intelligence based on a vector representation that accommodates diverse interpretation perspectives and encourages human annotators to disagree with each other, in order to expose latent elements such as ambiguity and worker quality. In other words, CrowdTruth assumes that when annotators disagree on how to label an example, it is because the example is ambiguous, the worker isn’t doing the right thing, or the task itself is not clear. In previous work on CrowdTruth, the focus was on how the disagreement signals from low quality workers and from unclear tasks can be isolated. Recently, we observed that disagreement can also signal ambiguity. The basic hypothesis is that, if workers disagree on the correct label for an example, then it will be more diﬃcult for a machine to classify that example. The elaborate data analysis to determine if the source of the disagreement is ambiguity supports our intuition that low clarity signals ambiguity, while high clarity sentences quite obviously express one or more of the target relations. In this talk I will share the experiences and lessons learned on the path to understanding diversity in human interpretation and the ways to capture it as ground truth to enable machines to deal with such diversity.

Data Science with Human in the Loop @Faculty of Science #Leiden University

Lora Aroyo

Software systems are becoming ever more intelligent and more useful, but the way we interact with these machines too often reveals that they don’t actually understand people. Knowledge Representation and Semantic Web focus on the scientific challenges involved in providing human knowledge in machine-readable form. However, we observe that various types of human knowledge cannot yet be captured by machines, especially when dealing with wide ranges of real-world tasks and contexts. The key scientific challenge is to provide an approach to capturing human knowledge in a way that is scalable and adequate to real-world needs. Human Computation has begun to scientifically study how human intelligence at scale can be used to methodologically improve machine-based knowledge and data management. My research is focusing on understanding human computation for improving how machine-based systems can acquire, capture and harness human knowledge and thus become even more intelligent. In this talk I will show how the CrowdTruth framework (http://crowdtruth.org) facilitates data collection, processing and analytics of human computation knowledge. Some project links: - http://controcurator.org/ - http://crowdtruth.org/ - http://diveproject.beeldengeluid.nl/ - http://vu-amsterdam-web-media-group.github.io/linkflows/

SXSW2017 @NewDutchMedia Talk: Exploration is the New Search

Lora Aroyo

Europeana GA 2016: Harnessing Crowds, Niches & Professionals in the Digital Age

Lora Aroyo

"Video Killed the Radio Star": From MTV to Snapchat

Lora Aroyo

UMAP 2016 Opening Ceremony

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Lora Aroyo

Stitch by Stitch: Annotating Fashion at the Rijksmuseum

Lora Aroyo

https://www.rijksmuseum.nl/en/stitch-by-stitch http://annotate.accurator.nl/ Fashion can be found everywhere in museums. Fashion heritage collected over centuries: costumes, accessories, paintings, prints and photographs. But while some clothes and accessories are easily found and identified, others are obscure and require a trained eye to describe. What are we looking at? What kind of sleeve is this? Which materials and techniques have been used? More specific descriptions of the images facilitate better use of digital collections and enable users to wander through them in detail.

Museums & the Web 2016 Presentation: Enriching Collections with Expert Knowle...

Lora Aroyo

http://mw2016.museumsandtheweb.com/proposal/accurator-enriching-collections-with-expert-knowledge-from-the-crowd/ Crowdsourcing is not a new phenomenon for museums. There are good examples for museums (e.g., Powerhouse museum, steve.museum). But not all crowdsourcing initiatives are successful. Crowdsourced tagging does not always contribute to a better understanding of art and can even be confusing. The Rijksmuseum and the VU University Amsterdam developed the Accurator: a visual tool to get experts in domains like birds, bibles, ships, castles, etc. involved in annotating art and enrich the museums’ metadata with expertise that is not available internally. In this how-to session, we demonstrate the tool and the ways other museums can implement this Open Web application for their own collections.

Keynote @Final NWO CATCH Program Event

Lora Aroyo

Closing Event - Watson Innovation Course

Lora Aroyo

CrowdTruth Games @NLeSc eHumanities day 2015

Lora Aroyo

Semantic Digital Humanities Workshop 2015 @Oxford

Lora Aroyo

Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...

Jeffrey Haguewood

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams. Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

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EntertainmentAI: New Ways of Engaging with Video @CognitiveAI Meetup #CAIM

1. for HBO we discover a rich set of people, objects, actions, locations, and narrations from 7,571 minutes of programming across 175 episodes and 10 series Movies and TV are a rich source of diverse and complex video with people, objects, actions, locations and narrations “in the wild” in a symbiotic pipeline with humans & machines in the loop tagasauris.com Entertainment-AI SEMANTIC ENRICHMENT OF MOVIES & TV SERIES

2. Entertainment-AI DISCOVER VIDEO MICRO-MOMENTS tagasauris.com

3. Entertainment-AI COMMERCIALIZING VIDEO MICRO-MOMENTS AI Factory* Augmented Intelligence & Data Network Effects. *U.S. Patent #13/863,751 DYNAMIC SEMANTIC PUBLISHING STACK Linked Media, technology architecture, modeled on state-of-the-art research, combining both human & artificial intelligence

EntertainmentAI: New Ways of Engaging with Video @CognitiveAI Meetup #CAIM

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