14 no tube dissemination and showcases [compatibility mode]

•

0 gefällt mir•481 views

MODUL Technology GmbH

Technologie

WPs 8, 9
Showcases, Dissemination
Showcases Dissemination
WP Leader: Lyndon Nixon, STI International
with thanks to all partners for their dissemination
activities, and RAI, BBC & Stoneroos for the showcases

Overview of NoTube activities
Overview of NoTube activities
• Web site http://www.notube.tv
eb s te ttp:// . otube.t
– Online and printed Showcases materials
• Other Web channels
• Dissemination activities
– Presentations
– Publications
– Events
• Standards & recommendations
• Outlook post‐NoTube

26‐27 March 2012 NoTube 3rd review 2

Weitere ähnliche Inhalte

Andere mochten auch

A complete guide to Google authorship and Google publisher

Advice Interactive Group

EU Cookie Directive ebook

Krishna De

Google Places Optimization Seopro 2012

Jorge González Bastero

Mobile Search Moments Understanding How Mobile Drives Conversions 3 2013

Dung Tri

Seo local eshow bcn 2013

Jorge González Bastero

Manual Concursos facebook

Beatriz González Pozo

Avatar Technology

lizepape

NoTube: Stoneroos show case

MODUL Technology GmbH

NoTube: Pattern-based Recommendations (part 1)

MODUL Technology GmbH

NoTube: Metadata Interoperability

MODUL Technology GmbH

Exploiting Metrics for Semantic Web Service Discovery

MODUL Technology GmbH

Natal

lindamairy

Integrating social networks into an IPTV recommender system

MODUL Technology GmbH

Computer Brain Power

Neelamani Samal

Digital Atlanta 2013: The Benefits of Google Authorship for Your Website

Mickey Mellen

Final Presentation Social Network Presentaion

lizepape

Avatar Technology

lizepape

Ddo5 Lieven Vermaele 20081017 Vermaele From Digital Dividend To Digital D...

imec.archive

An Unconstrained Dataset for Non-stationary Video Based Fire Detection

Cristiano Rafael Steffens

Andere mochten auch (19)

A complete guide to Google authorship and Google publisher

EU Cookie Directive ebook

Google Places Optimization Seopro 2012

Mobile Search Moments Understanding How Mobile Drives Conversions 3 2013

Seo local eshow bcn 2013

Manual Concursos facebook

Avatar Technology

NoTube: Stoneroos show case

NoTube: Pattern-based Recommendations (part 1)

NoTube: Metadata Interoperability

Exploiting Metrics for Semantic Web Service Discovery

Natal

Integrating social networks into an IPTV recommender system

Computer Brain Power

Digital Atlanta 2013: The Benefits of Google Authorship for Your Website

Final Presentation Social Network Presentaion

Avatar Technology

Ddo5 Lieven Vermaele 20081017 Vermaele From Digital Dividend To Digital D...

An Unconstrained Dataset for Non-stationary Video Based Fire Detection

Mehr von MODUL Technology GmbH

How distinct and aligned with UGC is European capitals’ DMO branding on Insta...

MODUL Technology GmbH

Knowledge Graph Completion (KGC) from text involves identifying known or unknown entities (nodes) as well as relations (edges) among these entities. Recent work has started to explore the use of Large Language Models (LLMs) for entity detection and relation extraction, due to their Natural Language Understanding (NLU) capabilities. However, LLM performance varies across models and depends on the quality of the prompt engineering. We examine specific relation extraction cases and present a set of examples collected from well-known resources in a small corpus. We provide a set of annotations and identify various issues that occur when using different LLMs for this task. As LLMs will remain a focal point of future KGC research, we conclude with suggestions for improving the KGC process.

Framing Few Shot Knowledge Graph Completion with Large Language Models

MODUL Technology GmbH

Transformer models have achieved state-of-the-art results for news classification tasks, but remain difficult to modify to yield the desired class probabilities in a multi-class setting. Using a neural topic model to create dense topic clusters helps with generating these class probabilities. The presented work uses the BERTopic clustered embeddings model as a preprocessor to eliminate documents that do not belong to any distinct cluster or topic. By combining the resulting embeddings with a Sentence Transformer fine-tuned with SetFit, we obtain a prompt-free framework that demonstrates competitive performance even with few-shot labeled data. Our findings show that incorporating BERTopic in the preprocessing stage leads to a notable improvement in the classification accuracy of news documents. Furthermore, our method outperforms hybrid approaches that combine text and images for news document classification.

Unsupervised Topic Modeling with BERTopic for Coarse and Fine-Grained News Cl...

MODUL Technology GmbH

Breaking New Ground with EPOCH: AI and Web Intelligence Transform Price Forec...

MODUL Technology GmbH

New Opportunities for Understanding Tourist Photography.pptx

MODUL Technology GmbH

Destination marketers are aware that online communication about their des-tination is increasingly dependent on visual media rather than text, due to the growing popularity of social networks such as Instagram. An accurate understanding of how the destination is being presented to users in this me-dium is critical for digital marketing activities, e.g. to know if the desired destination brand is present or if visitors focus on other aspects of the desti-nation than those being promoted in marketing. Unlike text mining, which has well established techniques to extract keywords and associations from text corpora, a consistent approach to understanding the content of images and expressing the resulting destination brand is lacking. This paper presents a visual classifier trained and fine-tuned specifically for destination brand measurement from images using 18 visual classes. It presents an exploratory study of how different destinations are being presented visually on Instagram and discusses how these insights could be used by destination marketers to adapt and improve their digital marketing.

How do destinations relate to one another? A study of visual destination bran...

MODUL Technology GmbH

As global travel emerges from the pandemic, pent up interest in travel will lead to consumers making their choice between global destinations. Insta-gram is a key source of destination inspiration. DMO marketing success on this channel relies on projecting a destination image that resonates with this target group. However, usual text-based marketing intelligence on this channel does not work as content is consumed first and foremost as a visual projection. The author has built a deep learning based visual classifier for destination image measure-ment from photos. In this paper, we compare projected and perceived destination images in Instagram photography for four of the most Instagrammed destinations worldwide. We find that whereas the projected destination image aligns well to the perceived image, there are specific aspects of the destinations that are of more interest to Instagrammers than reflected in the current destination marketing.

Do DMOs promote the right aspects of the destination? A study of Instagram ph...

MODUL Technology GmbH

This presentation considers if, and to what extent, visual social media can change the viewer’s perceived image of a tourism destination as well as which types of visual content are most effective in projecting a destination image. The results from an online survey, which compared three different test groups and their image of Mexico City, showed that UGC images from Instagram, as well as random Google images, were more effective at improving destination image than the UGC images reposted by a DMO. Additionally, the study used image annotations to determine which features in images were most important in terms of their contribution to an improvement in overall destination image, presenting a re-usable set of visual features for future work on using annotations in the measurement of visual destination image.

The Impact of Social Media on perceived Destination Image: case of Mexico Ci...

MODUL Technology GmbH

This presentation considers if, and to what extent, social media can change the viewer’s image of a tourism destination as well as which types of visual content are most effective. The results from an online survey, which compared three different test groups and their image of Mexico City, showed that UGC images from Instagram, as well as random Google images, were more effective at improving destination image than UGC images reposted by a DMO. Additionally, the study used image annotation to determine which features in images were most important in terms of their contribution to an improvement in overall destination image.

The Impact of Social Media on perceived Destination Image:the case of Mexico...

MODUL Technology GmbH

The Social Web is increasingly taking up the daily time of consumers and is becoming a primary source of impressions about tourism destinations. The recent shift towards more visual content, as evidenced in the fast growing social network Instagram being largely a photo sharing site, means that DMOs need to consider how photos of their destinations can be influencing consumers’ destination image. We present what may be the first study on how the selection of images from a social media site (Instagram) to promote a destination can be used to influence destination image. As a basis for our study, we have selected the DMO channels of Jordan and Costa Rica in Instagram. Through focus groups and a Likert-scale survey, we draw first conclusions on which types of photos are most effective to positively promote a destination and how the consumers’ previous image of a destination could affect this

How Instagram influences Visual Destination Image - a case study of Jordan an...

MODUL Technology GmbH

I address the rapid increase in non-textual content being shared online around tourism destinations and how this necesitates new media technologies for tourism stakeholders such as DMOs. Current platforms for "tourism intelligence" (providing actionable insights to tourism marketers based on online analysis of the discussions and content around their destinations) rely on text; to add images and videos at scale we would need accurate machine annotation. My talk will provide initial insights into this field of study and hopefully encourage a greater consideration of how to handle multimedia in future tourism research.

Media mining for smarter tourism

MODUL Technology GmbH

NoTube: Pattern-based Recommendations (part 3)

MODUL Technology GmbH

NoTube: Pattern-based Recommendations (part 1)

MODUL Technology GmbH

NoTube: Recommendations (Collaborative)

MODUL Technology GmbH

NoTube: User Profiling (Beancounter)

MODUL Technology GmbH

NoTube: BBC show case

MODUL Technology GmbH

NoTube: RAI Show Case

MODUL Technology GmbH

NoTube: Architecture

MODUL Technology GmbH

NoTube: Loudness Normalisation

MODUL Technology GmbH

NoTube: Ad Insertion [compatibility mode]

MODUL Technology GmbH

Mehr von MODUL Technology GmbH (20)