Video assets from archives or online platforms can provide relevant content for embedding into immersive scenes or for generation of 3D objects or scenes. However, XR content creators lack tools to find relevant video segments for their chosen topic. In this paper, we explore the use case of journalists creating immersive experiences for news stories and their need to find related video material to create and populate a 3D scene. An innovative approach creates text and video embeddings and matches textual input queries to relevant video shots. This is provided via a Web dashboard for search and retrieval across video collections, with selected shots forming the input to content creation tools to generate and populate an immersive scene, meaning journalists do not need specialist knowledge to communicate stories via XR.

This demonstration will show how video shot discovery through joint text-video embedding has been integrated into a journalistic workflow through a news monitoring dashboard with the purpose of identifying suitable video material for the creation of immersive scenes around a chosen news story or topic.

This paper explores the current landscape of visualizing large language models (LLMs). The main objective was threefold. Firstly, we investigate how we can visualize LLM-specific techniques such as prompt engineering, instruction tuning, or guidance. Secondly, LLM causality, interpretability, and explainability are examined through visualization. And finally, we showcase the role of visualization in illuminating the integration of multiple modalities. We are interested in discovering the papers that present visualization systems instead of those that use visualization to showcase a part of their work. Our survey aims to synthesize the state-of-the-art in LLM visualization, offering a compact resource for exploring future research avenues.

Abstract To optimise digital content marketing for broadcasters, the Horizon 2020 funded ReTV project developed an end-to-end process termed “Trans-Vector Publishing” and made it accessible through a Web-based tool termed “Content Wizard”. This paper presents this tool with a focus on each of the innovations in data and AI-driven media analysis to address each key step in the digital content marketing workflow: topic selection, content search and video summarisation. First, we use predictive analytics over online data to identify topics the target audience will give the most attention to at a future time. Second, we use neural networks and embeddings to find the video asset closest in content to the identified topic. Third, we use a GAN to create an optimally summarised form of that video for publication, e.g. on social networks. The result is a new and innovative digital content marketing workflow which meets the needs of media organisations in this age of interactive online media where content is transient, malleable and ubiquitous.

In order to optimise the distribution of video assets online, media organizations need tailor their offerings for specific digital channels and better understand the interests of their audiences at particular points in time, which are often influenced by contemporary new stories and trends on social media. For this purpose, the research project ReTV has developed a Web-based tool termed ’Content Wizard’ which demonstrates an end-to-end, semi-automated workflow for video content creation, adaptation and distribution across digital channels. Digital assets can be selected based on predicted future trending topics, re-purposed according to the different digital channels they will be published upon and scheduled for the optimal future publication date. The result is an innovative video publication workflow that meets the marketing needs of media organisations in this age of transient online media spread across multiple channels.

Annotation styles express guidelines that direct human annotators in what rules to follow when creating gold standard annotations of text corpora. These guidelines not only shape the gold standards they help create, but also influence the training and evaluation of Named Entity Linking (NEL) tools, since different annotation styles correspond to divergent views on the entities present in the same texts. Such divergence is particularly present in texts from the media domain that contain references to creative works. In this work we present a corpus of 1000 annotated documents selected from the media domain. Each document is presented with multiple gold standard annotations representing various annotation styles. This corpus is used to evaluate a series of Named Entity Linking tools in order to understand the impact of the differences in annotation styles on the reported accuracy when processing highly ambiguous entities such as names of creative works. Relaxed annotation guidelines that include overlap styles lead to better results across all tools.

Technological developments in comprehensive video understanding - detecting and identifying visual elements of a scene, combined with audio understanding (music, speech), as well as aligned with textual information such as captions, subtitles, etc. and background knowledge - have been undergoing a significant revolution during recent years. The workshop brings together experts from academia and industry in order to discuss the latest progress in artificial intelligence research in topics related to multimodal information analysis, and in particular, semantic analysis of video, audio, and textual information for smart digital TV content production, access and delivery.

This work in progress reports on ongoing experimentation with machine learning approaches on time series data, where the time series is a quantification of the success of content about a certain topic published on a certain digital channel over a past time period. The experiment tests how accurate predictive analytical approaches can be to predict the future success of a piece of media content published on the Web or social media platform according to its topics. Our intention is to enable a new innovation in media organizations’ content publication strategies, where the choice of media for a future publication can be informed by such predictive capabilities in order to maximize the potential content's reach to a digital audience.

Technological developments in comprehensive video understanding - detecting and identifying visual elements of a scene, combined with audio understanding (music, speech), as well as aligned with textual information such as captions, subtitles, etc. and background knowledge - have been undergoing a significant revolution during recent years. The workshop brings together experts from academia and industry in order to discuss the latest progress in artificial intelligence research in topics related to multimodal information analysis, and in particular, semantic analysis of video, audio, and textual information for smart digital TV content production, access and delivery.

This paper gives an overview of the First International Workshop on Multimedia Verification, organized as part of the 2017 ACM Multimedia Conference. The paper outlines the current verification scene and needs, discusses the goals of the workshop, and presents the workshop's program, consisting of two invited keynote talks and three presentations of full papers that have been accepted at the workshop.

The Semantic Web is about a Web which contains data which is machine-processable rather than human-interpretable. This makes new demands upon the Web architecture such as a standardized interface for access to this knowledge (how to interact), model

One of the main items on the eHealth agenda of the European Community is the design and promotion of electronic patient summaries as an instrument to facilitate the pervasive delivery of healthcare, thus ensuring the right to patient mobility and increasing the productivity and quality of health service delivery. From a technical point of view this objective requires middleware technology which is able to cope with the stringent interoperability, multi-lingualism, security and privacy requirements arising in eHealth settings. In this paper we present triplespace computing, a coordination middleware for the semantic Web and demonstrate its relevance to the realization of the European patient summary infrastructure.

The realization of the semantic Web needs a set of specialized middleware as its infrastructure. In this paper we describe the principles of tuplespace computing, explain why tuplespaces are a suitable middleware for the semantic Web, envision "semantic Web spaces", and outline how our tuplespace platform XMLSpaces can be extended to support semantic Web technologies, like RDF(S) and OWL.

The task of applications to integrate data from multiple sources in different environments (such as broadcast television and World Wide Web) encounters the problem of non-interoperability between the various standards used to encode and represent that data. The potential of the semantic Web to provide machine-understandable data to applications to aid interoperability needs to resolve the issue of the use of different semantic concept spaces for the same concepts. Without solutions to this, convergence applications will not be able to benefit from semantic information when seeking to integrate content. This paper analyses a number of possible approaches using semantic Web standards and outlines how the chosen approach will extend my research in implementing an automated system for the integration of Web-based content with audio-visual material.