This short paper is describing a demonstrator that is complementing the paper "Towards Cross-Media Feature Extraction" in these proceedings. The demo is exemplifying the use of textual resources, out of which semantic information can be extracted, for supporting the semantic annotation and indexing of associated video material in the soccer domain. Entities and events extracted from textual data are marked-up with semantic classes derived from an ontology modeling the soccer domain. We show further how extracted Audio-Video features by video analysis can be taken into account for additional annotation of specific soccer event types, and how those different types of annotation can be combined.
Kim Binsted Sony Computer Science Lab 3-14-13 Higashigotanda Shinagawa-ku, Tokyo 141 Abstract Byrne is a talking head system, developed with two goals in mind: to allow artists to create entertaining characters with strong personalities, expressed through speech and facial animation; and to allow cognitive scientists to implement and test theories of emotion and expression. Here we emphasize the latter aim. We describe Byrne's design, and discuss some ways in which it could be used in affect-related experiments. Byrne's first domain is football commentary; that is, Byrne provides an emotionally expressive running commentary on a RoboCup simulation league football game. We will give examples from this domain throughout this paper.
Over the course of the past year, it seems that more and more attention is being paid to ensuring that AI is used in ethical ways. Google and Microsoft have both recently warned investors that misuse of AI algorithms or poorly designed AI algorithms presents ethical and legal risks. Meanwhile, the state of California has just decided to pass a bill that bans the use of face recognition technology by California's law enforcement agencies. Recently, startups such as Arthur have been attempting to design tools that will help AI engineers quantify and qualify how their machine learning models perform. As reported by Wired, Arthur is trying to give AI developers a toolkit that will make it easier for them to discover problems when designing financial applications, like unveiling bias in investment or lending decisions.
In order to solve problems of reliability of systems based on lexical repetition and problems of adaptability of language-dependent systems, we present a context-based topic segmentation system based on a new informative similarity measure based on word co-occurrence. In particular, our evaluation with the state-of-the-art in the domain i.e. the c99 and the TextTiling algorithms shows improved results both with and without the identification of multiword units.
Thousands of attendees of the 2017 Champions League final in Cardiff, Wales were mistakenly identified as potential criminals by facial recognition technology used by local law enforcement. According to the Guardian, the South Wales police scanned the crowd of more than 170,000 people who traveled to the nation's capital for the soccer match between Real Madrid and Juventus. The cameras identified 2,470 people as criminals. Having that many potential lawbreakers in attendance might make sense if the event was, say, a convict convention, but seems pretty high for a soccer match. As it turned out, the cameras were a little overly-aggressive in trying to spot some bad guys.