125,299 research outputs found
Finding Person Relations in Image Data of the Internet Archive
The multimedia content in the World Wide Web is rapidly growing and contains
valuable information for many applications in different domains. For this
reason, the Internet Archive initiative has been gathering billions of
time-versioned web pages since the mid-nineties. However, the huge amount of
data is rarely labeled with appropriate metadata and automatic approaches are
required to enable semantic search. Normally, the textual content of the
Internet Archive is used to extract entities and their possible relations
across domains such as politics and entertainment, whereas image and video
content is usually neglected. In this paper, we introduce a system for person
recognition in image content of web news stored in the Internet Archive. Thus,
the system complements entity recognition in text and allows researchers and
analysts to track media coverage and relations of persons more precisely. Based
on a deep learning face recognition approach, we suggest a system that
automatically detects persons of interest and gathers sample material, which is
subsequently used to identify them in the image data of the Internet Archive.
We evaluate the performance of the face recognition system on an appropriate
standard benchmark dataset and demonstrate the feasibility of the approach with
two use cases
Hikester - the event management application
Today social networks and services are one of the most important part of our
everyday life. Most of the daily activities, such as communicating with
friends, reading news or dating is usually done using social networks. However,
there are activities for which social networks do not yet provide adequate
support. This paper focuses on event management and introduces "Hikester". The
main objective of this service is to provide users with the possibility to
create any event they desire and to invite other users. "Hikester" supports the
creation and management of events like attendance of football matches, quest
rooms, shared train rides or visit of museums in foreign countries. Here we
discuss the project architecture as well as the detailed implementation of the
system components: the recommender system, the spam recognition service and the
parameters optimizer
Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences
This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering
Understanding the EU Policy Portfolio: Conceptualising a Dynamic Model of Integration
This paper suggests that EU‘s policy portfolio resembles the outcome of interstate bargaining predicted by federalist theory. We conclude that federalist theory, when combined with economic integration theory, is a robust tool for conceptualizing the EU policy portfolio. The result is a dynamic model, which we call 'federal integration‘
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