9,370 research outputs found
SentiCap: Generating Image Descriptions with Sentiments
The recent progress on image recognition and language modeling is making
automatic description of image content a reality. However, stylized,
non-factual aspects of the written description are missing from the current
systems. One such style is descriptions with emotions, which is commonplace in
everyday communication, and influences decision-making and interpersonal
relationships. We design a system to describe an image with emotions, and
present a model that automatically generates captions with positive or negative
sentiments. We propose a novel switching recurrent neural network with
word-level regularization, which is able to produce emotional image captions
using only 2000+ training sentences containing sentiments. We evaluate the
captions with different automatic and crowd-sourcing metrics. Our model
compares favourably in common quality metrics for image captioning. In 84.6% of
cases the generated positive captions were judged as being at least as
descriptive as the factual captions. Of these positive captions 88% were
confirmed by the crowd-sourced workers as having the appropriate sentiment
Automatic Music Composition using Answer Set Programming
Music composition used to be a pen and paper activity. These these days music
is often composed with the aid of computer software, even to the point where
the computer compose parts of the score autonomously. The composition of most
styles of music is governed by rules. We show that by approaching the
automation, analysis and verification of composition as a knowledge
representation task and formalising these rules in a suitable logical language,
powerful and expressive intelligent composition tools can be easily built. This
application paper describes the use of answer set programming to construct an
automated system, named ANTON, that can compose melodic, harmonic and rhythmic
music, diagnose errors in human compositions and serve as a computer-aided
composition tool. The combination of harmonic, rhythmic and melodic composition
in a single framework makes ANTON unique in the growing area of algorithmic
composition. With near real-time composition, ANTON reaches the point where it
can not only be used as a component in an interactive composition tool but also
has the potential for live performances and concerts or automatically generated
background music in a variety of applications. With the use of a fully
declarative language and an "off-the-shelf" reasoning engine, ANTON provides
the human composer a tool which is significantly simpler, more compact and more
versatile than other existing systems. This paper has been accepted for
publication in Theory and Practice of Logic Programming (TPLP).Comment: 31 pages, 10 figures. Extended version of our ICLP2008 paper.
Formatted following TPLP guideline
Embedding Requirements within the Model Driven Architecture
The Model Driven Architecture (MDA) brings benefits to software development, among them the potential for connecting software models with the business domain. This paper focuses on the upstream or Computation Independent Model (CIM) phase of the MDA. Our contention is that, whilst there are many models and notations available within the CIM Phase, those that are currently popular and supported by the Object Management Group (OMG), may not be the most useful notations for business analysts nor sufficient to fully support software requirements and specification.
Therefore, with specific emphasis on the value of the Business Process Modelling Notation (BPMN) for business analysts, this paper provides an example of a typical CIM approach before describing an approach which incorporates specific requirements techniques. A framework extension to the MDA is then introduced; which embeds requirements and specification within the CIM, thus further enhancing the utility of MDA by providing a more complete method for business analysis
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