Mexico

Computer vision research into image aesthetic quality assessment seeks to use machine learning to measure the aesthetic quality of images, which bears resemblance to a variety of topics within computational creativity, but has not been discussed in our community. To foster a conversation around this research literature, we trace the development of computer vision algorithms foraesthetic judgment over the past fifteen years and critically consider whether these algorithms actually cross the “aesthetic gap” proposed by the first researchers in this space. We then build towards a more fundamental question regarding machine learning and subjectivity.

ICCC&#39;21

What Does it Take to Cross the Aesthetic Gap? The Development of Image Aesthetic Quality Assessment in Computer Vision

aesthetics

history

subjectivity

machine learning

computer vision

philosophy

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). <br>**ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. <br>**STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.<br><br><br>

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

[Register now](http://c4ai.inova.usp.br/bracis2021/)

Register now

BRACIS 2021

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

technical paper

### **Welcome to the Twelfth International Conference on Computational Creativity, ICCC’21!**

<iframe width=1280 height=720 style="border:0;" scrolling="no" src="https://screencast-o-matic.com/player/crQbitV66dw?width=1280&height=720&ff=1&title=0" allowfullscreen="true"></iframe>

Computational creativity is an interdisciplinary endeavor that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, social sciences and the arts. The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:

* to construct a program or computer capable of showing some level of creativity
* to better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans
* to design programs that can enhance human creativity without necessarily being creative themselves

<br>The ICCC conference series organized by the [**Association for Computational Creativity**](https://computationalcreativity.net/) since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.



To access the Twelfth International Conference on Computational Creativity, ICCC’21, you need to register for the conference.  Instructions on how to access the event will be provided directly to attendees and included on the Twelfth International Conference on Computational Creativity, ICCC’21 conference website. 

[Register now](https://computationalcreativity.net/iccc21/registration/)

ICCC'21

Welcome to the Twelfth International Conference on Computational Creativity, ICCC’21!

ML Design for Creative Pursuits

A chatbot is perceived as more humanlike and likeable if it includes some jokes in its output. But most existing joke generators were not designed to be integrated into chatbots. This paper presents Witscript, a novel joke generation system that can improvise original, contextually relevant jokes, such as humorous responses during a conversation. The system is based on joke writing algorithms created by an expert comedy writer. Witscript employs well-known tools of natural language processing to extract keywords from a topic sentence and, using wordplay, to link those keywords and related words to create a punch line. Then a pretrained neural network language model that has been fine-tuned on a dataset of TV show monologue jokes is used to complete the joke response by filling the gap between the topic sentence and the punch line. A method of internal scoring filters out jokes that don't meet a preset standard of quality. Human evaluators judged Witscript's responses to input sentences to be jokes more than 40% of the time. This is evidence that Witscript represents an important next step toward giving a chatbot a humanlike sense of humor.

Witscript: A System for Generating Improvised Jokes in a
Conversation

We explore a masked-language model based on BERT for shifting the meaning of text towards a target theme.
Content words in the original text are masked and the model provides a list of filling candidates, out of which one is selected based on its similarity to the theme and constraints regarding morphology and metre.
Experimentation is performed with Portuguese song lyrics and trade-offs between grammaticality, semantics, form, and novelty are analysed.
We confirm that BERT is a useful tool for creative text transformation.

Exploring a Masked Language Model for Creative TextTransformation

We present an interactive computational approach to fill curatorial gaps between artworks in a fine arts exhibition. We describe the algorithmic details of our semantic
approach based on word embedding of keywords and how we include additional curatorial constraints. We present an installation at a museum exhibition and discuss lessons learned during our arts based research.

Computational filling of curatorial gaps in a fine arts exhibition

We present a system that automatically generates music from visual art based on the perceived emotion of the given input. We propose the generated music as a framing device that can enhance the aesthetic experience of people viewing Computational Creativity (CC) outputs. In this paper, we carry out a first study to test this by comparing the aesthetic experience of viewing paintings generated by CC systems accompanied by either textual framing, our proposed musical framing or both. We evaluate our system by means of qualitative user evaluations, which require participants to rank their aesthetic experience from best to worst. The results from the study demonstrated that the musical framing generated by our system provided a better aesthetic experience for users compared to the textual framing. Furthermore, the results suggest that with more work, a combination of textual and musical framing could be used to further improve the aesthetic experience for people viewing visual CC art.

Framing through Music: A Pilot Study

We ask the question: to what extent can recent large-scale language and image generation models blend visual concepts? Given an arbitrary object, we identify a relevant object and generate a single-sentence description of the blend of the two using a language model. We then generate a visual depiction of the blend using a text-based image generation model. Quantitative and qualitative evaluations demonstrate the superiority of language models over classical methods for conceptual blending, and of recent large-scale image generation models over prior models for the visual depiction.

Visual Conceptual Blending with Large-scale Language and Vision Models

In this short paper, we reflect on the long quest for intelligence and creativity of computing machinery as well as the suitability for prevailing machine learning techniques to be used in creative tasks. We believe that modularization and multi-layered structures are among essential ingredients constituting creative minds and may greatly benefit machines on handling creative tasks. For proof of concept, we select musical composition, particularly, Species Counterpoint, as the task, adopt a recently proposed computational framework designed for investigating creativity and the creative process, and present an implementation capable of producing the outcomes that exhibit the desired effect.

What Does it Take to Cross the Aesthetic Gap? The Development of Image Aesthetic Quality Assessment in Computer Vision

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Classification of chest X-ray images using Machine Learning and Histogram of Oriented Gradients

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