Portugal

The field of bisociative literature-based discovery aims at exploring scientific literature to reveal new discoveries based on yet uncovered relations between knowledge from different, relatively isolated fields of specialization. This paper outlines selected outlier-based literature mining approaches, which focus on finding outlier documents as means for finding unexpected links crossing different contexts. Selected approaches to bridging term detection through outlier document exploration are briefly outlined, together with the lessons learned from recent applications in medical and biological literature-based knowledge discovery. Finally, the paper addresses new prospects in bisociative literature-based discovery, emphasizing the use of advanced embeddings technology for cross-domain literature mining.

ICCC&#39;20

Bisociative Literature-based Discovery: Lessons Learned and New Prospects

literature based discovery

bisociative knowledge discovery

lessons learned

outlier detection

text mining

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

poster

Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalising what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.


ICCC'20

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.
Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviors that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalizing what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.



This papers revisits the receptive theory in context of computational creativity. It presents a case study of a Paranoid Transformer — a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical details of the generative system, provide examples of output and discuss the impact of receptive theory, chance discovery and simulation of fringe mental state on the understanding of computational creativity.

Paranoid Transformer: Reading Narrative of Madnessas Computational Approach to Creativity

The ability to extract features from objects or concepts and to connect them in a meaningful way is believed to be crucial to creativity. This paper proposes a novel computational model for creative behaviour, by learning extractions and connections separately. Such separation enables adaptive feature and object connections, which means one object can be connected to different other objects within different contexts. This paper applies recent cognitive theories of computational creativity to two specific tasks: an image-to-image mapping and music- to-video mapping. In both cases deep neural networks are used to automate these two creative cognition tasks.

Being Creative: A Domain Mapping Network

We present Lemotif, an integrated natural language processing and image generation system that uses machine learning to (1) parse a text-based input journal entry describing the user's day for salient themes and emotions and (2) visualize the detected themes and emotions in creative and appealing image motifs. Synthesizing approaches from artificial intelligence and psychology, Lemotif acts as an affective visual journal, encouraging users to regularly write and reflect on their daily experiences through visual reinforcement. By making patterns in emotions and their sources more apparent, Lemotif aims to help users better understand their emotional lives, identify opportunities for action, and track the effectiveness of behavioral changes over time. We verify via human studies that prospective users prefer motifs generated by Lemotif over corresponding baselines, find the motifs representative of their journal entries, and think they would be more likely to journal regularly using a Lemotif-based app.

Lemotif: An Affective Visual Journal Using Deep Neural Networks

"This work is on the transformation of text relying mostly on a common analogy vector operation, computed in a distributional model, i.e., static word embeddings.
Given a theme, original song lyrics have their words replaced by new ones, related to the new theme as the original words are to the original theme.
As this is not enough for producing good lyrics, towards more coherent and singable text, constraints are gradually applied to the replacements.
Human opinions confirmed that more balanced lyrics are obtained when there is a one-to-one mapping between original words and their replacement; only content words are replaced; and the replacement has the same part-of-speech and rhythm as the original word."

WeirdAnalogyAutomatic: Experimenting with Analogy for Lyrics Transformation

Generation and transformation of images and videos using artificial intelligence have flourished over the past few years. Yet, there are only a few works aiming to produce creative 3D shapes, such as sculptures. Here we show a novel 3D-to-3D topology transformation method using Generative Adversarial Networks (GAN). We use a modified pix2pix GAN, which we call Vox2Vox, to transform the volumetric style of a 3D object while retaining the original object shape. In particular, we show how to transform 3D models into two new volumetric topologies - the 3D Network and the Ghirigoro. We describe how to use our approach to construct customized 3D representations. We believe that the generated 3D shapes are novel and inspirational. Finally, we compare the results between our approach and a baseline algorithm that directly convert the 3D shapes, without using our GAN.

3D Topology Transformation with Generative Adversarial Networks

"Self-expression is essential to processing our thoughts and feelings and is central to successful mental health therapy. Art therapy provides a wider range of expressive mechanisms than offered through traditional approaches, allowing individuals to process their emotions when traditional therapies prove unsuccessful. Yet, effective expression through art therapy may call on a level of artistic experience that is not available to all.  As such, a lack of expertise or comfort with artistic expression may hinder one's ability to receive needed mental health support. 
 
Creative machines can offer novel therapeutic approaches by offloading the need for creative expertise and opening up creative self-expression to those who lack the corresponding experience. In this paper, we focus on bereavement, and explore a co-creative songwriting system, ALYSIA, as a new form of therapy for those who had recently suffered the loss of a loved one. We evaluate the utility of this creative system in aiding bereaved individuals through several case studies. In addition, we discuss the utility of co-creative systems to the therapeutic context with potential application to  a broad range of therapies."

Co-Creative Songwriting for Bereavement Support

We consider the advent of computational creativity systems that are potentially far superior to human creators, at least in some domains. To do so, we revisit the concepts of P- and H-creativity and propose a social refinement called group-relative creativity (G-creativity). Using this reconceptualization, we explore several critical questions regarding computational creativity’s effect on human creators.

What Happens When a Computer Joins the Group?

"The presentation describes an exploratory study involving humans and computational musical agents. In the study, the system's co-creative behavior
was varied by manipulating a single experimental parameter to engender different co-creative experiences in rhythm duets. In one experimental condition, the system induced a sense of producing divergent material, influencing creative outcomes, and negotiating musical outcomes, three aspects that were consistent with group music creativity. These preliminary findings provide supportive evidence that the system’s parameter can be varied to systematically engender co-creative experiences distinct from creative support."

Engendering co-creative experiences through parametric variation

“Hello” is an interactive cinematic generative artwork that gained traction within the Cultural Adventures research project, an international endeavor between Portugal and Thailand. The encompassing rationale assumed the need of a link connecting both countries, and thus “Hello” was born from the similarities that can be found in commuters that flock to and from both capital cities by train, often more engaged with their mobile phones than with their immediate surroundings – people, train or traversed landscapes –, which constitute more of a shapeless blurred audiovisual background than a sharp attention-grabbing focus. The artwork is also used as a demonstrator of the potential of generative art in producing complex cinematic narrative figurative artworks, yet permeated with interference, chaos and randomness, and how the audience perceives and makes sense of them through the ability to identify and create patterns, to conceptualize, to deal with information overload, and to imagine and build a narrative through abductive reasoning from incomplete yet suggestive information.

Hello, An Interactive Cinematic Generative Artwork

Many machine learning approaches are focused on defining artificial agents able to find solutions to a certain problem given fixed design tools or parameters to optimize. In order to do that, creators must have a certain knowledge of the solution space to define design parameters that ensure enough exploration allowing agents to find its best configuration. However, this approach may limit artificial agents since they are restricted by their initial conditions of a certain design problem. In addition, specific initial conditions also limit them to scale across multiple challenges. In this paper, we explore how the definition of more general design tools can allow artificial agents to better explore the solution space and generalize through multiple design problems. To do that, we compare design artifacts produced by an artificial agent that learns to construct 2D shapes with a fixed number of pieces to another artificial agent that also learns to add or remove pieces from its design proposal. We demonstrate how by allowing more freedom in design, an artificial system is able to produce more novel artifacts with higher performances in multiple scenarios.

Bisociative Literature-based Discovery: Lessons Learned and New Prospects

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Paranoid Transformer: Reading Narrative of Madnessas Computational Approach to Creativity

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