Synthetic Semiotics: on modelling and simulating the \ud emergence of sign processes

Based on formal-theoretical principles about the \ud sign processes involved, we have built synthetic experiments \ud to investigate the emergence of communication based on \ud symbols and indexes in a distributed system of sign users, \ud following theoretical constraints from C.S.Peirce theory of \ud signs, following a Synthetic Semiotics approach. In this paper, we summarize these computational experiments and results regarding associative learning processes of symbolic sign modality and cognitive conditions in an evolutionary process for the emergence of either symbol-based or index-based communication

Studying sign processes in the emergence of communication

Communication depends on the production and interpretation \ud of representations, but the study of representational processes \ud underlying communication finds little discussion in \ud computational experiments. Here we present an experiment \ud on the emergence of both interpretation and production of \ud multiple representations, with multiple referents, where \ud referential processes can be tracked. Results show the \ud dynamics of semiotic processes during the evolution of \ud artificial creatures and the emergence of a variety of semiotic \ud processes, such as sign production, sign interpretation, and \ud sign-object-interpretant relations

Emergence of Self-Organized Symbol-Based Communication \ud in Artificial Creatures

In this paper, we describe a digital scenario where we simulated the emergence of self-organized symbol-based communication among artificial creatures inhabiting a \ud virtual world of unpredictable predatory events. In our experiment, creatures are autonomous agents that learn symbolic relations in an unsupervised manner, with no explicit feedback, and are able to engage in dynamical and autonomous communicative interactions with other creatures, even simultaneously. In order to synthesize a behavioral ecology and infer the minimum organizational constraints for the design of our creatures, \ud we examined the well-studied case of communication in vervet monkeys. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex adaptive system, where self-organized communicative interactions play a \ud major role in the emergence of symbol-based communication. We also strive in this paper for a careful use of the theoretical concepts involved, including the concepts of symbol and emergence, and we make use of a multi-level model for explaining the emergence of symbols in semiotic systems as a basis for the interpretation of inter-level relationships in the semiotic processes we are studying

The Emergence of Symbol-Based Communication in a Complex System of Artificial Creatures

We present here a digital scenario to simulate the emergence of self-organized symbol-based communication among artificial creatures inhabiting a virtual world of predatory events. In order to design the environment and creatures, we seek theoretical and empirical constraints from C.S.Peirce Semiotics and an ethological case study of communication among animals. Our results show that the creatures, assuming the role of sign users and learners, behave collectively as a complex system, where self-organization of communicative interactions plays a major role in the emergence of symbol-based communication. We also strive for a careful use of the theoretical concepts involved, including the concepts of symbol, communication, and emergence, and we use a multi-level model as a basis for the interpretation of inter-level relationships in the semiotic processes we are studying

Symbols are not uniquely human

Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic and neurobiological constraints. The simulations indicate that learning depends on the tutor-predator ratio, and that apprentice-generated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80% of mistakes are tolerated). In contrast, just 10% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner. Tutor unreliability was also deleterious to vocal symbol learning: a mere 5% of “lying” tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices. Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious. We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input and the motor output, able to support the gradual complexification of grammatical categories into syntax

Aplicação de Algoritmos Genéticos de Otimização para a minimização de risco em um Portfólio de Negociações Automatizadas

Com a digitalização das bolsas e casas de câmbio houve o surgimento dos chamados Automated Trading System (ATS), que consistem em sistemas que realizam as negociações de uma forma automática no mercado de renda variável, assim não se tem a mais a necessidade de um usuário humano está realizando as operações, como pode ser visto em Pauna (2018), Lu e Alvarez (2016), Paraná (2017), Gao e Chan (2000), Aldridge (2013), Pardo (2011) e Parikh e Shah (2015)

Identificação de Espécies de Plantas em Imagens com Aprendizado Profundo Baseado em Taxonomia

Considerando a flora mundial, estimativas atuais apontam para a existência de cerca de 420.000 espécies de angiospermas. Devido às especificidades das espécies e variações fenotípicas, sua identificação é considerada uma tarefa muito difícil para o cidadão comum e até mesmo para especialistas em botânica [1,2]

Avaliação de Políticas de Aumento de Dados de Estado-da-Arte para Reconhecimento de Espécies de Plantas em Larga Escala através de Deep Learning

As técnicas de Aumento de Dados (AD) consistem na aplicação de transformações sobre imagens a fim de potencializar o treinamento de modelos de aprendizado de máquina através da codificação manual de invariâncias para realização de tarefas decomputação visual

Mapeando redes de co-autoria na comunidade acadêmica de artes em Minas Gerais

Research communities (technological, scientific, artistic, philosophical) could be defined as socially aligned groups whose cohesion is supported by artifacts and technologies of self recognition and collective memory. Some of the most important mechanisms of cohesion depend on several types of collaborative bonds, which accumulate temporally between agents, and they include processes and protocols of coauthorship and publication. The interest in the collaborative phenomenon has been increasing for obvious reasons – collaboration, both quantitatively and qualitatively, improves research within a community. The community should recognize themselves as a group with a certain cohesion, committed to achieving results of 'shared intentionality' among the agents. We have analyzed the architecture and dynamics of multi-author collaboration in the research community of arts, using several network analysis metrics, based on paper production published between 2000 and 2020 and indexed on Web of Science, SciELO and Scopus. The number of journals, articles, and researchers in the Brazilian field of study known as 'Linguistics, Letters, and Arts' (LLA) is comparatively low. Out of a total of 260,663 doctors and 2,487,827 articles in the eight major fields of knowledge institutionalized in Brazil (CNPq), only 16,241 doctors and 105,592 articles are in LLA, representing 6.23% of the total researchers between 1998 and 2016 (Mugnaini et al., 2019). In the LLA field, 68.07% of articles were written by a single author, compared to a mean of 35.85% in other fields. The percentage of collaboration among researchers in Agrarian Science and Biological Science is approximately 60%, while in LLA, it is only around 10% (Mena-Chalco et al., 2014: 1433). The main sample group of this research comprises professors who have institutional ties to post-graduate programs, in 2022 in the arts in the state of Minas Gerais. Data were obtained through the Sucupira and Lattes platforms.. To understand the dynamics and architecture of the community, we have modeled collaborations using temporal graphs and analyzed various network properties (density, connectivity, and betweenness centrality). This is the first systematic study to focus on the phenomenon of multi-author collaboration in the areas of linguistics, literature, and the arts in Brazil, using network analysis to examine dynamic transformations over time.                 Comunidades de pesquisa (tecnológicas, científicas, artísticas, filosóficas) podem ser definidas como grupos socialmente alinhados, cuja coesão apoia-se em artefatos e tecnologias de auto-reconhecimento e memória coletiva. Alguns dos mecanismos mais importantes de coesão dependem de diversos tipos de vinculação colaborativa, que acumulam-se temporalmente, entre agentes, e incluem processos e protocolos de publicação em co-autoria. O interesse pelo fenômeno colaborativo é crescente por motivos óbvios – a colaboração melhora (quantitativa e qualitativamente) a pesquisa no interior de uma comunidade, que deve reconhecer-se, a si-mesma, como um grupo, com certa coesão, empenhado na obtenção de resultados de “intencionalidades compartilhadas” entre seus agentes. Analisamos, por meio da análise de redes, a arquitetura e a dinâmica da colaboração multi-autoral da comunidade de pesquisadores em Artes, baseados na produção de artigos publicados entre 2000 e 2020, indexados na Web of Science, SciELO, Scopus. É, comparativamente, reduzido o número de periódicos, artigos e pesquisadores em Linguística, Letras e Artes (LLA). De um total de 260.663 doutores, e 2.487.827 artigos, das oito grandes áreas (CNPq), apenas 16.241 doutores e 105.592 artigos são de LLA, representando 6,23% do total de pesquisadores, entre 1998 e 2016 (Mugnaini et al., 2019). Nessa área (LLA), 68,07% dos artigos foram realizados por um único autor, sendo a média das grandes áreas de 35,85%. O percentual de colaboração entre pesquisadores de Ciências Agrárias e Ciências Biológicas é de aproximadamente 60%, enquanto entre pesquisadores de LLA é de aproximadamente 10%. O principal grupo amostral dessa pesquisa são docentes vinculados, em 2022, a Programas de Pós-Graduação de Artes, em Minas Gerais. Os dados foram obtidos através das plataformas Sucupira e Lattes. Para compreendermos a dinâmica, e a arquitetura da comunidade, modelamos as colaborações através de grafos temporais, e de diversas propriedades de redes (densidade, conectividade, centralidade de intermediação). Trata-se do primeiro estudo sistemático a abordar o fenômeno da colaboração multi-autoral da área de Linguística, Letras e Artes, através de análise de redes, para examinar sua transformação ao longo do tempo