Data augmentation using background replacement for automated sorting of littered waste

The introduction of sophisticated waste treatment plants is making the process of trash sorting and recycling more and more effective and eco-friendly. Studies on Automated Waste Sorting (AWS) are greatly contributing to making the whole recycling process more efficient. However, a relevant issue, which remains unsolved, is how to deal with the large amount of waste that is littered in the environment instead of being collected properly. In this paper, we introduce BackRep: a method for building waste recognizers that can be used for identifying and sorting littered waste directly where it is found. BackRep consists of a data-augmentation procedure, which expands existing datasets by cropping solid waste in images taken on a uniform (white) background and superimposing it on more realistic backgrounds. For our purpose, realistic backgrounds are those representing places where solid waste is usually littered. To experiment with our data-augmentation procedure, we produced a new dataset in realistic settings. We observed that waste recognizers trained on augmented data actually outperform those trained on existing datasets. Hence, our data-augmentation procedure seems a viable approach to support the development of waste recognizers for urban and wild environments

Sexually Antagonistic Selection in Human Male Homosexuality

Several lines of evidence indicate the existence of genetic factors influencing male homosexuality and bisexuality. In spite of its relatively low frequency, the stable permanence in all human populations of this apparently detrimental trait constitutes a puzzling ‘Darwinian paradox’. Furthermore, several studies have pointed out relevant asymmetries in the distribution of both male homosexuality and of female fecundity in the parental lines of homosexual vs. heterosexual males. A number of hypotheses have attempted to give an evolutionary explanation for the long-standing persistence of this trait, and for its asymmetric distribution in family lines; however a satisfactory understanding of the population genetics of male homosexuality is lacking at present. We perform a systematic mathematical analysis of the propagation and equilibrium of the putative genetic factors for male homosexuality in the population, based on the selection equation for one or two diallelic loci and Bayesian statistics for pedigree investigation. We show that only the two-locus genetic model with at least one locus on the X chromosome, and in which gene expression is sexually antagonistic (increasing female fitness but decreasing male fitness), accounts for all known empirical data. Our results help clarify the basic evolutionary dynamics of male homosexuality, establishing this as a clearly ascertained sexually antagonistic human trait

Informativit\ue0 e scritture brevi del web

Il contributo si propone di mostrare l\u2019applicazione della tradizionale prospettiva funzionalista alle forme tipiche delle scritture brevi del web, tra storia della linguistica (Martinet) e teoria matematica dell\u2019informazione (Shannon). La concezione del lavoro \ue8 unitaria, frutto della collaborazione fra gli autori; solo ai fini di attribuzione formale, Francesca Chiusaroli \ue8 autrice del paragrafo 1 e Fabio Massimo Zanzotto \ue8 autore del paragrafo 2. https://sites.google.com/site/scritturebrevi

Change My Mind: how Syntax-based Hate Speech Recognizer can Uncover Hidden Motivations based on Different Viewpoints

Hate speech recognizers may mislabel sentences by not considering the different opinions that society has on selected topics. In this paper, we show how explainable machine learning models based on syntax can help to understand the motivations that induce a sentence to be offensive to a certain demographic group. To explore this hypothesis, we use several syntax-based neural networks, which are equipped with syntax heat analysis trees used as a post-hoc explanation of the classifications and a dataset annotated by two different groups having dissimilar cultural backgrounds. Using particular contrasting trees, we compared the results and showed the differences. The results show how the keywords that make a sentence offensive depend on the cultural background of the annotators and how this differs in different fields. In addition, the syntactic activations show how even the sub-trees are very relevant in the classification phase

Language Evolution in Social Media: a Preliminary Study

Language, as a social phenomenon, is in constant evolution. New words are added, disused ones are forgotten, and some others change their morphology and semantics to adapt to a dynamic World. Today we are leaving a new “Social Media” revolution, that is changing many languages. The pace with which new words are created in social media is unprecedented. People from different demographic groups are often “speaking different languages”, in that not only they use a different set of words, but also assign different meanings to the same words. In this paper, we investigate whether it is possible to lower the “linguistic barrier”, by analyzing the phenomenon of language evolution in social media, and by evaluating to what extent the use of cooperative on-line dictionaries and natural language processing techniques can help in tracking and regulate the evolution of languages in the social media era. We report a study of language evolution in a specific social media, Twitter; and we evaluate whether cooperative dictionaries (specifically Urban Dictionary) can be used to deal with the evolving language. We discover that this method partially solves the problem, by allowing a better understanding of the behavior of new words and expressions. We then analyze how natural language processing techniques can be used to capture the meaning of new words and expressions

Dis-cover ai minds to preserve human knowledge

Modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. The structure of symbols—the operations by which the intellectual solution is realized—and the search for strategic reference points evoke important issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped ahead with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we would investigate the syntax as possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning, and human knowledge