The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them

Mixing Metaphors In The Cerebral Hemispheres: What Happens When Careers Collide?

Are processes of figurative comparison and figurative categorization different? An experiment combining alternative-sense and matched-sense metaphor priming with a divided visual field assessment technique sought to isolate processes of comparison and categorization in the 2 cerebral hemispheres. For target metaphors presented in the right visual field/left cerebral hemisphere (RVF/LH), only matched-sense primes were facilitative. Literal primes and alternative-sense primes had no effect on comprehension time compared to the unprimed baseline. The effects of matched-sense primes were additive with the rated conventionality of the targets. For target metaphors presented to the left visual field/right cerebral hemisphere (LVF/RH), matched-sense primes were again additively facilitative. However, alternative-sense primes, though facilitative overall, seemed to eliminate the preexisting advantages of conventional target metaphor senses in the LVF/RH in favor of metaphoric senses similar to those of the primes. These findings are consistent with tightly controlled categorical coding in the LH and coarse, flexible, context-dependent coding in the RH. (PsycINFO Database Record (c) 2013 APA, all rights reserved)(journal abstract

Affect and Metaphor Sensing in Virtual Drama

We report our developments on metaphor and affect sensing for several metaphorical language phenomena including affects as external entities metaphor, food metaphor, animal metaphor, size metaphor, and anger metaphor. The metaphor and affect sensing component has been embedded in a conversational intelligent agent interacting with human users under loose scenarios. Evaluation for the detection of several metaphorical language phenomena and affect is provided. Our paper contributes to the journal themes on believable virtual characters in real-time narrative environment, narrative in digital games and storytelling and educational gaming with social software

Probability density estimation of photometric redshifts based on machine learning

Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) is a novel method designed to provide a reliable PDF (Probability density Function) of the error distribution of photometric redshifts predicted by ML methods. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. After a short description of the software, we present a summary of results on public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison with a completely different method based on Spectral Energy Distribution (SED) template fitting.Comment: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 784995

Metaphor Aptness And Conventionality: A Processing Fluency Account

Conventionality and aptness are two dimensions of metaphorical sentences thought to play an important role in determining how quick and easy it is to process a metaphor. Conventionality reflects the familiarity of a metaphor whereas aptness reflects the degree to which a metaphor vehicle captures important features of a metaphor topic. In recent years it has become clear that operationalizing these two constructs is not as simple as asking naïve raters for subjective judgments. It has been found that ratings of aptness and conventionality are highly correlated, which has led some researchers to pursue alternative methods for measuring the constructs. Here, in four experiments, we explore the underlying reasons for the high correlation in ratings of aptness and conventionality, and question the construct validity of various methods for measuring the two dimensions. We find that manipulating the processing fluency of a metaphorical sentence by means of familiarization to similar senses of the metaphor (“in vivo conventionalization”) influences ratings of the sentence\u27s aptness. This misattribution may help explain why subjective ratings of aptness and conventionality are highly correlated. In addition, we find other reasons to question the construct validity of conventionality and aptness measures: for instance, we find that conventionality is context dependent and thus not attributable to a metaphor vehicle alone, and we find that ratings of aptness take more into account than they should

Contextual Effects on Metaphor Comprehension: Experiment and Simulation

This paper presents a computational model of referential metaphor comprehension. This model is designed on top of Latent Semantic Analysis (LSA), a model of the representation of word and text meanings. Compre­hending a referential metaphor consists in scanning the semantic neighbors of the metaphor in order to find words that are also semantically related to the context. The depth of that search is compared to the time it takes for humans to process a metaphor. In particular, we are interested in two independent variables : the nature of the reference (either a literal meaning or a figurative meaning) and the nature of the context (inductive or not inductive). We show that, for both humans and model, first, metaphors take longer to process than the literal meanings and second, an inductive context can shorten the processing time

Cognitive networks: brains, internet, and civilizations

In this short essay, we discuss some basic features of cognitive activity at several different space-time scales: from neural networks in the brain to civilizations. One motivation for such comparative study is its heuristic value. Attempts to better understand the functioning of "wetware" involved in cognitive activities of central nervous system by comparing it with a computing device have a long tradition. We suggest that comparison with Internet might be more adequate. We briefly touch upon such subjects as encoding, compression, and Saussurean trichotomy langue/langage/parole in various environments.Comment: 16 page

Corpus Analysis and Lexical Pragmatics: An Overview

Lexical pragmatics studies the processes by which lexically encoded meanings are modified in use; well-studied examples include lexical narrowing, approximation and metaphorical extension. Relevance theorists have been trying to develop a unitary account on which narrowing, approximation and metaphorical extension are all explained in the same way. While there have been several corpus-based studies of metaphor and a few of hyperbole or approximation, there has been no attempt so far to test the unitary account using corpus data. This paper reports the results of a corpus-based investigation of lexical-pragmatic processes, and discusses the theoretical issues and challenges it raises

METAPHOR: Probability density estimation for machine learning based photometric redshifts

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).Comment: proceedings of the International Astronomical Union, IAU-325 symposium, Cambridge University pres