Chaotic particle swarm optimization with neural network structure and its application

Abstract: A new particle swarm optimization (PSO) algorithm having a chaotic Hopfield Neural Network (HNN) structure is proposed. Particles exhibit chaotic behaviour before converging to a stable fixed point which is determined by the best points found by the individual particles and the swarm. During the evolutionary process, the chaotic search expands the search space of individual particles. Using a chaotic system to determine particle weights helps the PSO to escape from the local extreme and find the global optimum. The algorithm is applied to some benchmark problems and a pressure vessel problem with nonlinear constraints. The results show that the proposed algorithm consistently outperforms rival algorithms by enhancing search efficiency and improving search qualit

Caminar en la cultura de los Cris (Crees) de Baie James

Today, ethnologists are confronted with cultural media whichhave not only been transformed, but above all have become composite. Fieldexperience defies the laws of cultural homogeneity, because the individualsthat the ethnographer encounters will have lived a multitude of experiencesin environments which no longer necessarily depend on the social universeto which they belong. This finding implies the need to construct tools withwhich to understand an ever more diverse reality. Field experience is thereforesubject to a set of competences, which the ethnographer must acquire,the principles of which depend on different environments on which he/shehas been «projected». This article reports on a work of reflexive analysisresulting from fieldwork done in the Bay James Cree society, in the northernpart of the province of Quebec, Canada. The fieldwork and the competencesthat this implied in the ethnographer, to introduce herself into the daily lifeof one of the families of this group, are thought out in accordance with threespatial entities inhabited by this people on a daily basis, namely the huntingcamps, the village and the region. This spatial partition results from a marchby this society after their ancestral territory was industrialised.Hoy en día, los etnólogos son confrontados a medios culturalesque no sólo han sido transformados sino, y sobre todo, se han vueltocompuestos. En el presente, el terreno desafía las leyes de la homogeneidadcultural, pues los individuos que el etnógrafo encontrará serán seres quehabrán vivido una multitud de experiencias en entornos que ya no dependennecesariamente de su universo social de pertenencia. Esta constatación implicala necesidad de construir herramientas de comprensión de la realidadcada vez más diversas. La experiencia del terreno está sujeta, entonces, a unconjunto de competencias que el etnógrafo debe adquirir, y cuyos principiosdependen de diferentes entornos en los que él habrá sido «proyectado». Esteartículo da testimonio de un trabajo de análisis reflexivo resultante de unterreno, y fue llevado a cabo al interior de la sociedad de los Cris de BaieJames, en las regiones septentrionales de la provincia del Quebec en Canadá.El terreno y las competencias que éste implica por parte del etnógrafopara introducirse en la vida cotidiana de una de las familias de este grupo,son pensados siguiendo tres entidades espaciales que estos últimos viven enlo cotidiano, que son los campamentos de caza, el pueblo, la región, unapartición espacial que resulta de una puesta en marcha de la sociedad comoconsecuencia de la industrialización de su territorio ancestral

Contour detection by CORF operator

We propose a contour operator, called CORF, inspired by the properties of simple cells in visual cortex. It combines, by a weighted geometric mean, the blurred responses of difference-of-Gaussian operators that model cells in the lateral geniculate nucleus (LGN). An operator that has gained particular popularity as a computational model of a simple cell is based on a family of Gabor Functions (GFs). However, the GF operator short-cuts the LGN, and its effectiveness in contour detection tasks, which is assumed to be the primary biological role of simple cells, has never been compared with the effectiveness of alternative operators. We compare the performances of the CORF and the GF operators using the RuG and the Berkeley data sets of natural scenes with associated ground truths. The proposed CORF operator outperforms the GF operator (RuG: t(39)=4.39, p<10−4 and Berkeley: t(499)=4.95, p<10−6).peer-reviewe

MusCaps: generating captions for music audio

Content-based music information retrieval has seen rapid progress with the adoption of deep learning. Current approaches to high-level music description typically make use of classification models, such as in auto tagging or genre and mood classification. In this work, we propose to address music description via audio captioning, defined as the task of generating a natural language description of music audio content in a human-like manner. To this end, we present the first music audio captioning model, MusCaps, consisting of an encoder-decoder with temporal attention. Our method combines convolutional and recurrent neural network architectures to jointly process audio-text inputs through a multimodal encoder and leverages pre-training on audio data to obtain representations that effectively capture and summarise musical features in the input. Evaluation of the generated captions through automatic metrics shows that our method outperforms a baseline designed for non-music audio captioning. Through an ablation study, we unveil that this performance boost can be mainly attributed to pre-training of the audio encoder, while other design choices – modality fusion, decoding strategy and the use of attention -- contribute only marginally. Our model represents a shift away from classification-based music description and combines tasks requiring both auditory and linguistic understanding to bridge the semantic gap in music information retrieval

Reliable Local Explanations for Machine Listening

One way to analyse the behaviour of machine learning models is through local explanations that highlight input features that maximally influence model predictions. Sensitivity analysis, which involves analysing the effect of input perturbations on model predictions, is one of the methods to generate local explanations. Meaningful input perturbations are essential for generating reliable explanations, but there exists limited work on what such perturbations are and how to perform them. This work investigates these questions in the context of machine listening models that analyse audio. Specifically, we use a state-of-the-art deep singing voice detection (SVD) model to analyse whether explanations from SoundLIME (a local explanation method) are sensitive to how the method perturbs model inputs. The results demonstrate that SoundLIME explanations are sensitive to the content in the occluded input regions. We further propose and demonstrate a novel method for quantitatively identifying suitable content type(s) for reliably occluding inputs of machine listening models. The results for the SVD model suggest that the average magnitude of input mel-spectrogram bins is the most suitable content type for temporal explanations