1,720,955 research outputs found
Cognitive Assemblages: Spatial Generation Through Wave Function Collapse and Reinforcement Learning
No full textThis research explores the integration of AI in an iterative decision process for the open-ended procedural generation of architectural spaces. Leveraging on state-of-the-art Deep Reinforcement Learning techniques, an Artificial Neural Network (ANN) is trained to perform local decisions selecting tiles in a Wave Function Collapse (WFC) algorithm, assembling discrete elements that build up a complex spatial organization, pursuing selected spatial qualities at the architectural scale
Training Spaces - Fostering machine sensibility for spatial assemblages through wave function collapse and reinforcement learning
No full textThis research explores the integration of Deep Reinforcement Learning (RL) and a Wave Function Collapse (WFC) algorithm for a goal-driven, open-ended generation of architectural spaces. Our approach binds RL to a distributed network of decisions, unfolding through three key steps: the definition of a set of architectural components (tiles) and their connectivity rules, the selection of the tile placement location, which is determined by the WFC, and the choice of which tile to place, which is performed by RL. The act of thinking becomes granular and embedded in an iterative process, distributed among human and non-human cognition, which constantly negotiate their agency and authorial status. Tools become active agents capable of developing their own sensibility while controlling specific spatial conditions. Establishing an interdependency with the human, that engenders the design patterns and becomes an indispensable prerequisite for the exploration of the generated design space, exceeding human or machinic reach alone
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
EEG data driven automatic soundtrack composition based on emotion recognition
No full textLAUREA MAGISTRALEMusica e immagini sono componenti fondamentale per l’impatto emotivo durante la visione di un video. La natura degli elementi che contribuiscono a rendere la musica uno stimolo emotivo non è facilmente identificabile. Questo perché le emozioni sono fortemente soggettive. In questa tesi proproniamo un metodo per comporre musica utilizzando le emozioni come input, andando ad analizzare quali elementi possono contribuire a rendere un pezzo più emotivo.
Ci sono alcuni elementi misurabili nell’uomo che possono portare informazioni riguardo al mood in un dato istante, come ad esempio i segnali celebrali. Il nostro lavoro propone quindi un metodo che, a partire da questi segnali, suggerisce l'emozione tramite un algoritmo di Machine Learning. Questa viene poi usata per comporre un brano musicale, che esprima una certa emozione, grazie a un algoritmo di Transformer. Nel sistema useremo anche delle features musicali i cui valori hanno una variazione che è legata alla variazione delle emozioni.
I segnali celebrali sono tradotti usando un sistema di EEG su soggetti stimolati da video. La musica generata a partire dai segnali acquisiti è usata come soundtrack per altri video. In questo modo il soggetto diventa il compositore della colonna sonora realtiva al video che sta vedendo, tramite le sue onde celebrali.
Questo studio si propone di aggiungere elementi di ricerca allo studio delle emozioni nell’esecuzione e nella generazione di brani musicali. Abbiamo scelto di effettuare l’acquisione dei dati di EEG tramite caschetti Muse, in collaborazione con Fuse*Factory, che ne ha approfondito l’uso durante lo studio per le loro performance artistiche.Music and images are both key components to provide an emotional impact when we watch videos. The nature of the elements that contribute to making music an emotional stimulus is not easily identifiable. This is because emotions are highly subjective. In this thesis we propose a method to compose music using emotions as an input and analyse which elements can contribute to making a piece more emotional.
There are certain measurable elements in humans that can carry information about the mood at a given moment, such as brain signals. Our work therefore proposes a method that, starting from these signals, first infers the emotion felt using a Machine Learning algorithm. The retrieved emotion is then used to compose a piece of music that expresses a certain emotion, thanks to a Transformer algorithm. In the system we will also use musical features whose values have a variation linked to the variation of the emotions. Brain signal are traduced using EEG system on subject stimulated by videos. The music generated starting by the acquired signals is used as soundtrack for the selected video. In this way the subject become the composer of the soundtrack related to the video is watching, through his/her brainwaves.
This study aims to add research elements to the study of emotions in the performance and generation of musical pieces. We have chosen to carry out the acquisition of EEG data using Muse headbands, in collaboration with Fuse*Factory, who have furthered their use during the study for their artistic performances
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