Visual analytics for supply network management: system design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip

Expediting TTS Synthesis with Adversarial Vocoding

Recent approaches in text-to-speech (TTS) synthesis employ neural network strategies to vocode perceptually-informed spectrogram representations directly into listenable waveforms. Such vocoding procedures create a computational bottleneck in modern TTS pipelines. We propose an alternative approach which utilizes generative adversarial networks (GANs) to learn mappings from perceptually-informed spectrograms to simple magnitude spectrograms which can be heuristically vocoded. Through a user study, we show that our approach significantly outperforms na\"ive vocoding strategies while being hundreds of times faster than neural network vocoders used in state-of-the-art TTS systems. We also show that our method can be used to achieve state-of-the-art results in unsupervised synthesis of individual words of speech.Comment: Published as a conference paper at INTERSPEECH 201

Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication

This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of unknown authors in a way that is not easy to be deceived by forged art. The methodology used is based on quantifying the characteristics of individual strokes in drawings. We propose a novel algorithm for segmenting individual strokes. We designed and compared different hand-crafted and learned features for the task of quantifying stroke characteristics. We also propose and compare different classification methods at the drawing level. We experimented with a dataset of 300 digitized drawings with over 80 thousands strokes. The collection mainly consisted of drawings of Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of representative works of other artists. The experiments shows that the proposed methodology can classify individual strokes with accuracy 70%-90%, and aggregate over drawings with accuracy above 80%, while being robust to be deceived by fakes (with accuracy 100% for detecting fakes in most settings)

Simulation of urban system evolution in a synergetic modelling framework. The case of Attica, Greece

Spatial analysis and evolution simulation of such complex and dynamic systems as modern urban areas could greatly benefit from the synergy of methods and techniques that constitute the core of the fields of Information Technology and Artificial Intelligence. Additionally, if during the decision making process, a consistent methodology is applied and assisted by a user-friendly interface, premium and pragmatic solution strategies can be tested and evaluated. In such a framework, this paper presents both a prototype Decision Support System and a consorting spatio-temporal methodology, for modelling urban growth. Its main focus is on the analysis of current trends, the detection of the factors that mostly affect the evolution process and the examination of user-defined hypotheses regarding future states of the problem environment. According to the approach, a neural network model is formulated for a specific time intervals and each different group of spatial units, mainly based to the degree of their contiguity and spatial interaction. At this stage, fuzzy logic provides a precise image of spatial entities, further exploited in a twofold way. First, for the analysis and interpretation of up-to-date urban evolution and second, for the formulation of a robust spatial simulation model. It should be stressed, however, that the neural network model is not solely used to define future urban images, but also to evaluate the degree of influence that each variable as a significant of problem parameter, contributes to the final result. Thus, the formulation and the analysis of alternative planning scenarios are assisted. Both the proposed methodological framework and the prototype Decision Support System are utilized during the study of Attica, Greece?s principal prefecture and the definition of a twenty-year forecast. The variables considered and projected refer to population data derived from the 1961-1991 censuses and building uses aggregated in ten different categories. The final results are visualised through thematic maps in a GIS environment. Finally, the performance of the methodology is evaluated as well as directions for further improvements and enhancements are outlined. Keywords: Computational geography, Spatial modelling, Neural network models, Fuzzy logic.

Subversive blood ties: gothic decadence in three characters from murnau's and coppola's renderings of bram stoker's dracula

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Comunicação e Expressão, Programa de Pós-Graduação em Letras/Inglês e Literatura Correspondente, Florianópolis, 2013Esta dissertação consiste em investigar a construção do tema da decadência Gótica em Drácula de Bram Stoker e duas adaptações fílmicas do romance - Nosferatu, de Friedrich Wilhelm Murnau, e Drácula de Bram Stoker, de Francis Ford Coppola - tendo como centro da análise como três personagens - Drácula, Jonathan Harker e Mina Harker - se relacionam com tal tema. A decadência Gótica é um padrão literário do contexto fin-de-siècle da sociedade vitoriana inspirada pela crise social que acontecia na Inglaterra no fim do século XIX (Punter e Byron 39-40). Autores como Bram Stoker escreveram histórias que refletiam medos morais e sociais da sociedade vitoriana, retratando imagens de monstros que representavam a transgressão de fronteiras morais e sexuais estabelecidas pelas tradições vitorianas (Botting 88). Tendo tal discussão em mente, este estudo busca conectar a retratação de tal tema do romance às adaptações, também utilizando uma análise fílmica para identificar técnicas que destacam a representação do tema relacionado aos três personagens, finalmente ligando tal tema a crises e confusões sociais que aconteciam nos contextos de ambos os filmes.Abstract : The present dissertation consists of an investigation of the construction of the Gothic theme of decadence in Bram Stoker's Dracula and two film adaptations of the novel - Friedrich Wilhelm Murnau's Nosferatu and Francis Ford Coppola's Bram Stoker's Dracula - having as the centre of analysis how three characters - Dracula, Jonathan Harker and Mina Harker - relate to that theme. The Gothic decadence is a literary motif from the fin-de-siècle context of the Victorian Era inspired by the social crisis that took place in England in the late nineteenth century (Punter and Byron 39-40). Authors like Bram Stoker wrote stories that reflected moral and social fears of the Victorian society, depicting images of monsters that represented the crossing of moral and sexual boundaries established by the Victorian traditions (Botting 88). Bearing that discussion in mind, this study aims at connecting the portrayal of such a theme from novel to the two adaptations, also making use of a filmic analysis to identify techniques that highlight the depiction of the theme related to the three characters, ultimately linking such a thematic depiction to crises and social commotions that were taking place in both films' social contexts

Comparative Analysis of 3D Human modeling generation using 3D Structured Light Scanning and Machine Learning generation

Integration of 3D model production from a single 2D RGB picture using machine learning into mainstream human 3D modelling requires significant evaluation data with an appropriate reference value relative to structured light approach scanning. The purpose of this work is to bridge the gap between the structured light technique and the machine learning algorithm generation method through a comparative analysis based on qualitative criteria such as accuracy and efficiency. The subsequent research was undertaken in two parts. Phase 1 centered on the experimental setup of the data collecting approach utilizing several scanning techniques on the sample model in a controlled setting, whereas phase 2 focuses on the analysis of the subsequent data to determine functional equivalency. The most significant finding of the comparison study is the practicality of the PIFuHD machine learning algorithm with respect to the Artec EVA scanner in terms of efficiency and equivalent precision. Despite the advancements in Machine learning algorithm for generative 3D modeling, major improvements are necessary to achieve functional parity with the structured light scanning approach in terms of accuracy