Learning icons appearance similarity

Selecting an optimal set of icons is a crucial step in the pipeline of visual design to structure and navigate through content. However, designing the icons sets is usually a difficult task for which expert knowledge is required. In this work, to ease the process of icon set selection to the users, we propose a similarity metric which captures the properties of style and visual identity. We train a Siamese Neural Network with an online dataset of icons organized in visually coherent collections that are used to adaptively sample training data and optimize the training process. As the dataset contains noise, we further collect human-rated information on the perception of icon's similarity which will be used for evaluating and testing the proposed model. We present several results and applications based on searches, kernel visualizations and optimized set proposals that can be helpful for designers and non-expert users while exploring large collections of icons.Comment: 12 pages, 11 figure

Efficiency of Spearcon-Enhanced Navigation of One Dimensional Electronic Menus

This study simulated and compared cell phone contact book menu navigation using combinations of both auditory (text-to-speech and spearcons) and visual cues. A total of 127 undergraduates participated in a study that required using one of five conditions of alphabetically listed menu cues to find a target name. Participants using visual cues (either alone or combined with auditory cues) outperformed those using only auditory cues. Performance was not found to be significantly different among the three auditory only conditions. When combined with visual cues, spearcons improved navigational efficiency more than both text-to-speech cues and menus using no sound, and provided evidence for the ability of sound to enhance visual menus. Research results provide evidence applicable to efficient auditory menu creation.Gregory Corso - Committee Member/Second Reader ; Bruce Walker - Faculty Mento

Investigating familiar interfaces to help older adults learn computer applications more easily

Many older adults wish to gain competence in using a computer, but many application interfaces are perceived as complex and difficult to use, deterring potential users from investing the time to learn them. Hence, this study looks at the potential of ‘familiar’ interface design which builds upon users’ knowledge of real world interactions, and applies existing skills to a new domain. Tools are provided in the form of familiar visual objects, and manipulated like real-world counterparts, rather than with buttons, icons and menus found in classic WIMP interfaces. This paper describes the formative evaluation of computer interactions that are based upon familiar real world tasks, which supports multitouch interaction, involves few buttons and icons, no menus, no right-clicks or double-clicks and no dialogs. Using an example of an email client to test the principles of using “familiarity”, the initial feedback was very encouraging, with 3 of the 4 participants being able to undertake some of the basic email tasks with no prior training and little or no help. The feedback has informed a number of refinements of the design principles, such as providing clearer affordance for visual objects. A full study is currently underway

Sequences of purchases in credit card data reveal life styles in urban populations

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics and social sciences. In human activities, Zipf-laws describe for example the frequency of words appearance in a text or the purchases types in shopping patterns. In the latter, the uneven distribution of transaction types is bound with the temporal sequences of purchases of individual choices. In this work, we define a framework using a text compression technique on the sequences of credit card purchases to detect ubiquitous patterns of collective behavior. Clustering the consumers by their similarity in purchases sequences, we detect five consumer groups. Remarkably, post checking, individuals in each group are also similar in their age, total expenditure, gender, and the diversity of their social and mobility networks extracted by their mobile phone records. By properly deconstructing transaction data with Zipf-like distributions, this method uncovers sets of significant sequences that reveal insights on collective human behavior.Comment: 30 pages, 26 figure

Leaming Visual Appearance: Perception, Modeling and Editing.

La apariencia visual determina como entendemos un objecto o imagen, y, por tanto, es un aspecto fundamental en la creación de contenido digital. Es un término general, englobando otros como la apariencia de los materiales, definida como la impresión que tenemos de un material, y la cual supone una interacción física entre luz y materia, y como nuestro sistema visual es capaz de percibirla. Sin embargo, modelar computacionalmente el comportamiento de nuestro sistema visual es una tarea difícil, entre otros motivos porque no existe una teoría definitiva y unificada sobre la percepción visual humana. Además, aunque hemos desarrollado algoritmos capaces de modelar fehacientemente la interacción entre luz y materia, existe una desconexión entre los parámetros físicos que usan estos algoritmos, y los parámetros perceptuales que el sistema visual humano entiende. Esto hace que manipular estas representaciones físicas, y sus interacciones, sea una tarea tediosa y costosa, incluso para usuarios expertos. Esta tesis busca mejorar nuestra comprensión de la percepción de la apariencia de materiales y usar dicho conocimiento para mejorar los algoritmos existentes para la generación de contenido visual. Específicamente, la tesis tiene contribuciones en tres áreas: proponiendo nuevos modelos computacionales para medir la similitud de apariencia; investigando la interacción entre iluminación y geometría; y desarrollando aplicaciones intuitivas para la manipulación de apariencia, en concreto, para el re-iluminado de humanos y para editar la apariencia de materiales.Una primera parte de la tesis explora métodos para medir la similaridad de apariencia. Ser capaces de medir cómo de similares son dos materiales, o imágenes, es un problema clásico en campos de la computación visual como visión por computador o informática gráfica. Abordamos primero el problema de similaridad en la apariencia de materiales. Proponemos un método basado en deep learning que combina imágenes con juicios subjetivos sobre la similitud de materiales, recogidos mediante estudios de usuario. Por otro lado, se explora el problema de la similaridad entre iconos. En este segundo caso, se hace uso de redes neuronales siamesas, y el estilo y la identidad que dan los artistas juega un papel clave en dicha medida de similaridad. La segunda parte avanza en la comprensión de cómo los factores de confusión (confounding factors) afectan a nuestra percepción de la apariencia de los materiales. Dos factores de confusión claves son la geometría de los objetos y la iluminación de la escena. Comenzamos investigando el efecto de dichos factores a la hora de reconocer los materiales a través de diversos experimentos y estudios estadísticos. También investigamos el efecto del movimiento del objeto en la percepción de la apariencia de materiales.En la tercera parte exploramos aplicaciones intuitivas para la manipulación de la apariencia visual. Primero, abordamos el problema de la re-iluminación de humanos. Proponemos una nueva formulación del problema, y basándonos en ella, se diseña y entrena un modelo basado en redes neuronales profundas para re-iluminar una escena. Por último, abordamos el problema de la edición intuitiva de materiales. Para ello, recopilamos juicios humanos sobre la percepción de diferentes atributos y presentamos un modelo, basado en redes neuronales profundas, capaz de editar materiales de forma realista simplemente variando el valor de los atributos recogidos.<br /

Symbols are not uniquely human

Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic and neurobiological constraints. The simulations indicate that learning depends on the tutor-predator ratio, and that apprentice-generated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80% of mistakes are tolerated). In contrast, just 10% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner. Tutor unreliability was also deleterious to vocal symbol learning: a mere 5% of “lying” tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices. Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious. We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input and the motor output, able to support the gradual complexification of grammatical categories into syntax