Design Guidelines for Agent Based Model Visualization

In the field of agent-based modeling (ABM), visualizations play an important role in identifying, communicating and understanding important behavior of the modeled phenomenon. However, many modelers tend to create ineffective visualizations of Agent Based Models (ABM) due to lack of experience with visual design. This paper provides ABM visualization design guidelines in order to improve visual design with ABM toolkits. These guidelines will assist the modeler in creating clear and understandable ABM visualizations. We begin by introducing a non-hierarchical categorization of ABM visualizations. This categorization serves as a starting point in the creation of an ABM visualization. We go on to present well-known design techniques in the context of ABM visualization. These techniques are based on Gestalt psychology, semiology of graphics, and scientific visualization. They improve the visualization design by facilitating specific tasks, and providing a common language to critique visualizations through the use of visual variables. Subsequently, we discuss the application of these design techniques to simplify, emphasize and explain an ABM visualization. Finally, we illustrate these guidelines using a simple redesign of a NetLogo ABM visualization. These guidelines can be used to inform the development of design tools that assist users in the creation of ABM visualizations.Visualization, Design, Graphics, Guidelines, Communication, Agent-Based Modeling

A Survey for Graphic Design Intelligence

Graphic design is an effective language for visual communication. Using complex composition of visual elements (e.g., shape, color, font) guided by design principles and aesthetics, design helps produce more visually-appealing content. The creation of a harmonious design requires carefully selecting and combining different visual elements, which can be challenging and time-consuming. To expedite the design process, emerging AI techniques have been proposed to automatize tedious tasks and facilitate human creativity. However, most current works only focus on specific tasks targeting at different scenarios without a high-level abstraction. This paper aims to provide a systematic overview of graphic design intelligence and summarize literature in the taxonomy of representation, understanding and generation. Specifically we consider related works for individual visual elements as well as the overall design composition. Furthermore, we highlight some of the potential directions for future explorations.Comment: 10 pages, 2 figure

THE STORYTELLING STYLES AND BRAND PERCEPTION OF WINE LABELS

Based on the past marketing research, it can be stated that stories play a significant role in influencing consumers, since story-based communication has been deemed more effective than stating mere facts (Kelley & Littman 2006). However, especially the link between brand perception and the storytelling of product labels is simply under-researched. (Charters et al. 2000; Kniazeva & Belk 2007.) Therefore, the purpose of this study was to investigate how storytelling on product labels effects consumers’ brand perception. The research questions wanted to discover: what storytelling styles and elements can be found on wine labels, how can the different storytelling elements of wine labels be interpreted/classified and how consumers perceive the stories told by wine label design styles. In order to answer the above-presented questions, methodologically this research was divided into three steps: literature review, content analysis and focus group interview, each step representing one sub-question. The first part contained the identification of different storytelling elements through a literature review. The second sub-question was aimed to group the different storytelling elements according to different wine label design styles by using a content analysis. During the third step, a focus group interview and content analysis were conducted in order to construct a brand experience table. The findings gathered throughout this thesis show that charismatic well-told stories are being characterised by the front label containing the brand name, the main image and the used colours, and the back label the food matching, description for tastes and smells, grape varietal, the colour choices as well as an explication for the brand name within the packaging narrative. This is true no matter the wine label design style. In addition, overall theme or the atmosphere of the main image together with the brand name on the front label play an essential role in determining the wine label design style. On the other hand, the wine label aims to communicate to the consumer, the sense of style of the wine, the occasion for use, the production technique as well as the relevant information about the tastes and the smells of the wine. These attributes are among the most important factors that influence consumers’ brand perception, interpretation of the wines style as well as the occasion for use. Based on the findings, it can be assumed that in order to establish the relationship between story-telling through wine labels and the consumers’ brand perception, the front label needs to set the tone and the atmosphere for the story that the back label helps to fortify, enliven and support. In other words, the front label can be understood as creating expectations that the back label not only needs to confirm but to enhance, support and further convince. As a general conclusion, it can be stated that, more the different elements form a well-design ensemble or the overall impression being congruent in style – in other words, the written and the non-written elements are in line with each other and coherent on both labels – more likely the consumers find the wine brand truthful and desirable, which in turn means that the consumers end up believing the story told by the wine labels.siirretty Doriast

Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the first part, we propose two different solutions to the coronal loop detection problem. The first solution is a block-based coronal loop mining method that detects coronal loops from solar images by dividing the solar image into fixed sized blocks, labeling the blocks as Loop or Non-Loop , extracting features from the labeled blocks, and finally training classifiers to generate learning models that can classify new image blocks. The block-based approach achieves 64% accuracy in IO-fold cross validation experiments. To improve the accuracy and scalability, we propose a contour-based coronal loop detection method that extracts contours from cluttered regions, then labels the contours as Loop and Non-Loop , and extracts geometric features from the labeled contours. The contour-based approach achieves 85% accuracy in IO-fold cross validation experiments, which is a 20% increase compared to the block-based approach. In the second part, we propose a method to extract semi-elliptical open curves from cluttered regions. Our method consists of the following steps: obtaining individual smooth contours along with their saliency measures; then starting from the most salient contour, searching for possible grouping options for each contour; and continuing the grouping until an optimum solution is reached. Our work involved the design and development of a complete system for coronal loop mining in solar images, which required the formulation of new Gestalt perceptual rules and a systematic methodology to select and combine them in a fully automated judicious manner using machine learning techniques that eliminate the need to manually set various weight and threshold values to define an effective cost function. After finding salient contour groups, we close the gaps within the contours in each group and perform B-spline fitting to obtain smooth curves. Our methods were successfully applied on cluttered solar images from TRACE and STEREO/SECCHI to discern coronal loops. Aerial road images were also used to demonstrate the applicability of our grouping techniques to other contour-types in other real applications

Perception in statistical graphics

There has been quite a bit of research on statistical graphics and visualization, generally focused on new types of graphics, new software to create graphics, interactivity, and usability studies. Our ability to interpret and use statistical graphics hinges on the interface between the graph itself and the brain that perceives and interprets it, and there is substantially less research on the interplay between graph, eye, brain, and mind than is sufficient to understand the nature of these relationships. The goal of the work presented here is to further explore the interplay between a static graph, the translation of that graph from paper to mental representation (the journey from eye to brain), and the mental processes that operate on that graph once it is transferred into memory (mind). Understanding the perception of statistical graphics should allow researchers to create more effective graphs which produce fewer distortions and viewer errors while reducing the cognitive load necessary to understand the information presented in the graph. Taken together, these experiments should lay a foundation for exploring the perception of statistical graphics. There has been considerable research into the accuracy of numerical judgments viewers make from graphs, and these studies are useful, but it is more effective to understand how errors in these judgments occur so that the root cause of the error can be addressed directly. Understanding how visual reasoning relates to the ability to make judgments from graphs allows us to tailor graphics to particular target audiences. In addition, understanding the hierarchy of salient features in statistical graphics allows us to clearly communicate the important message from data or statistical models by constructing graphics which are designed specifically for the perceptual system

Moi Helsinki. Personalised user interface solutions for generative data

In the modern days, online search stands out as the most popular way to access a major amount of information. At the same time, browsing through too much data could lead to an information overload. Helping users to feel more individual, as well as appropriately navigating them through the data is an objective designers should raise. In the theoretical background of this work, I bring attention to techniques that allow one to work with generative data and its contextualisation. I study historical and philosophical aspects of information perception, as well as the modern experience of working with online search engines such as Google. I refer to information architecture principles that can adapt user interface designs to generative content. In the age of big data and information pollution, a designer’s objective could be employing technology to make data more human-centred. Along with the theoretical writing, this thesis also consists of project work. Moi Helsinki is a location-based event calendar for the Helsinki area. The calendar gathers information about events retrieved from social media API, and showcases aggregated data in a single feed. Moi Helsinki reshapes the data output with the help of interface personalisation, showing the most relevant results at the top. It employs a user’s current geographical location in order to tailor search results based on proximity for each visitor. The options provided to website visitors within the UI are extended with further customisation, which can be enabled by adjusting the data output beyond just a user’s location. Setting aside certain distinctive features of event calendars, Moi Helsinki chooses another path to explore. Being more of a mediator than proprietor, Moi Helsinki offers a new way to reshape the data and communicate human-centred values through user interface

Visual intelligence for online communities : commonsense image retrieval by query expansion

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (leaves 65-67).This thesis explores three weaknesses of keyword-based image retrieval through the design and implementation of an actual image retrieval system. The first weakness is the requirement of heavy manual annotation of keywords for images. We investigate this weakness by aggregating the annotations of an entire community of users to alleviate the annotation requirements on the individual user. The second weakness is the hit-or-miss nature of exact keyword matching used in many existing image retrieval systems. We explore this weakness by using linguistics tools (WordNet and the OpenMind Commonsense database) to locate image keywords in a semantic network of interrelated concepts so that retrieval by keywords is automatically expanded semantically to avoid the hit-or-miss problem. Such semantic query expansion further alleviates the requirement for exhaustive manual annotation. The third weakness of keyword-based image retrieval systems is the lack of support for retrieval by subjective content. We investigate this weakness by creating a mechanism to allow users to annotate images by their subjective emotional content and subsequently to retrieve images by these emotions. This thesis is primarily an exploration of different keyword-based image retrieval techniques in a real image retrieval system. The design of the system is grounded in past research that sheds light onto how people actually encounter the task of describing images with words for future retrieval. The image retrieval system's front-end and back- end are fully integrated with the Treehouse Global Studio online community - an online environment with a suite of media design tools and database storage of media files and metadata.(cont.) The focus of the thesis is on exploring new user scenarios for keyword-based image retrieval rather than quantitative assessment of retrieval effectiveness. Traditional information retrieval evaluation metrics are discussed but not pursued. The user scenarios for our image retrieval system are analyzed qualitatively in terms of system design and how they facilitate the overall retrieval experience.James Jian Dai.S.M

Visualising collective actions – The process of creating the ArtovaModel

This thesis illustrates the process of designing a visualisation of collective action practices through the case of the ArtovaModel visualisation. The vision was, through this visualisation, to help the Artova neighbourhood association enable further pleasurable collective actions, engage people in a discussion on collective actions or provide common grounds of communication. I aimed to achieve this by firstly helping to compile a vocabulary, a structure and a database of real-life examples from Artova–facilitated collective actions and later making them accessible by visualising the gathered information in a way that it is transparent and modifiable. This thesis project has derived from a combination of data visualisation practices, human- centered design methods and collective action theory. It describes the collaboration with Artova, six collective actions and the independent company Avanto, to compile practices that Artova-facilitated collective actions share. Human-centered design methods were utilised to identify how these practices might aid other collective actions and the Artova association members. Lastly, an online interactive visualisation to communicate the gathered information was created, based on data visualisation practices. The activities employed and their risks and impact at each step of the visualising process are discussed. The thesis concludes with a personal reflection on this journey and lessons learned about visualising collective actions

Dynamic OSINT System Sourcing from Social Networks

In the past, it was humanly impossible to observe and extract large amounts of textual information from web platforms in short periods of time, but the trend has changed and in recent years several surveillance, selection, and extraction of textual information systems have emerged, based on Open­Source Intelligence (OSINT). These platforms became popular among computer security professionals, allowing them to detect new threats and respond in a timely manner by locating, collecting and analysing information made available to the public through social networks, blogs, newspapers, television, etc., proving to be a great advantage in terms of information gathering and a good help with regards to preventing problems, especially in the area of information security. This dissertation focuses on the development of a platform based on OSINT, and has two main objectives. First, to continue the work previously developed in another technology ­ Hypertext Preprocessor (PHP), in which formulas and algorithms were developed to classify posts from Twitter. And second, to present a new platform (using Node JS technology), by applying the formulas from the previous work, evaluating the new platform with users, and improving the user experience (UX). During the development process two versions were provided to the users and hosted on a virtual machine, based on cloud services of Microsoft Azure. The platform architecture is composed by three processes developed in Node JS (one that provides the page, the web server, one that collects the posts, and another one that does the classification of each post). The posts are collected through an API provided by Twitter, and stored and managed in PHPMyAdmin a platform based on MySql database. The User­Centered Design (UCD) was applied during the development process, a process that is focused on the user and his experience. The participation of users has contributed to define new features and to improve the presented layout. Users were included in the testing phase, being called to fill forms, one form for each version. Based on the collected feedback, the following improvements were implemented: the possibility of searching for several topics at the same time, the possibility of havving header monitors by ranges of time, and the possibility of applying filters, such as the number of minutes the posts are available on the screen, and the order by which they are presented.No passado, era humanamente impossível observar e extrair grandes quantidades de informações textuais de plataformas da web em curtos espaços de tempo, mas a tendência mudou e nos últimos anos surgiram diversos sistemas de vigilância baseados na seleção e extração de informação textual proveniente de fontes de informação abertas, denominadas Open­Source Intelligence (OSINT), que se têm tornado populares principalmente entre os profissionais de segurança informática, permitindo a deteção de novas ameaças, a localização e recolha de informação disponível para o público em geral através das redes sociais, blogs, jornais, televisão, etc., revelando­se uma grande vantagem em termos de recolha de informação e uma boa ajuda no que diz respeito à prevenção de problemas principalmente na área de segurança da informação. Esta dissertação foca­se no desenvolvimento de uma plataforma com base em informação open source, dando continuidade a um trabalho anteriormente desenvolvido numa outra tecnologia ­ Hypertext Preprocessor (PHP), onde se apresentaram fórmulas e algoritmos para classificação de posts do Twitter sobre o tema da segurança da informação. Focandose este trabalho no desenvolvimento de novas versões da plataforma com base na tecnologia Node JS, na implementação das fórmulas apresentadas, na melhoria da experiência do utilizador (UX) e na avaliação da plataforma desenvolvida com utilizadores. Durante o desenvolvimento do trabalho foram apresentadas duas versões da plataforma, e hospedadas numa máquina virtual, tornando­as acessíveis aos utilizadores, que na fase final contribuíram com o seu feedback sobre as mesmas. Essa máquina virtual baseia­se em serviços cloud da Microsoft Azure, onde estão instalados três processos desenvolvidos em Node JS (um que disponibiliza a página, um que classifica, e outro que recolhe posts), os posts são recolhidos através de uma API disponibilizada pelo Twitter, e guardados numa base de dados MySql, baseada na plataforma de administração de base de dados PHPMyAdmin, disponibilizando à comunidade as notícias mais recentes e relevantes sobre vários temas. Durante o processo de desenvolvimento teve­se em conta o modelo User­Centered Design (UCD), um processo focado no utilizador e na experiência de utilização. A participação de utilizadores foi assim a chave para a definição das características, e da forma como é apresentado o front­end da plataforma, sendo estes incluídos na fase de testes, com o preenchimento de formulários visando recolher feedback sobre os protótipos desenvolvidos. Com base no feedback recolhido foram implementadas novas melhorias. De todas as mais relevantes foram: a possibilidade de pesquisa por vários temas em simultâneo, a inserção de monitores, e a possibilidade de aplicar filtros, como o número de minutos em que os posts ficam disponíveis no ecrã, e a ordem com que os mesmos devem ser apresentados