Showing papers in "Journal of Visual Languages and Computing in 2017"
TL;DR: The paper explores the current challenges of computers from the cloud to digital fabrication and the need to design for solitude and suggests that HCI should not just react to the changes around it, but also shape those changes.
Abstract: This paper explores the roots of humancomputer interaction as a discipline, the various trends which have marked its development, and some of the current and future challenges for research. Humancomputer interaction, like any vocational discipline, sits upon three broad foundations: theoretical principles, professional practice and a community of people. As an interdisciplinary field the theoretical roots of HCI encompass a number of other disciplines including psychology, computing, ergonomics, and social sciences; however, it also has theoretical and practical challenges of its own. The evolving internal and external context of HCI, computers, have become smaller and less costly; this has led to changes in nature of the users and uses of computers, with corresponding impact on society. The paper explores the current challenges of computers from the cloud to digital fabrication and the need to design for solitude. It suggests that HCI should not just react to the changes around it, but also shape those changes.
77 citations
TL;DR: Existing works on declarative specifications and user interfaces for visualization construction are reviewed by summarizing their methods for producing information visualizations and efforts on improving usability in terms of a design space which describes the tools in several different aspects.
Abstract: Information visualization has been widely used to convey information from data and assist communication. There are enormous needs of efficient visualization design for users from diverse fields to leverage the power of data. As a result, emerging construction tools for information visualization focus on providing solutions with different aspects including expressiveness, accessibility, and efficiency. In this paper, we review existing works on declarative specifications and user interfaces for visualization construction. By summarizing their methods for producing information visualizations and efforts on improving usability, we express the design patterns in terms of a design space which describes the tools in several different aspects. We discuss how the design space can be applied to support further exploration of potential research topics in the future.
67 citations
TL;DR: A socio-technical approach to design is applied to design being able to study the social and the technological aspects of the use of the Internet of Things ecosystem, considering them as closely interconnected and dependent.
Abstract: This paper presents the definition of a visual language and its implementation with the design of a visual interactive system for the collaborative management of Internet of Things (IoT) sensors (e.g., wearable fitness trackers, ambient sensors, fitness apps, nutrition apps, sleep trackers) for improving people's quality of life and promoting wellness awareness. The system, called SmartFit Rule Editor, is designed to be used by coaches and trainers of non-professional teams of athletes for monitoring and analyze fitness and wellness data streams and to support them in detecting relevant events and specifying rules for actions taking. Our research is framed under the scope of computer semiotics and semiotic engineering theories. This allows us to study how to support coaches and trainers as a community of domain experts – but not IT and IoT experts – to use elements of a visual language to indirectly manage physical devices and their data streams without the need to know technical specification of the devices, the apps, and the data. We apply a socio-technical approach to design being able to study the social and the technological aspects of the use of the Internet of Things ecosystem, considering them as closely interconnected and dependent. Such an approach underpins user-centered design and development methodologies in order to design the most suitable User eXperience according to users' culture, needs, context of use, and activity.
37 citations
TL;DR: A succinct visual language ontology is obtained, which captures the essential aspects of visual languages and which can be used to characterize individual languages through visual language profiles.
Abstract: The visual language research community does not have a single, universally agreed-upon definition of exactly what a visual language is. This is surprising since the field of visual languages has been a vibrant research area for over three decades now. Disagreement about fundamental definitions can undermine a field, fragment the research community, and potentially harm the research progress. To address this issue we have analyzed two decades of VL/HCC publications to clarify the concept of visual language as accepted by the VL/HCC community, effectively adopting the approach of descriptive linguistics. As a result we have obtained a succinct visual language ontology, which captures the essential aspects of visual languages and which can be used to characterize individual languages through visual language profiles. These profiles can tell whether and in what sense a notation can be considered a visual language. We also report trends from and statistics about the field of visual languages.
37 citations
TL;DR: This paper investigates how the raise of big data and cognitive computing systems is going to redesign the labor market, also impacting on the learning processes, and depicts a model of a smart university, which relies on the concepts that are at the basis of the novel smart-cities development trends.
Abstract: In this paper, we investigate how the raise of big data and cognitive computing systems is going to redesign the labor market, also impacting on the learning processes. In this respect, we make reference to higher education and we depict a model of a smart university, which relies on the concepts that are at the basis of the novel smart-cities development trends. Thus, we regard education as a process so that we can find specific issues to solve to overcome existing criticisms, and provide some suggestions on how to enhance universities performances. We highlight inputs, outputs, and dependencies in a block diagram, and we propose a solution built on a new paradigm called smarter-university, in which knowledge grows rapidly, is easy to share, and is regarded as a common heritage of both teachers and students. Among the others, a paramount consequence is that there is a growing demand for competences and skills that recall the so called T-shape model and we observe that this is pushing the education system to include a blend of disciplines in the curriculums of their courses. In this overview, among the wide variety of recent innovations, we focus our attention on cognitive computing systems and on the exploitation of big data, that we expect to further accelerate the refurbishment process of the key components of the knowledge society and universities as well.
35 citations
TL;DR: A new technique is presented in which a set of low-dimensional PCPs are interactively constructed by sampling user-selected subsets of the high-dimensional data space by dynamically specifying the most relevant lower dimensional data to be used for the construction of focused PCPs.
Abstract: Parallel coordinate plots (PCPs) are among the most useful techniques for the visualization and exploration of high-dimensional data spaces. They are especially useful for the representation of correlations among the dimensions, which identify relationships and interdependencies between variables. However, within these high-dimensional spaces, PCPs face difficulties in displaying the correlation between combinations of dimensions and generally require additional display space as the number of dimensions increases. In this paper, we present a new technique for high-dimensional data visualization in which a set of low-dimensional PCPs are interactively constructed by sampling user-selected subsets of the high-dimensional data space. In our technique, we first construct a graph visualization of sets of well-correlated dimensions. Users observe this graph and are able to interactively select the dimensions by sampling from its cliques, thereby dynamically specifying the most relevant lower dimensional data to be used for the construction of focused PCPs. Our interactive sampling overcomes the shortcomings of the PCPs by enabling the visualization of the most meaningful dimensions (i.e., the most relevant information) from high-dimensional spaces. We demonstrate the effectiveness of our technique through two case studies, where we show that the proposed interactive low-dimensional space constructions were pivotal for visualizing the high-dimensional data and discovering new patterns.
29 citations
TL;DR: Three empirical studies investigating the use of natural language for computation are described in which it is found that although natural language provides support for understanding computational concepts, it introduces additional difficulties when used for coding.
Abstract: Given the current focus on teaching computational concepts to all from an early age, combined with the growing trend to empower end users to become producers of technology rather than mere consumers, we consider the issue of computational notation Specifically, where the goal is to help individuals develop their understanding of computation and/or use computation in real world settings, we question whether natural language might be a preferred notation to traditional programming languages, given its familiarity and ubiquity We describe three empirical studies investigating the use of natural language for computation in which we found that although natural language provides support for understanding computational concepts, it introduces additional difficulties when used for coding We distilled our findings into a set of design guidelines for novice programming environments which consider the ways in which different notations, including natural language, can best support the various activities that comprise programming These guidelines were embodied in Flip, a bi-modal programming language used in conjunction with the Electron toolset, which allows young people to create their own commercial quality, narrative based role- playing games Two empirical studies on the use of Flip in three different real world contexts considered the extent to which the design guidelines support ease of use and an understanding of computation The guidelines have potential to be of use both in analysing the use of natural language in existing novice programming environments, and in the design of new ones HighlightsNatural language presents problems when used for program generationNatural language is beneficial when used for program comprehension and debuggingNovice programming environments should use multiple notations for support
28 citations
TL;DR: This paper presents rainbow boxes, a novel technique for visualizing overlapping sets, and its application to the presentation of the properties of amino-acids, the comparison of drug properties, and the visualization of gene annotation.
Abstract: Overlapping set visualization is a well-known problem in information visualization. This problem considers elements and sets containing all or part of the elements, a given element possibly belonging to more than one set. A typical example is the properties of the 20 amino-acids. A more complex application is the visual comparison of the contraindications or the adverse effects of several similar drugs. The knowledge involved is voluminous, each drug has many contraindications and adverse effects, some of them are shared with other drugs. Another real-life application is the visualization of gene annotation, each gene product being annotated with several annotation terms indicating the associated biological processes, molecular functions and cellular components. In this paper, we present rainbow boxes, a novel technique for visualizing overlapping sets, and its application to the presentation of the properties of amino-acids, the comparison of drug properties, and the visualization of gene annotation. This technique requires solving a combinatorial optimization problem; we propose a specific heuristic and we evaluate and compare it to general optimization algorithms. We also describe a user study comparing rainbow boxes to tables and showing that the former allowed physicians to find information significantly faster. Finally, we discuss the limits and the perspectives of rainbow boxes.
25 citations
TL;DR: A methodology to instrument the city via the placement of Wi-Fi Access Points, AP, and to use them as sensors to capture and understand city user behaviour with a significant precision rate is presented.
Abstract: Monitoring, understanding and predicting city user behaviour (hottest places, trajectories, flows, etc.) is one the major topics in the context of Smart City management. People flow surveillance provides valuable information about city conditions, useful not only for monitoring and controlling the environmental conditions, but also to optimize the deliverying of city services (security, clean, transport,..). In this context, it is mandatory to develop methods and tools for assessing people behaviour in the city. This paper presents a methodology to instrument the city via the placement of Wi-Fi Access Points, AP, and to use them as sensors to capture and understand city user behaviour with a significant precision rate (the understanding of city user behaviour is concretized with the computing of heat-maps, origin destination matrices and predicting user density). The first issue is the positioning of Wi-Fi AP in the city, thus a comparative analyses have been conducted with respect to the real data (i.e., cab traces) of the city of San Francisco. Several different positioning methodologies of APs have been proposed and compared, to minimize the cost of AP installation with the aim of producing the best origin destination matrices. In a second phase, the methodology was adopted to select suitable AP in the city of Florence (Italy), with the aim of observing city users behaviour. The obtained instrumented Firenze Wi-Fi network collected data for 6 months. The data has been analysed with data mining techniques to infer similarity patterns in AP area and related time series. The resulting model has been validated and used for predicting the number of AP accesses that is also related to number of city users. The research work described in this paper has been conducted in the scope of the EC funded Horizon 2020 project Resolute ( http://www.resolute-eu.org ), for early warning and city resilience.
24 citations
TL;DR: Teachers’ design of VLE learning activities as end user development is approached, showing that some teachers design very specific learning activities using the VLE - not by using the dedicated VLE tool, but by reinterpreting more generic tools.
Abstract: In research on Virtual Learning Environments (VLEs), it has been shown that teachers often do not explore VLEs to their full potential and only adopt a limited set of the available tools. In this article, we approach teachers’ design of VLE learning activities as end user development. We describe a study of Toledo, a virtual learning environment used across several higher education institutions in Belgium. Using a combination of a semiotic, multimodal analysis and an in-depth user study with 24 respondents, we provide a detailed account of how teachers appropriate the learning environment to suit their needs. Combining the insights from the semiotic investigation and the user research, we analyze how user appropriations can be explained as practices emerging from both how the platform communicates, and contextual factors. The study showed that some teachers design very specific learning activities using the VLE - not by using the dedicated VLE tool, but by reinterpreting more generic tools. These appropriation tactics concentrate platform use in a limited number of tools, even when teachers do use more complex learning activities. These results have implications for the design of VLEs: rather than offering a wide range of tools targeted at specific learning activities, VLEs could concentrate on providing basic communication tools that are open for appropriation.
24 citations
TL;DR: The results show that high level primitives, with a close mapping to social interaction, are suitable for programming social robot applications, however, the abstraction level should not be so high that it takes away too much control from programmers.
Abstract: Whilst robots are increasingly being deployed as social agents, it is still difficult to program them to interact socially. To create usable tools for programming these robots, tool developers need to know what abstraction levels are appropriate for programming social robot applications. We explore this through the iterative design and evaluation of an API for programming social robots. The results show that high level primitives, with a close mapping to social interaction, are suitable for programming social robot applications. However, the abstraction level should not be so high that it takes away too much control from programmers. This has the potential to enable programmers to produce high quality social robot applications with less programming effort. HighlightsWe seek abstraction levels appropriate for programming social robot applications.High level primitives with a close mapping to social interaction are appropriate.But the abstraction level should not be so high that it takes away too much control.Benefits: a close mapping, high abstraction level and high local visibility.Negative effect: low remote visibility, making progressive evaluation harder.
TL;DR: This paper reinforces conventional association rule mining process by mapping the entire process into a visualization assisted loop, with which the user workload for modulating parameters and mining rules is reduced, and the mining efficiency is greatly improved.
Abstract: Association rules have been widely used for detecting relations between attribute-value pairs of categorical datasets. Existing solutions of mining interesting association rules are based on the support-confidence theory. However, it is non-trivial for the user to understand and modify the rules or the results of intermediate steps in the mining process, because the interestingness of rules might differ largely for various tasks and users. In this paper we reinforce conventional association rule mining process by mapping the entire process into a visualization assisted loop, with which the user workload for modulating parameters and mining rules is reduced, and the mining efficiency is greatly improved. A hierarchical matrix-based visualization technique is proposed for the user to explore the measure value and the intermediate results of association rules. We also design a set of visual exploration tools to support interactively inspection and manipulation of mining process. The effectiveness and usability of our approach is demonstrated with two scenarios.
TL;DR: It is suggested that it is important to be aware and take into account experience and UML familiarity before using object diagrams in software modeling and the use of object diagrams does not always introduce significant benefits in terms of design comprehensibility.
Abstract: Objective : The main objective of our study is to assess whether the use of UML (Unified Modeling Language) object diagrams improves comprehensibility of software design when this kind of diagrams is added to UML class diagrams. Method : We have conducted a family of four controlled experiments. We involved groups of bachelor and master students. Results : Results suggest that the use of object diagrams does not always introduce significant benefits in terms of design comprehensibility. We found that benefits strongly depend on the experience of participants and their familiarity with UML. More experienced participants achieved better design comprehensibility when provided with both class and object diagrams, while less experienced seemed to be damaged when using class and object diagrams together. Results also showed the absence of substantial variations in the time needed to comprehend UML models, with or without object diagrams. Implications : Our results suggest that it is important to be aware and take into account experience and UML familiarity before using object diagrams in software modeling.
TL;DR: A visual analytics method that supports the visual analysis of flood risk from multiple aspects, including predicted flood peak flow, flood propagation, flood impact, and vulnerability is proposed.
Abstract: World cultural heritage is the accumulation and essence of the development of human civilization, as well as the rare and irreplaceable treasures bestowed by history However, cultural heritage is increasingly exposed to various risks caused by natural and man-made factors Flood risk is the most common and the most devastating risk for cultural heritage This study proposes a visual analytics method that supports the visual analysis of flood risk from multiple aspects, including predicted flood peak flow, flood propagation, flood impact, and vulnerability The proposed method can also provide the required information from multiple scales, including the basin-, site-, multi-cave-, and single-cave-scale levels The combination of the visualization techniques of flood risk analysis will enable the proposed method to support users to make decisions with respect to mitigation measures Lastly, the proposed method is evaluated by water experts and cultural heritage site managers
TL;DR: A Brazilian CTA program guided by semiotic principles is reported on and a study of how the technology used in it prefigures elements of software engineering in the participants’ programs created with AgentSheets is described.
Abstract: Nonprofessional end user programs have increased remarkably in volume and diversity. However, for such programs to be usable and reliable, their creators should be familiar with software engineering practices that are typically not part of their range of competence and source of enjoyment. While the expansion of computational thinking acquisition (CTA) initiatives at schools and the availability of improved programming environments have contributed to facilitate the learners’ coding tasks, much less has been done to facilitate the acquisition of software quality notions. This paper reports on a Brazilian CTA program guided by semiotic principles and describes a study of how the technology used in it prefigures elements of software engineering in the participants’ programs created with AgentSheets. Our research contributions touch on the semiotic potential of CTA infrastructures and on associated pedagogical considerations for expanding CTA programs with software engineering basics. We also propose items for an interdisciplinary research agenda.
TL;DR: The design and development of the visual interaction language made available in ImAtHome is described, for empowering end users, without programming skills, to create event-condition-action rules that control home behavior.
Abstract: ImAtHome is an iOS application for smart home configuration and management built over Apple HomeKit, a framework for communicating with and controlling home automation accessories. This paper describes the design and development of the visual interaction language made available in ImAtHome for empowering end users, without programming skills, to create event-condition-action rules that control home behavior. It can be regarded as an alternative approach to traditional trigger-action programming interfaces, where the user must define such rules by means of “if-then” constructs. Last but not least, attention has been put to make the interaction style as much coherent as possible with other iOS applications. The paper finally presents a user experiment, carried out with 30 participants according to a between-subject protocol, to evaluate the usability of ImAtHome and compare it with the official app for home automation recently released by Apple.
TL;DR: An interactive visual analytic system for exploring complex flows generated by PBS is presented, helpful for visually classifying stations with different flow patterns, speculating in-depth reasons, as well as investigating abnormal behaviors, helping decision makers to gain a better understanding of the large dataset.
Abstract: Public Bicycle System(PBS) is an increasingly popular mode of public transit, with the advantage of pollution-free and flexibility. In this paper, we present an interactive visual analytic system for exploring complex flows generated by PBS. Four inter-linked visualization views are designed to illustrate multiple perspectives of data, such as the spatial-temporal changes, the relationships and differences between flow OD pairs and the multi-dimensional factors(weather condition, calendar events) influencing on the rental numbers. A new presentation “Parallel coordinates with line and set” combined with flexible interaction schemes is proposed to support the exploration of multivariate association. We exemplify our approach with a real citywide PBS dataset. The results of case study demonstrate that our system is helpful for visually classifying stations with different flow patterns, speculating in-depth reasons, as well as investigating abnormal behaviors, helping decision makers to gain a better understanding of the large dataset.
TL;DR: The improved Pearson correlation coefficient and mutual information correlation analysis respectively are introduced to detect the dimensions’ linear and non-linear correlations and used as the criteria for interactive ordering of axes in parallel coordinate displays.
Abstract: With the era of data explosion coming, multidimensional visualization, as one of the most helpful data analysis technologies, is more frequently applied to the tasks of multidimensional data analysis. Correlation analysis is an efficient technique to reveal the complex relationships existing among the dimensions in multidimensional data. However, for the multidimensional data with complex dimension features,traditional correlation analysis methods are inaccurate and limited. In this paper, we introduce the improved Pearson correlation coefficient and mutual information correlation analysis respectively to detect the dimensions’ linear and non-linear correlations. For the linear case,all dimensions are classified into three groups according to their distributions. Then we correspondingly select the appropriate parameters for each group of dimensions to calculate their correlations. For the non-linear case,we cluster the data within each dimension. Then their probability distributions are calculated to analyze the dimensions’ correlations and dependencies based on the mutual information correlation analysis. Finally,we use the relationships between dimensions as the criteria for interactive ordering of axes in parallel coordinate displays.
TL;DR: This work proposes hyper-graph and the visualization of it to describe the structure of subspaces and proposes a dimension relevance measure to indicate the cluster significance in the corresponding subspace.
Abstract: The proposed work aims at visual subspace clustering and addresses two challenges: an efficient visual subspace clustering workflow and an intuitive visual description of subspace structure. Handling the first challenge is to escape the circular dependency between detecting meaningful subspaces and discovering clusters. We propose a dimension relevance measure to indicate the cluster significance in the corresponding subspace. The dynamic dimension relevance guides the subspace exploring in our visual analysis system. To address the second challenge, we propose hyper-graph and the visualization of it to describe the structure of subspaces. Dimension overlapping between subspaces and data overlapping between clusters are clearly shown with our visual design. Experimental results demonstrate that our approach is intuitive, efficient, and robust in visual subspace clustering.
TL;DR: This document is the Accepted Manuscript version and the final, definitive version is available online at doi.org/10.1016/j.jvlc.2016.11.002.
Abstract: This document is the Accepted Manuscript version. Under embargo until 8 June 2018. The final, definitive version is available online at doi: https://doi.org/10.1016/j.jvlc.2016.11.002, published by Elsevier Ltd.
TL;DR: An efficient unsupervised method based on graph partition for automatically segmenting motion capture data, using t-nearest neighbors and the Nystrom method, and an energy function to refine the results of behavioral segmentation is introduced.
Abstract: With the development of human motion capture, realistic human motion capture data has been widely implemented to many fields. However, segmenting motion capture data sequences manually into distinct behavior is time-consuming and laborious. In this paper, we introduce an efficient unsupervised method based on graph partition for automatically segmenting motion capture data. For N-Frame motion capture data sequence, we construct an undirected, weighted graph G = G ( V , E ) , where the node set V represent frames of motion sequence and the weight of the edge set E describes similarity between frames. In this way, behavioral segmentation problem can be transformed into graph cut problem. However, traditional graph cut problem is NP hard. By analyzing the relationship between graph cut and spectral clustering, we apply spectral clustering to the NP hard problem of graph cut. In this paper, two methods of spectral clustering, t-nearest neighbors and the Nystrom method, are employed to cluster motion capture data for getting behavioral segmentation. In addition, we define an energy function to refine the results of behavioral segmentation. Extensive experiments are conducted on the dataset of multi-behavior motion capture data from CMU database. The experimental results prove that our novel method is robust and effective.
TL;DR: A cluster-aware arrangement method of the parallel coordinate plots is proposed and a visualization framework for the multi-dimensional data exploration is designed, which largely highlights the relations of categories across dimensions.
Abstract: The dimension ordering of parallel coordinate plots has been widely studied, aiming at the insightful exploration of multi-dimensional data. However, few works focus on the category distributions across dimensions and construct an effective dimension ordering to enable the visual exploration of clusters. Therefore, we propose a cluster-aware arrangement method of the parallel coordinate plots and design a visualization framework for the multi-dimensional data exploration. Firstly, a hierarchical clustering scheme is employed to identify the categories of interest across different dimensions. Then we design a group of icicle views to present the hierarchies of dimensions, the colors of which also indicate the relationships between different categories. A cluster-aware correlation is defined to measure the relationships between different attribute axes, based on the distributions of categories. Furthermore, a matrix map is designed to present the relationships between dimensions, and the MDS method is employed to transform the dimensions into 2D coordinates, in which the correlations among the dimensions are conserved. At last, we solve the Traveling Salesman Problem (TSP) and achieve an automated dimension ordering of the parallel coordinate plots, which largely highlights the relations of categories across dimensions. A set of convenient interactions are also integrated in the visualization system, allowing users to get insights into the multi-dimensional data from various perspectives. A large number of experimental results and the credible user studies further demonstrate the usefulness of the cluster-aware arrangement of the parallel coordinate plots.
TL;DR: The results provide a first report of systematic biases in relative size judgment in tag clouds, and suggest that, to a first approximation, simple power-law scaling models developed for simple displays containing 1–2 objects on a blank background, may be applicable to relative size judgments in complex tag clouds.
Abstract: This paper focuses on viewers’ perception of the relative size of words presented in tag clouds. Tag clouds are a type of visualization that displays the contents of a document as a cluster (cloud) of key words (tags) with frequency (importance) indicated by tag word features such as size or color, with variation of size within a tag cloud being the most common indicator of tag importance. Prior studies have shown that word size is the most influential factor of tag importance and tag memory. Systematic biases in relative size perception in tag clouds are therefore likely to have important implications for viewer understanding of tag cloud visualizations. Significant under- and over-perception of the relative size of tag words were observed, depending on the relative size ratio of the target tag words compared. The qualitative change in the direction of the estimation bias was predicted by a simple power-law model for size perception. This bias in relative size perception was modulated somewhat by a change to a bold typeface, but the typeface effect varied in a complex manner with the size and location of the tags. The results provide a first report of systematic biases in relative size judgment in tag clouds, suggest that, to a first approximation, simple power-law scaling models developed for simple displays containing 1–2 objects on a blank background, may be applicable to relative size judgments in complex tag clouds. The results may provide useful design guidance for tag cloud designers.
TL;DR: The first visualization targeting MDP testing, MDPvis, is presented and it is shown the visualization's generality by connecting it to two reinforcement learning frameworks that implement many different MDPs of interest in the research community.
Abstract: Markov Decision Processes (MDPs) are a formulation for optimization problems in sequential decision making Solving MDPs often requires implementing a simulator for optimization algorithms to invoke when updating decision making rules known as policies The combination of simulator and optimizer are subject to failures of specification, implementation, integration, and optimization that may produce invalid policies We present these failures as queries for a visual analytic system (MDPVIS) MDPVIS addresses three visualization research gaps First, the data acquisition gap is addressed through a general simulator-visualization interface Second, the data analysis gap is addressed through a generalized MDP information visualization Finally, the cognition gap is addressed by exposing model components to the user MDPVIS generalizes a visualization for wildfire management We use that problem to illustrate MDPVIS and show the visualization's generality by connecting it to two reinforcement learning frameworks that implement many different MDPs of interest in the research community HighlightsMarkov decision processes (MDPs) formalize sequential decision optimization problemsComplex simulators often implement MDPs and are subject to a variety of bugsInteractive visualizations support testing MDPs and optimization algorithmsThe first visualization targeting MDP testing, MDPvis, is presented
TL;DR: This paper reports on the results of user studies with a series of map-like visualisations, and reveals the factors that have an impact on the human perception of visualisations that are designed to resemble geographic maps and proposes design suggestions for building realistic map- like visualisations.
Abstract: Maps have traditionally been used for displaying geographical information However, apart from this obvious purpose, the metaphor of maps has been applied to other uses, such as information visualisation and novel user interfaces, since the map metaphor is easy-to-understand and allows users to explore data intuitively There are several methods for creating these map-like visualisations and user interfaces, but there is little understanding on how people perceive these non-geographical maps, and how to make the visualisation output more realistic As such, we aim to find preliminary answers on these issues by conducting user studies with a series of map-like visualisations In this paper, we report on the results of the studies and reveal the factors that have an impact on the human perception of visualisations that are designed to resemble geographic maps Based on these results, we propose design suggestions for building realistic map-like visualisations
TL;DR: A set of principles on how to help end-user programmers like this learn just a little when they need to overcome a barrier are presented, and a generalized architecture to facilitate the inclusion of Idea Gardens into other systems is presented.
Abstract: Many systems are designed to help novices who want to learn programming, but few support those who are not necessarily interested in learning programming. This paper targets the subset of end-user programmers (EUPs) in this category. We present a set of principles on how to help EUPs like this learn just a little when they need to overcome a barrier. We then instantiate the principles in a prototype and empirically investigate them in three studies: a formative think-aloud study, a pair of summer camps attended by 42 teens, and a third summer camp study featuring a different environment attended by 48 teens. Finally, we present a generalized architecture to facilitate the inclusion of Idea Gardens into other systems, illustrating with examples from Idea Garden prototypes. Results have been very encouraging. For example, under our principles, Study #2s camp participants required significantly less in-person help than in a previous camp to learn the same amount of material in the same amount of time. The Idea Garden, based on 7 principles, supports several dimensions of diversity.The Idea Garden helps stuck EUPs by providing just-in-time problem-solving support.Three separate environments have hosted versions of the Idea Garden.Each version was empirically evaluated for effectiveness.The Idea Garden can be ported to other environments via a generalized architecture.
TL;DR: Gneiss is a tool that extends the familiar spreadsheet metaphor to support using structured web service data and introduces a novel visualization that represents hierarchies in data using nested spreadsheet cells and allows users to easily reshape and regroup the extracted structured data.
Abstract: Web services offer a more reliable and efficient way to access online data than scraping web pages. However, interacting with web services to retrieve data often requires people to write a lot of code. Moreover, many web services return data in complex hierarchical structures that make it difficult for people to perform any further data manipulation. We developed Gneiss, a tool that extends the familiar spreadsheet metaphor to support using structured web service data. Gneiss lets users retrieve or stream arbitrary JSON data returned from web services to a spreadsheet using interaction techniques without writing any code. It introduces a novel visualization that represents hierarchies in data using nested spreadsheet cells and allows users to easily reshape and regroup the extracted structured data. Data flow is two-way between the spreadsheet and the web services, enabling people to easily make a new web service call and retrieve new data by modifying spreadsheet cells. We report results form a user study that showed that Gneiss helped spreadsheet users use and analyze structured data more efficiently than Excel and even outperform professional programmers writing code. We further use a set of examples to demonstrate our tool's ability to create reusable data extraction and manipulation programs that work with complex web service data.
TL;DR: In this paper, the authors present a methodology based on a visual graph transformation and graph constraint language, for developing incremental topology control algorithms that are guaranteed to fulfill a set of specified consistency and optimization constraints.
Abstract: Communication networks form the backbone of our society. Topology control algorithms optimize the topology of such communication networks. Due to the importance of communication networks, a topology control algorithm should guarantee certain required consistency properties (e.g., connectivity of the topology), while achieving desired optimization properties (e.g., a bounded number of neighbors). Real-world topologies are dynamic (e.g., because nodes join, leave, or move within the network), which requires topology control algorithms to operate in an incremental way, i.e., based on the recently introduced modifications of a topology. Visual programming and specification languages are a proven means for specifying the structure as well as consistency and optimization properties of topologies. In this paper, we present a novel methodology, based on a visual graph transformation and graph constraint language, for developing incremental topology control algorithms that are guaranteed to fulfill a set of specified consistency and optimization constraints. More specifically, we model the possible modifications of a topology control algorithm and the environment using graph transformation rules, and we describe consistency and optimization properties using graph constraints. On this basis, we apply and extend a well-known constructive approach to derive refined graph transformation rules that preserve these graph constraints. We apply our methodology to re-engineer an established topology control algorithm, kTC, and evaluate it in a network simulation study to show the practical applicability of our approach.
TL;DR: A novel calculation method of personality based on Chinese physiognomy to understand the relationship between personality and facial features and to model a baseline to shape facial features is proposed.
Abstract: This paper proposes a novel calculation method of personality based on Chinese physiognomy. The proposed solution combines ancient and modern physiognomy to understand the relationship between personality and facial features and to model a baseline to shape facial features. We compute a histogram of image by searching for threshold values to create a binary image in an adaptive way. The two-pass connected component method indicates the feature’s region. We encode the binary image to remove the noise point, so that the new connected image can provide a better result. According to our analysis of contours, we can locate facial features and classify them by means of a calculation method. The number of clusters is decided by a model and the facial feature contours are classified by using the k-means method. The validity of our method was tested on a face database and demonstrated by a comparative experiment.
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TL;DR: An iconic language, named MicroApp, is described, for modeling pervasive mobile applications directly on the mobile device and qualitatively evaluate the visual environment that implements this iconic language.
Abstract: After tracing the steps that led to the current generation of iconic languages starting from the original idea ofS.K. Chang, we describe an iconic language, named MicroApp, for modeling pervasive mobile applications directly on the mobile device. MicroApp exploits generalized icons for composing mobile applications: services are represented by icons and are composed of adopting colors for representing data-flow. We also qualitatively evaluate the visual environment that implements this iconic language.