Using Sugiyama-styled Graphs to Directly Manipulate Role-Based Access Control Configurations

Classical Role-Based Access Control (RBAC) software lacks options to allow users to gain a deeper understanding of RBAC configurations. Users need to comprehend a configuration to efficiently maintain and manipulate it. We developed a RBAC visualization based on a Sugiyama-styled graph. Our visualization allows RBAC untrained users to understand and manipulate RBAC configurations

An approach to the visualization of adaptive hypermedia structures and other small-world networks based on hierarchically Clustered Graphs

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, julio de 200

Task-based Adaptation of Graphical Content in Smart Visual Interfaces

To be effective visual representations must be adapted to their respective context of use, especially in so-called Smart Visual Interfaces striving to present specifically those information required for the task at hand. This thesis proposes a generic approach that facilitate the automatic generation of task-specific visual representations from suitable task descriptions. It is discussed how the approach is applied to four principal content types raster images, 2D vector and 3D graphics as well as data visualizations, and how existing display techniques can be integrated into the approach.Effektive visuelle Repräsentationen müssen an den jeweiligen Nutzungskontext angepasst sein, insbesondere in sog. Smart Visual Interfaces, welche anstreben, möglichst genau für die aktuelle Aufgabe benötigte Informationen anzubieten. Diese Arbeit entwirft einen generischen Ansatz zur automatischen Erzeugung aufgabenspezifischer Darstellungen anhand geeigneter Aufgabenbeschreibungen. Es wird gezeigt, wie dieser Ansatz auf vier grundlegende Inhaltstypen Rasterbilder, 2D-Vektor- und 3D-Grafik sowie Datenvisualisierungen anwendbar ist, und wie existierende Darstellungstechniken integrierbar sind

Retail property market performance of cities: an investigation of the relationships between spatial configuration of consumer movement and changes in retail stock and value in Leeds, Newcastle and York

The influence of consumer activities on the performance of retail locations and retail property market in cities can be critical. This is because where and how retail consumers choose to transact influences the locational performance of retail property markets in cities. This study investigates relationships between consumer movement and the performance of retail property markets (RPM) between 2010 and 2017 in York, Leeds and Newcastle. The study adopts the spatial configuration (street segment) analysis technique to compute consumer movement patterns (CMP) on the sampled cities’ layouts using DepthMapX to obtain the CMP variables; specifically, integration, choice and NACH metrics. The RPM data were sourced from valuation summary lists belonging to the VOA dataset and analysed using MS Access and MS Excel to obtain RPM variables, namely, changes in retail rental value and changes in retail stock across locations. The study investigates the spatial and statistical relationships between the CMP and RPM variables of cities at mesoscales and macroscales using QGIS and SPSS tools, respectively. The spatial investigations visualise locational relationships between changes in RPM variables and the spatial accessibility index of the CMP variables. The statistical analyses adopted Spearman-rho coefficients to investigate the rank correlation between the RPM and CMP variables. Further statistical (multiple regression) analysis were undertaken to estimate the locational performance of the RPM (dependent variable) using the CMP (independent variables) across all the estimable city layouts. Findings show that there are significant relationships between changes in retail rental value and all the CMP variables at York mesoscale, Leeds mesoscale and Newcastle macroscale. The results indicate that the relationship between configured consumer movement and changes in retail rental value are influenced by scale and city characteristics. The research is the first to estimate the location performance of commercial property by way of spatial configuration analysis. The research outputs are useful tools for retail property market actors to make locational decisions on investments, occupation, development and the strategic management of urban retail space. The study recommends further studies on the prospects of spatial configuration analysis and other methods in estimating the future performance of the commercial property market for optimum utilisation and the management of urban resources

Social Comparisons in the Classroom Revisited: Insights Into Underlying Processes Using Immersive Virtual Reality as a Research Tool

Social comparisons are commonplace in every classroom and widely acknowledged as central determinants of students’ academic self-evaluations (see, e.g., Dijkstra et al., 2008; Trautwein & Möller, 2016). Most prominently, in educational psychology research, social comparisons have been assumed to be the cause behind the well-known Big-Fish-Little-Pond effect (BFLPE; Marsh, 1987), suggesting negative effects of higher class-average (or school-average) achievement on students’ academic self-concept while controlling for individual achievement. Whereas existing research has provided compelling evidence of the effects of certain reference groups on students’ self-evaluations (Marsh et al., 2017; Marsh & Seaton, 2015), the actual mechanisms behind the proposed effects and how students process social information while learning are still a black box. The present dissertation was aimed at gaining insights into the respective underlying processes (i.e., the “inner workings” of this black box) by using immersive virtual reality (IVR) as a research tool. IVR technology provides an unprecedent opportunity for educational psychology research to integrate ecological validity and experimental control in research designs to gain authentic and yet standardized insights into classroom processes, such as social comparisons and beyond (see, e.g., Blascovich et al., 2002). To this end, the present dissertation was aimed at a theoretical as well as a methodological advancement of research on social comparisons in the classroom. To address these objectives, the dissertation drew on three empirical studies with an IVR classroom including an experimental manipulation of classmates’ performance-related behavior. First, pursuing a more in-depth theoretical understanding of social comparisons and the respective processing of social information in the classroom, the dissertation aimed to identify covert and overt social comparison behaviors that (a) reflect students’ cognitive and behavioral responses to social comparison information in an IVR classroom and (b) ultimately explain individual differences in students’ self-concepts. Studies 1 and 2 used students’ self-reports (of their interpretation of classmates’ performance-related behavior) and eye movement data (e.g., visual attention on classmates) to identify different social comparison processes in the IVR classroom and to provide insights into the mechanisms that underlie the BFLPE. Second, aiming to provide insights into how IVR classrooms can be used as an experimental tool in educational psychology research, Study 3 focused on the configuration of an IVR classroom to authentically simulate and control a (social) classroom environment. The study provides insights into how different fields of view, virtual avatar visualization styles and virtual classmates’ performance-related behaviors affect students’ processing of social information provided in the IVR classroom. Taken together, by using an IVR classroom as an experimentally controlled yet authentic research setting, the present dissertation was able to advance the theoretical understanding of social comparisons and respective processing of social information in the classroom that ultimately explain individual differences in students’ self-concept. Moreover, the present dissertation demonstrates how IVR classrooms and the corresponding standardized process data can be used to gain insights into classroom processes, such as social comparisons. The dissertation thereby provides implications for research on both social comparisons in the classroom and the use of IVR as an experimental tool in educational and social psychology research in general

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Deep Networks and Knowledge: from Rule Learning to Neural-Symbolic Argument Mining

Deep Learning has revolutionized the whole discipline of machine learning, heavily impacting fields such as Computer Vision, Natural Language Processing, and other domains concerned with the processing of raw inputs. Nonetheless, Deep Networks are still difficult to interpret, and their inference process is all but transparent. Moreover, there are still challenging tasks for Deep Networks: contexts where the success depends on structured knowledge that can not be easily provided to the networks in a standardized way. We aim to investigate the behavior of Deep Networks, assessing whether they are capable of learning complex concepts such as rules and constraints without explicit information, and then how to improve them by providing such symbolic knowledge in a general and modular way. We start by addressing two tasks: learning the rule of a game and learning to construct the solution to Constraint Satisfaction Problems. We provide the networks only with examples, without encoding any information regarding the task. We observe that the networks are capable of learning to play by the rules and to make feasible assignments in the CSPs. Then, we move to Argument Mining, a complex NLP task which consists of finding the argumentative elements in a document and identifying their relationships. We analyze Neural Attention, a mechanism widely used in NLP to improve networks' performance and interpretability, providing a taxonomy of its implementations. We exploit such a method to train an ensemble of deep residual networks and test them on four different corpora for Argument Mining, reaching or advancing the state of the art in most of the datasets we considered for this study. Finally, we realize the first implementation of neural-symbolic argument mining. We use the Logic Tensor Networks framework to introduce logic rules during the training process and establish that they give a positive contribution under multiple dimensions