Computer graphics techniques for modeling page turning

Turning the page is a mechanical part of the cognitive act of reading that we do literally unthinkingly. Interest in realistic book models for digital libraries and other online documents is growing. Yet actually producing a computer graphics implementation for modeling page turning is a challenging undertaking. There are many possible foundations: two-dimensional models that use reflection and rotation; geometrical models using cylinders or cones; mass-spring models that simulate the mechanical properties of paper at varying degrees of fidelity; finite-element models that directly compute the actual forces within a piece of paper. Even the simplest methods are not trivial, and the more sophisticated ones involve detailed physical and mathematical models. The variety, intricacy and complexity of possible ways of simulating this fundamental act of reading is virtually unknown. This paper surveys computer graphics models for page turning. It combines a tutorial introduction that covers the range of possibilities and complexities with a mathematical synopsis of each model in sufficient detail to serve as a basis for implementation. Illustrations are included that are generated by our implementations of each model. The techniques presented include geometric methods (both two- and three-dimensional), mass-spring models with varying degrees of accuracy and complexity, and finite-element models. We include a detailed comparison of experimentally-determined computation time and subjective visual fidelity for all methods discussed. The simpler techniques support convincing real-time implementations on ordinary workstations

Moderated Network Models

Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research this is often implausible. In this paper, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct the MNM and propose an L1-regularized nodewise regression approach to estimate it. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification

Modelling benefits-oriented costs for technology enhanced learning

The introduction of technology enhanced learning (TEL) methods changes the deployment of the most important resource in the education system: teachers' and learners' time. New technology promises greater personalization and greater productivity, but without careful modeling of the effects on the use of staff time, TEL methods can easily increase cost without commensurate benefit. The paper examines different approaches to comparing the teaching time costs of TEL with traditional methods, concluding that within-institution cost-benefit modeling yields the most accurate way of understanding how teachers can use the technology to achieve the level of productivity that makes personalisation affordable. The analysis is used to generate a set of requirements for a prospective, rather than retrospective cost-benefit model. It begins with planning decisions focused on realizing the benefits of TEL, and uses these to derive the likely critical costs, hence the reversal implied by a 'benefits-oriented cost model'. One of its principal advantages is that it enables innovators to plan and understand the relationship between the expected learning benefits and the likely teaching costs

Machine Learning Aided Static Malware Analysis: A Survey and Tutorial

Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in technical Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware analysis that has been thoroughly studied before. In this paper, we address this research gap by conducting an in-depth survey of different machine learning methods for classification of static characteristics of 32-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and evaluate accuracy and practical generalization of these techniques. Finally, the results of experimental study of all the method using common data was given to demonstrate the accuracy and complexity. This paper may serve as a stepping stone for future researchers in cross-disciplinary field of machine learning aided malware forensics.Comment: 37 Page

A pedagogical framework for embedding C&IT into the curriculum

This paper proposes a methodology for effectively embedding communication and information technologies (C&IT) into the curriculum. This builds on existing frameworks for designing courses involving C&IT. A hypothetical illustration of this process is provided, and issues relating to the adoption and application of the methodology are identified