Abstract This paper reports on the experiences of using an early assessment intervention, specifically employing a Use-Modify-Create scaffold, to teach first-year undergraduate functional programming. The particular intervention that was trialled was the use of an early assessment instrument, in which students had to use code given to them, or slightly modify it, to achieve certain goals. The intended outcome was that the students would thus engage earlier with the functional language, enabling them to be better prepared for the second piece of assessment, where they create code to solve given problems. This intervention showed promise: the difference between a student’s score on the Create assignment improved by an average of 9% in the year after the intervention was implemented, a small effect.

Comprehension of justifications is known to be difficult for even experienced ontology engineers, and much more so for other stakeholders. In this paper, we present two methods for displaying justifications using concept diagrams: using multiple concept diagrams to represent the justification (one diagram for each axiom); and using a merged concept diagram to represent all axioms in the justification. We performed an empirical evaluation of both methods along with a textual representation of the justification using Protégé. The results were that novice users could both more accurately and more quickly identify an incoherence when using merged diagrams than using multiple diagrams or Protégé statements.

Clutter in a diagram can be broadly defined as how visually complex the diagram is. It may be that different users perceive clutter in different ways, however. Moreover, it has been shown that, for certain types of diagrams and tasks, an increase in clutter negatively affects task performance, making quantifying clutter an important problem. In this paper we investigate the perceived clutter in concept diagrams, a visual language used for representing ontologies. Using perceptual theory and existing research on clutter for other diagrams, we propose five plausible measures for assigning clutter scores to concept diagrams. By performing an empirical study we evaluated each of these proposed measures against participants' rankings of diagrams. Whilst more than one of our measures showed strong correlation with perceived clutter, our results suggest that a measure based on the number of points where lines cross is the most appropriate way to quantify clutter for concept diagrams.

This paper presents the first design principles that optimize the visualization of sets using linear diagrams. These principles are justified through empirical studies that evaluate the impact of graphical features on task performance. Linear diagrams represent sets using straight line segments, with line overlaps corresponding to set intersections. This study builds on recent empirical research, which establishes that linear diagrams can be superior to prominent set visualization techniques, namely Euler and Venn diagrams. We address the problem of how to best visualize overlapping sets using linear diagrams. To solve the problem, we investigate which graphical features of linear diagrams significantly impact user task performance. To this end, we conducted seven crowdsourced empirical studies involving a total of 1,760 participants. These studies allowed us to identify the following design principles, which significantly aid task performance: use a minimal number of line segments, use guidelines where overlaps start and end, and draw lines that are thin as opposed to thick bars. We also evaluated the following graphical properties that did not significantly impact task performance: color, orientation, and set order. The results are brought to life through a freely available software implementation that automatically draws linear diagrams with user-controlled graphical choices. An important consequence of our research is that users are now able to create effective visualizations of sets automatically, thus improving human--computer interaction.

Web applications divide their state between the client and the server. The frequent and highly dynamic client-server communication that is characteristic of modern web applications leaves them vulnerable to side-channel leaks, even over encrypted connections. We describe a black-box tool for detecting and quantifying the severity of side-channel vulnerabilities by analyzing network traffic over repeated crawls of a web application. By viewing the adversary as a multi-dimensional classifier, we develop a methodology to more thoroughly measure the distinguishably of network traffic for a variety of classification metrics. We evaluate our detection system on several deployed web applications, accounting for proposed client and server-side defenses. Our results illustrate the limitations of entropy measurements used in previous work and show how our new metric based on the Fisher criterion can be used to more robustly reveal side-channels in web applications.

The process of designing and modelling an ontology can be difficult, especially if the user finds the syntax to be relatively inaccessible. Providing users with graphical syntax with which they can model and visualise their ontology has the potential to be helpful. Previously, we informally introduced concept diagrams for ontology visualisation and modelling. We present a case study comprising: (a) a set of axioms for an ontology, and (b) a set of theorems that follow from the axioms, together with their proofs. The proofs have been constructed so that they are, in our opinion, of an intuitive style. From these proofs, we derive a set of sound inference rules that can be used to formally reason about ontologies following the same intuitive style. This approach to designing inference rules differs from previous efforts where the primary focus has been on obtaining a set of sound and complete inference rules, rather than on intuitiveness.

There has been significant research effort focussed on the study of regular languages, since they play a vital role in our understanding of computation. This existing research draws a large number of connections with other areas, such as algebra and symbolic logic. Recently, research has begun into how diagrammatic logics can define regular languages, providing another mechanism through which we can understand regular languages. However, the formalised diagrammatic logics are first-order, so they cannot define non-starfree regular languages. The primary contributions of this paper are: (a) to develop and formalise a second-order diagrammatic logic, extending spider diagrams of order, and (b) to establish a class of regular languages that this logic can define. This lays the essential foundations for providing an exact classification of the regular languages that are definable using this new second-order logic.