An integrated approach to courseware

Software engineering is becoming increasingly important as an engineering discipline, and its teaching in universities and other higher education institutions should be of high quality. In this paper we describe a tool (BOSS — the Boss Online Submission System) which aids the education of software engineers. BOSS allows students to submit programming assignments online, and to run black-box tests on their programs prior to submission. Instructors can use BOSS to assist in marking such assignments by allowing submitted programs to be tested against multiple data sets. We describe how BOSS helps in the teaching of specific conceptual aspects of software engineering, and how it addresses some of the practical issues involved in teaching large student numbers in a pedagogically neutral manner

Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

Factors controlling lava dome morphology

Research suggests that variations in lava dome morphology on different planets will depend much more critically on local gravity and the style of eruption than on the magma composition, ambient temperature, or the relative roles of convective and radiative cooling. Eruption style in turn reflects differences in tectonic conditions and the ability of magma to exsolve volatiles. Observed crude correlations between silica content and calculated yield strengths for terrestrial lava flows and domes probably are do to differences in extrusion rate and volatile solubility, rather than intrinsic rheological properties. Thus, even after taking the known effect of gravity into account, observed differences in gross dome morphology on different planets cannot by themselves be directly related to composition. Additional information such as the distribution of surface textures and structures, or spectroscopic data will be needed to conclusively establish dome compositions

Image scoring in ad-hoc networks : an investigation on realistic settings

Encouraging cooperation in distributed Multi-Agent Systems (MAS) remains an open problem. Emergent application domains such as Mobile Ad-hoc Networks (MANETs) are characterised by constraints including sparse connectivity and a lack of direct interaction history. Image scoring, a simple model of reputation proposed by Nowak and Sigmund, exhibits low space and time complexity and promotes cooperation through indirect reciprocity, in which an agent can expect cooperation in the future without repeat interactions with the same partners. The low overheads of image scoring make it a promising technique for ad-hoc networking domains. However, the original investigation of Nowak and Sigmund is limited in that it (i) used a simple idealised setting, (ii) did not consider the effects of incomplete information on the mechanism’s efficacy, and (iii) did not consider the impact of the network topology connecting agents. We address these limitations by investigating more realistic values for the number of interactions agents engage in, and show that incomplete information can cause significant errors in decision making. As the proportion of incorrect decisions rises, the efficacy of image scoring falls and selfishness becomes more dominant. We evaluate image scoring on three different connection topologies: (i) completely connected, which closely approximates Nowak and Sigmund’s original setup, (ii) random, with each pair of nodes connected with a constant probability, and (iii) scale-free, which is known to model a number of real world environments including MANETs

Manipulating concept spread using concept relationships

The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from a set of initially activated individuals. The study of such influence cascades, in particular the identification of influential individuals, has a wide range of applications including epidemic control, viral marketing and the study of social norms. In real-world environments there may be many concepts spreading and interacting. These interactions can affect the spread of a given concept, either boosting it and allowing it to spread further, or inhibiting it and limiting its capability to spread. Previous work does not consider how the interactions between concepts affect concept spread. Taking concept interactions into consideration allows for indirect concept manipulation, meaning that we can affect concepts we are not able to directly control. In this paper, we consider the problem of indirect concept manipulation, and propose heuristics for indirectly boosting or inhibiting concept spread in environments where concepts interact. We define a framework that allows for the interactions between any number of concepts to be represented, and present a heuristic that aims to identify important influence paths for a given target concept in order to manipulate its spread. We compare the performance of this heuristic, called maximum probable gain, against established heuristics for manipulating influence spread

Limiting concept spread in environments with interacting concepts

The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from an initial set of individuals. In real-world environments there may be many concepts spreading and interacting. Previous work does not consider utilising concept interactions to limit the spread of a concept. In this paper we present a method for limiting concept spread, in environments where concepts interact and do not block others from spreading. We define a model that allows for the interactions between any number of concepts to be represented and, using this model, develop a solution to the influence limitation problem, which aims to minimise the spread of a target concept through the use of a secondary inhibiting concept. We present a heuristic, called maximum probable gain, and compare its performance to established heuristics for manipulating influence spread in both simulated smallworld networks and real-world networks

Indirect influence manipulation with partially observable networks

The propagation of concepts through a population of agents can be modelled as a cascade of influence spread from an initial set of individuals. In real-world environments there may be many concepts spreading and interacting, and we may not be able to directly control the target concept we wish to manipulate, requiring indirect manipulation through a secondary controllable concept. Previous work on influence spread typically assumes that we have full knowledge of a network, which may not be the case. In this paper, we investigate indirect influence manipulation when we can only observe a sample of the full network. We propose a heuristic, known as Target Degree, for selecting seed nodes for a secondary controllable concept that uses the limited information available in a partially observable environment to indirectly manipulate the target concept. Target degree is shown to be effective in synthetic small-world networks and in real-world networks when the controllable concept is introduced after the target concept

Addressing class imbalance in trust and stereotype assessment

Trust, reputation and stereotypes enable agents to identify reliable interaction partners based on past interactions. However, such methods can cause agents to choose the same known partners instead of unknown, but potentially better, alternatives, giving rise to a class imbalance in their interaction histories. In this paper, we present a Class Imbalance Modification (CIM) method, to improve agents' initial assessments of others by becoming aware of the bias towards known agents. CIM enables an agent to determine whether data-driven trust, reputation and stereotypes are appropriate to assess a target agent, depending on how representative the agent's past interaction data is of the target. We also present a technique, Direct Comparative Stereotypes (DCS), which does not use past interaction data to make a stereotypical assessment, and so can be used if CIM concludes the data is inappropriate. Finally, CIM determines whether data-driven models have been rendered inappropriate by dynamic agent behaviour, where old interactions may no longer reflect current behaviour. Our results show that CIM significantly reduces error in a priori estimates, which improves partner selection and increases average utility

Using tags to bootstrap stereotypes and trust

Agents joining multi-agent systems (MAS) face two significant problems: they do not know who to trust and others do not know if they are trustworthy. Our contribution extends trust and stereotype approaches to use a comparison of agents’ observable features, called tags, as an initial indication of expected behaviour. The results show an improvement in agents’ rewards in the early stages of their lifetimes, prior to having sufficient information to use trust or stereotype methods

Addressing concept drift in reputation assessment

In this paper, we address the limitations of existing methods to select representative data for trust assessment when agent behaviours can change at varying speeds and times across a system. We propose a method that uses concept drift detection to identify and exclude unrepresentative past experiences, and show that our approach is more robust to dynamic agent behaviours