The learning network on sustainability: An e-mechanism for the development and diffusion of teaching materials and tools on design for sustainability in an open-source and copy left ethos

This is the post-print version of the Article. The official published version can be obtained from the link below - Copyright @ 2011 InderscienceThis paper presents the intermediate results of the Learning Network on Sustainability (LeNS) project, Asian-European multi-polar network for curricula development on Design for Sustainability. LeNS is a mechanism to develop and diffuse system design for sustainability in design schools with a transcultural perspective. The main output of the project is the Open Learning E-Package (OLEP), an open web-platform that allows a decentralised and collaborative production and fruition of knowledge. Apart from the contents, the same LeNS web-platform is realised in an open-source and copy left ethos, allowing its download and reconfiguration in relation to specific needs, interests and geographical representation

Local and Global Trust Based on the Concept of Promises

We use the notion of a promise to define local trust between agents possessing autonomous decision-making. An agent is trustworthy if it is expected that it will keep a promise. This definition satisfies most commonplace meanings of trust. Reputation is then an estimation of this expectation value that is passed on from agent to agent. Our definition distinguishes types of trust, for different behaviours, and decouples the concept of agent reliability from the behaviour on which the judgement is based. We show, however, that trust is fundamentally heuristic, as it provides insufficient information for agents to make a rational judgement. A global trustworthiness, or community trust can be defined by a proportional, self-consistent voting process, as a weighted eigenvector-centrality function of the promise theoretical graph

A foundation for machine learning in design

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

e-Report Generator Supporting Communications and Fieldwork: A Practical Case of Electrical Network Expansion Projects

In this piece of work we present a simple way to incorporate Geographical Information System tools that have been developed using open source software in order to help the different processes in the expansion of the electrical network. This is accomplished by developing a novel fieldwork tool that provides the user with automatically generated enriched e-reports that include information about every one of the involved private real estates in a specific project. These reports are an eco-friendly alternative to paper format, and can be accessed by clients using any kind of personal device with a minimal set of technical requirements

A Study of the Relationship Between Antivirus Regressions and Label Changes

AntiVirus (AV) products use multiple components to detect malware. A component which is found in virtually all AVs is the signature-based detection engine: this component assigns a particular signature label to a malware that the AV detects. In previous analysis [1-3], we observed cases of regressions in several different AVs: i.e. cases where on a particular date a given AV detects a given malware but on a later date the same AV fails to detect the same malware. We studied this aspect further by analyzing the only externally observable behaviors from these AVs, namely whether AV engines detect a malware and what labels they assign to the detected malware. In this paper we present the results of the analysis about the relationship between the changing of the labels with which AV vendors recognize malware and the AV regressions