The right to humanitarian assistance in natural and human-made disasters: progress and challenges for an emerging international legal framework

Natural and human-made disasters cause on average 120,000 deaths and over US$140 billion in damage to property and infrastructure every year, with national, regional and international actors consistently responding to the humanitarian imperative to alleviate suffering wherever it may be found. Despite various attempts to codify international disaster laws since the 1920s, a right to humanitarian assistance remains contested, reflecting concerns regarding the relative importance of state sovereignty vis-à-vis individual rights under international law. However, the evolving acquis humanitaire of binding and non-binding normative standards for responses to humanitarian crises highlights the increasing focus on rights and responsibilities applicable in disasters; although the International Law Commission has also noted the difficulty of identifying lex lata and lex ferenda regarding the protection of persons in the event of disasters due to the “amorphous state of the law relating to international disaster response.” Therefore, using the conceptual framework of transnational legal process, this thesis analyses the evolving normative frameworks and standards for rights-holders and duty-bearers in disasters. Determining the process whereby rights are created and evolve, and their potential internalisation into domestic law and policy, provides a powerful analytical framework for examining the progress and challenges of developing accountable responses to major disasters.

A pro-active and dynamic prediction assistance using BaranC framework

Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.