Can aid switch gears to respond to sudden forced displacement? The case of Haut-Uele, DRC

How does the aid system respond when insecurity and suddenforced displacement occur in what has long been considered a stable, development context? Can longer-term aid interventions adapt when challenged to “shift gears” to address acute needs resulting from forced displacement? Based on observations from Médecins Sans Frontières projects in Haut-Uélé in northeastern DRC in 2008–2009, this article examines assistance to displaced populations and the residents hosting them in LRA-affected areas—above all, the stakes and dilemmas involved in responding to such a sudden-onset emergency in what international donors and the national government considered an area in development. Initially, a much-needed response to violence and displacement failed to materialize, with little permanent humanitarian presence on the ground, while development approaches failed to adapt and meet emergency needs. Short-term contingency support was provided through development NGOs, but with limited scope and maintaining cost-recovery schemes for health toward an impoverished population facing an increasingly precarious situation. A long-term development approach was simply unable to respond to the sudden population increase and a fragile health situation.Comment réagit le système d’aide lorsque l’insécurité et le déplacement forcé soudain se manifestent dans un contexte qui a longtemps été considéré comme stable et propice au développement? L’intervention humanitaire à long terme peut-elle s’adapter quand il lui faut « changer de vitesse » pour répondre aux besoins aigus résultant des déplacements forcés? S’appuyant sur l’étude de projets de Médecins Sans Frontières dans le Haut-Uélé, dans le nord-est de la RDC en 2008–2009, cet article examine l’aide aux populations déplacées et aux résidents qui les accueillent en zones touchées par l’Armée de résistance du Seigneur (LRA), plus particulièrement les enjeux et dilemmes liés à la réaction envers une situation d’urgence apparue soudai-nement dans une zone que les donateurs internationaux et le gouvernement national considéraient comme une zone de développement. Au départ, une réponse fort nécessaire à la violence et au déplacement ne s’est pas concrétisée, avec une faible présence humanitaire permanente sur le terrain, alors que les approches de développement n’ont su s’adapter et répondre aux besoins d’urgence. Des ONG de développement ont apporté un soutien d’urgence à court terme mais de portée limitée et le maintien d’un système de recouvrement des coûts pour les régimes de santé à l’intention d’une population appauvrie confronté à une situation de plus en plus précaire. Une approche de développement à long terme était tout simplement incapable de répondre à l’augmentation soudaine de la population et une situation de santé précaire

Encoding Markov Logic Networks in Possibilistic Logic

Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes.Comment: Extended version of a paper appearing in UAI 201

Induction of Interpretable Possibilistic Logic Theories from Relational Data

The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which make them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.Comment: Longer version of a paper appearing in IJCAI 201

Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness

In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, which means that the success of applications that use such path features crucially relies on their ability to select high-quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this paper we instead propose to learn to predict path quality from crowdsourced human assessments. Since we are interested in a generic task-independent notion of quality, we simply ask human participants to rank paths according to their subjective assessment of the paths' naturalness, without attempting to define naturalness or steering the participants towards particular indicators of quality. We show that a neural network model trained on these assessments is able to predict human judgments on unseen paths with near optimal performance. Most notably, we find that the resulting path selection method is substantially better than the current heuristic approaches at identifying meaningful paths.Comment: In Proceedings of the Web Conference (WWW) 201