Space-contained conflict revision, for geographic information

Using qualitative reasoning with geographic information, contrarily, for instance, with robotics, looks not only fastidious (i.e.: encoding knowledge Propositional Logics PL), but appears to be computational complex, and not tractable at all, most of the time. However, knowledge fusion or revision, is a common operation performed when users merge several different data sets in a unique decision making process, without much support. Introducing logics would be a great improvement, and we propose in this paper, means for deciding -a priori- if one application can benefit from a complete revision, under only the assumption of a conjecture that we name the "containment conjecture", which limits the size of the minimal conflicts to revise. We demonstrate that this conjecture brings us the interesting computational property of performing a not-provable but global, revision, made of many local revisions, at a tractable size. We illustrate this approach on an application.Comment: 14 page

Automatic goal allocation for a planetary rover with DSmT

In this chapter, we propose an approach for assigning aninterest level to the goals of a planetary rover. Assigning an interest level to goals, allows the rover to autonomously transform and reallocate the goals. The interest level is defined by data-fusing payload and navigation information. The fusion yields an 'interest map',that quantifies the level of interest of each area around the rover. In this way the planner can choose the most interesting scientific objectives to be analysed, with limited human intervention, and reallocates its goals autonomously. The Dezert-Smarandache Theory of Plausible and Paradoxical Reasoning was used for information fusion: this theory allows dealing with vague and conflicting data. In particular, it allows us to directly model the behaviour of the scientists that have to evaluate the relevance of a particular set of goals. This chaptershows an application of the proposed approach to the generation of a reliable interest map

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression

Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)

The Hidden Love of God and the Imaging Defense

J. L. Schellenberg has recently argued that there is a logical incompatibility between God’s being perfectly loving and there being non-resistant nonbelievers in the proposition that God exists. In this paper I highlight the parallel between this claim and the claim made by the logical problem of evil. Following Plantinga’s strategy in undermining the logical problem of evil, I argue that all that is needed to undermine the alleged incompatibility of divine love with non-resistant non-belief is a counterexample showing how the two might possibly co-exist. But whereas most attempts to show this have been grounded anthropologically, by drawing on forms of love-relationship that God and humans have in common, I offer a defense of the compatibility of perfect divine love with human non-resistant non-belief in God’s existence grounded theologically, in the unique sort of love relationship that God wants with us, which is the relationship of “imaging.

The process of civilization (and its discontents): violence, narrative and history

Historical violence studies are being increasingly influenced by theoretical approaches which focus on the development of ‘cultures of violence’. However, this growing interest in the interconnections between violence and culture faces a number of significant challenges posed by the influence of disciplines other than history as well as by internal difficulties in (and disagreements over) identifying the precise role of discourse in shaping (and changing) cultures of violence. In dealing with these issues, historians are becoming increasingly interested in Norbert Elias’s theory of the ‘civilising process’. This perspective has proven to be very fruitful; nonetheless, there are problematic issues raised by Elias’s approach. In particular, the relationship between 'culture' (and thus 'discourse') and the social forces which, according to Elias, have driven a historical decline in violent behaviour – interdependence, class differentiation and the state monopolisation of legitimate physical force – remains unsettled. In this essay, I contribute to the theoretical discussion of discourses of violence from a historical perspective marked by a critical engagement with the notion of a ‘civilising process’ and incorporating conceptual tools from the fields of discourse analysis, social geography and anthropology. My conclusions, though focused on the past, are nevertheless relevant to current issues in violence and the ways that it is understood

Between social control and conflict: an analytical framework for social movements

The main goal of this article is to attempt to determine the analytical framework of social movements that would constitute an essential element of this form of collective activity. In order to identify this element (or elements) I will review the four main approaches to the study of social movements, which allows me to settle the issue in sociological conflict tradition. From the point of view of the outlined objective, Alain Touraine’s approach will be a key perspective