The future of urban models in the Big Data and AI era: a bibliometric analysis (2000-2019)

This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. To identify these effects, we use two complementary materials: bibliometric data and interviews. We consider two areas in urban research: one, covering the academic research dealing with transportation systems and the other, with water systems. First, we measure the evolution of AI and Big Data keywords in these two areas. Second, we measure the evolution of the share of publications published in computer science journals about urban traffic and water quality. To guide these bibliometric analyses, we rely on the content of interviews conducted with academics and higher education officials in Paris and Edinburgh at the beginning of 2018

Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation

The availability of datasets for analytical solution development is a common bottleneck in data-driven predictive maintenance. Therefore, novel solutions are mostly based on synthetic benchmarking examples, such as NASA’s C-MAPSS datasets, where researchers from various disciplines like artificial intelligence and statistics apply and test their methodical approaches. The majority of studies, however, only evaluate the overall solution against a final prediction score, where we argue that a more fine-grained consideration is required distinguishing between detailed method components to measure their particular impact along the prognostic development process. To address this issue, we first conduct a literature review resulting in more than one hundred studies using the C-MAPSS datasets. Subsequently, we apply a taxonomy approach to receive dimensions and characteristics that decompose complex analytical solutions into more manageable components. The result is a first draft of a systematic benchmarking framework as a more comparable basis for future development and evaluation purposes

Reasoning with conditionals

This paper reviews the psychological investigation of reasoning with conditionals, putting an emphasis on recent work. In the first part, a few methodological remarks are presented. In the second part, the main theories of deductive reasoning (mental rules, mental models, and the probabilistic approach) are considered in turn; their content is summarised and the semantics they assume for if and the way they explain formal conditional reasoning are discussed, in particular in the light of experimental work on the probability of conditionals. The last part presents the recent shift of interest towards the study of conditional reasoning in context, that is, with large knowledge bases and uncertain premises

“Was Peirce a Genuine Anti-Psychologist in Logic?”

The aim of the paper is to try and make one’s ideas clearer about such concepts as “logic,” “psychology,” “mind,” “normativity,” rationality,” as they were conceived by Peirce, in order to elucidate his genuine position as far as the relationship between logic and pychology is concerned, whether he was or was not a straightforward “anti psychologist” in logic, and from such analyses, to make some suggestions about the contemporary relevance of Peirce’s original views on such isues

Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling

We propose a fully automatic method for learning gestures on big touch devices in a potentially multi-user context. The goal is to learn general models capable of adapting to different gestures, user styles and hardware variations (e.g. device sizes, sampling frequencies and regularities). Based on deep neural networks, our method features a novel dynamic sampling and temporal normalization component, transforming variable length gestures into fixed length representations while preserving finger/surface contact transitions, that is, the topology of the signal. This sequential representation is then processed with a convolutional model capable, unlike recurrent networks, of learning hierarchical representations with different levels of abstraction. To demonstrate the interest of the proposed method, we introduce a new touch gestures dataset with 6591 gestures performed by 27 people, which is, up to our knowledge, the first of its kind: a publicly available multi-touch gesture dataset for interaction. We also tested our method on a standard dataset of symbolic touch gesture recognition, the MMG dataset, outperforming the state of the art and reporting close to perfect performance.Comment: 9 pages, 4 figures, accepted at the 13th IEEE Conference on Automatic Face and Gesture Recognition (FG2018). Dataset available at http://itekube7.itekube.co

Extreme Learning Machine Based Prognostics of Battery Life

This paper presents a prognostic scheme for estimating the remaining useful life of Lithium-ion batteries. The proposed scheme utilizes a prediction module that aims to obtain precise predictions for both short and long prediction horizons. The prediction module makes use of extreme learning machines for one-step and multi-step ahead predictions, using various prediction strategies, including iterative, direct and DirRec, which use the constant-current experimental capacity data for the estimation of the remaining useful life. The data-driven prognostic approach is highly dependent on the availability of high quantity of quality observations. Insufficient amount of available data can result in unsatisfactory prognostics. In this paper, the prognostics scheme is utilized to estimate the remaining useful life of a battery, with insufficient direct data available, but taking advantage of observations available from a fleet of similar batteries with similar working conditions. Experimental results show that the proposed prognostic scheme provides a fast and efficient estimation of the remaining useful life of the batteries and achieves superior results when compared with various state-of-the-art prediction techniques

Organization as A Multi-dimensional Network of Actants Mediated by An Organized and Organizing Network of Cultural Rules

Programme du colloque : www.groupelog.umontreal.ca/anglais/LOGConferenceProgram.pdfThis paper aims to provide a complementary perspective to the Montreal School's conceptual framework on the organizing properties of communication. The network metaphor is used to address two issues of this theory: how to link entities with variable ontologies together and how to explain that entities are objects (inherent ontology) and mediators (relational ontology) at the same time? Networks are considered both as material structures (directed graphs seen as topological objects) and as abstracted ones (matrixes). Complex networks display structural properties when they emerge as structures. However, the emergence of structure is only considered as a realization of one of the virtual states and possible patterns of a meta-network made up of at least three networks on several dimensions: a material network of actants, a cognitive network and a cultural network of rules. The latter network is linked to human entities only. Cultural rules are seen as habitus, i.e. kinds of force fields that guide but do not determine action. Human agency is constrained by cultural rules so that human beings are able to reproduce social systems. Agency is also seen as continuous modification and displacement. It modifies the structure of the network of actants and that of the network of rules. The network of actants makes the network of rules evolve through feedback loops. The network of rules generates calculations in the cognitive network. Sensemaking results from the continuous process of reproduction-modification of the cognitive structure.Pas de résumé en françai