Active learning based laboratory towards engineering education 4.0

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version

Cross-Modal Data Programming Enables Rapid Medical Machine Learning

Labeling training datasets has become a key barrier to building medical machine learning models. One strategy is to generate training labels programmatically, for example by applying natural language processing pipelines to text reports associated with imaging studies. We propose cross-modal data programming, which generalizes this intuitive strategy in a theoretically-grounded way that enables simpler, clinician-driven input, reduces required labeling time, and improves with additional unlabeled data. In this approach, clinicians generate training labels for models defined over a target modality (e.g. images or time series) by writing rules over an auxiliary modality (e.g. text reports). The resulting technical challenge consists of estimating the accuracies and correlations of these rules; we extend a recent unsupervised generative modeling technique to handle this cross-modal setting in a provably consistent way. Across four applications in radiography, computed tomography, and electroencephalography, and using only several hours of clinician time, our approach matches or exceeds the efficacy of physician-months of hand-labeling with statistical significance, demonstrating a fundamentally faster and more flexible way of building machine learning models in medicine

Context Aware Adaptable Applications - A global approach

Actual applications (mostly component based) requirements cannot be expressed without a ubiquitous and mobile part for end-users as well as for M2M applications (Machine to Machine). Such an evolution implies context management in order to evaluate the consequences of the mobility and corresponding mechanisms to adapt or to be adapted to the new environment. Applications are then qualified as context aware applications. This first part of this paper presents an overview of context and its management by application adaptation. This part starts by a definition and proposes a model for the context. It also presents various techniques to adapt applications to the context: from self-adaptation to supervised approached. The second part is an overview of architectures for adaptable applications. It focuses on platforms based solutions and shows information flows between application, platform and context. Finally it makes a synthesis proposition with a platform for adaptable context-aware applications called Kalimucho. Then we present implementations tools for software components and a dataflow models in order to implement the Kalimucho platform