Performance monitoring and analysis of a yield-control support system in squeezing rock

ISBN 978-0-415-58654-

A tree-based kernel for graphs with continuous attributes

The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to non-discrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this paper, we propose a graph kernel for complex and continuous nodes' attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel which reduces its complexity significantly. Experimental results obtained on six real-world datasets show that the kernel is the best performing one on most of them. Moreover, in most cases the approximated version reaches comparable performances to current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.Comment: This work has been submitted to the IEEE Transactions on Neural Networks and Learning Systems for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs

Kernel methods are considered an effective technique for on-line learning. Many approaches have been developed for compactly representing the dual solution of a kernel method when the problem imposes memory constraints. However, in literature no work is specifically tailored to streams of graphs. Motivated by the fact that the size of the feature space representation of many state-of-the-art graph kernels is relatively small and thus it is explicitly computable, we study whether executing kernel algorithms in the feature space can be more effective than the classical dual approach. We study three different algorithms and various strategies for managing the budget. Efficiency and efficacy of the proposed approaches are experimentally assessed on relatively large graph streams exhibiting concept drift. It turns out that, when strict memory budget constraints have to be enforced, working in feature space, given the current state of the art on graph kernels, is more than a viable alternative to dual approaches, both in terms of speed and classification performance.Comment: Author's version of the manuscript, to appear in Neurocomputing (ELSEVIER

VULNERABILITY ASSESSMENT FOR PRELIMINARY FLOOD RISK MAPPING AND MANAGEMENT IN COASTAL AREAS

Planning and management of coastal environment, both terrestrial and marine, is affected by several actions in environment resource conservation and improvement paying specific attention to risk forecasting and preventing. In such context the EU flood Directive 2007/60/EC, which requires Member States the assessment and management of flood risk, and the EU water framework Directive (2000/60/EC) are the key factors in the integrated river basin management to assure an efficient and rational use of resources. Afterwards, coastal risk assessment and mapping is a propaedeutic phase to plan and manage coastal areas. In this work risk analysis refers to the results obtained by the combined application of coastal flooding and erosion risks in the activities carried out to prepare Regional Coast Management Plan for the Ionian coast of Basilicata Region located in the south of Italy. In order to define the driving forces acting on the shore, high resolution lidar data, bathymetric information and wave climate statistics acquired by meteorological analyses on wind field data referred to different acquisition times are used. The systemic vulnerability estimation is achieved by composing both hazard factors combined in the Criticality Coastal Index depending on of the assessment of Coastal Flood Index and Coastal Erosion Index based on morphologic and socio-economic variables

On the structure of continua with finite length and Golab's semicontinuity theorem

The main results in this note concern the characterization of the length of continua1 (Theorems 1.5) and the parametrization of continua with finite length (Theorem 3.4). Using these results we give two independent and relatively elementary proofs of Golab’s semicontinuity theorem

Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection

In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. Our system relies on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic in the sense that they promote a particular political cause or viewpoint. We trained a logistic regression model with features ranging from simple bag-of-words to vocabulary richness and text readability features. Our system achieved 72.9% accuracy on the test data that is annotated manually and 60.8% on the test data that is annotated with distant supervision. Additional experiments showed that significant performance improvements can be achieved with better feature pre-processing.Comment: Hyperpartisanship, propaganda, news media, fake news, SemEval-201

Investigation of creep phenomenon on composite material for bolt connections

One of the main target in the automotive design is the weight reduction. This reduction leads to the reduction of the gas emissions. The designers tend to use innovative materials for the automotive field such as plastics and composites. To use the right material for the right application, multi material solutions are increasingly adopted. To join dissimilar materials, solutions like adhesive, bolt and nuts, riveting are necessary. It is necessary to know the behaviour of the materials to be joined, under different loading conditions to ensure the joint. In this work, a bolt connection between composite and aluminium plates has been considered. The behaviour of a carbon fibre reinforced material under compression load, taking into account creep is studied. A specific experimental equipment has been design and built. A series of experimental compressive tests, in the laminate thickness direction, have been done on carbon fibre reinforced material specimens. Different set-up in terms of temperature, compression load and surface roughness have been investigated. The obtained results are presented and discussed. A mathematical model will be proposed for interpolation of the obtained results. Finally, a possible strategy for reducing the tight loss in the initial phase of the joint life is propose