Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly being produced in many fields of research. Although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is in the hundreds of thousands or millions. When we face huge problems, scalability becomes an issue, and most algorithms are not applicable.Thus, paradoxically, instance selection algorithms are for the most part impracticable for the same problems that would benefit most from their use. This paper presents a way of avoiding this difficulty using several rounds of instance selection on subsets of the original dataset. These rounds are combined using a voting scheme to allow good performance in terms of testing error and storage reduction, while the execution time of the process is significantly reduced. The method is particularly efficient when we use instance selection algorithms that are high in computational cost. The proposed approach shares the philosophy underlying the construction of ensembles of classifiers. In an ensemble, several weak learners are combined to form a strong classifier; in our method several weak (in the sense that they are applied to subsets of the data) instance selection algorithms are combined to produce a strong and fast instance selection method.An extensive comparison of 30 medium and large datasets from the UCI Machine Learning Repository using 3 different classifiers shows the usefulness of our method. Additionally, the method is applied to 5 huge datasets (from three hundred thousand to more than a million instances) with good results and fast execution time.     

UAVs Integration in the SWIM Based Architecture for ATM

The System Wide Information Management (SWIM) approach has been conceived to overcome the capacity and flexibility limitations of the current ATM systems. On the other hand the commercial applications of Unmanned Aerial Vehicles (UAVs) require the integration of these vehicles in the ATM. From this perspective, the unavoidable modernization of the ATM is seen as an opportunity to integrate the UAVs with the rest of the air traffic. This paper is devoted to study the feasibility and impact of the aggregation of UAVs on the future ATM supported by a SWIM inspired architecture. Departing from the existing technical documents that describe the fundamentals of SWIM we have explored the compatibility with a potential UAVs integration and also explored how the UAVs could help to improve the future ATM system. We will use the weather application as an example in both cases.     

An integrated domain analysis approach for teleoperated systems

Teleoperated systems for ship hull maintenance (TOS) are robotic systems for ship maintenance tasks, such as cleaning or painting a ship’s hull. The product line paradigm has recently been applied to TOS, and a TOS reference architecture has thus been designed. However, TOS requirements specifications have not been developed in any rigorous way with reuse in mind. We therefore believe that an opportunity exists to increase the abstraction level at which stakeholders can reason about this product line. This paper reports an experience in which this TOS domain was analyzed, including the lessons learned in the construction and use of the TOS domain model. The experience is based on the application of extensions of well-known domain analysis techniques, together with the use of quality attribute templates traced to a feature model to deal with non-functional issues. A qualitative research method (action research) was used to carry out the experience.     

Boosting k-nearest neighbor classifier by means of input space projection

The k-nearest neighbors classifier is one of the most widely used methods of classification due to several interesting features, such as good generalization and easy implementation. Although simple, it is usually able to match, and even beat, more sophisticated and complex methods. However, no successful method has been reported so far to apply boosting to k-NN. As boosting methods have proved very effective in improving the generalization capabilities of many classification algorithms, proposing an appropriate application of boosting to k-nearest neighbors is of great interest.Ensemble methods rely on the instability of the classifiers to improve their performance, as k-NN is fairly stable with respect to resampling, these methods fail in their attempt to improve the performance of k-NN classifier. On the other hand, k-NN is very sensitive to input selection. In this way, ensembles based on subspace methods are able to improve the performance of single k-NN classifiers. In this paper we make use of the sensitivity of k-NN to input space for developing two methods for boosting k-NN. The two approaches modify the view of the data that each classifier receives so that the accurate classification of difficult instances is favored.The two approaches are compared with the classifier alone and bagging and random subspace methods with a marked and significant improvement of the generalization error. The comparison is performed using a large test set of 45 problems from the UCI Machine Learning Repository. A further study on noise tolerance shows that the proposed methods are less affected by class label noise than the standard methods.     

Electrically insulating polymeric nanocomposites with enhanced thermal conductivity by visible‐light curing of epoxy–boron nitride nanotube formulations

Epoxy–boron nitride nanotube (BNNT) composites were prepared using visible light through a radical‐induced cationic polymerization method activated by camphorquinone. The fully cured films showed an enhancement of glass transition temperature in the presence of the filler. Electrical characterization showed a slight dielectric constant decrease with BNNT content. Finally, thermal conductivity measured using nano‐flash analysis showed a linear increase in the thermal conductivity of the materials with increasing BNNT content in the photocurable formulations. © 2017 Society of Chemical Industry     

Mechanical properties of freely suspended semiconducting graphene-like layers based on MoS2

We fabricate freely suspended nanosheets of molybdenum disulphide (MoS₂) which are characterized by quantitative optical microscopy and high-resolution friction force microscopy. We study the elastic deformation of freely suspended nanosheets of MoS₂ using an atomic force microscope. The Young''s modulus and the initial pre-tension of the nanosheets are determined by performing a nanoscopic version of a bending test experiment. MoS₂ sheets show high elasticity and an extremely high Young''s modulus (0.30 TPa, 50% larger than steel). These results make them a potential alternative to graphene in applications requiring flexible semiconductor materials.PACS, 73.61.Le, other inorganic semiconductors, 68.65.Ac, multilayers, 62.20.de, elastic moduli, 81.40.Jj, elasticity and anelasticity, stress-strain relations.     

Increasing espresso coffee brew antioxidant capacity using phenolic extract recovered from hazelnut skin waste

The simultaneous consumption of different classes of phytochemical antioxidants in the diet can result in more beneficial effects than when consumed alone. In the present study, the in vitro and in vivo antioxidative effects of espresso coffee brew (EC) (rich in chlorogenic acids) with added crude hazelnut skin phenolic extract (HSPE) from hazelnut skin waste (rich in flavonoids) were studied. Both post-brewing and pre-brewing phenolic-enriched espresso coffees (PE-ECs) were analysed for total phenols and screened for their in vitro antiradical ability. Moreover, the in vivo biological effect on the antioxidant potential of plasma in rats was evaluated. The PE-ECs showed increased both in vitro and in vivo antiradical activity proportional to the added HSPE. The in vivo experiments suggested that HSPE was much more antioxidant active than the phenolic fraction naturally contained in EC. Moreover, evidence of possible synergic effects of EC and HSPE phenolics was observed in vivo.     

Effect of different treatments on the microstructure and functional and pasting properties of pigeon pea (Cajanus cajan L.), dolichos bean (Dolichos lablab L.) and jack bean (Canavalia ensiformis) flours from the north‐east Argentina

The effect of germination (G; 5 days), soaking‐cooking (SC; 6 h–20 min, 6 h–40 min, 6 h–60 min) and microwave (M; 50%, 70%, 100%) treatments on pigeon pea (PP), dolichos bean (DB) and jack bean (JB) seeds was studied. Microstructure of seeds and functional (protein solubility, water‐holding capacity, oil‐holding capacity, emulsion stability) and pasting properties of flours were determined. Germination and microwave treatments modified the protein matrix of cotyledon cells preserving the shape of the starch granule, whereas the SC treatment (6 h–60 min) affected both. The soaking‐cooking is the most influential treatment on the functional properties of PP, DB and JB flours, as increased water absorption capacity (73–96%), decreased protein solubility (>80%) and the tendency to retrogradation of amylose (69–85%) also improved emulsion stability.     

Identification of unbalance forces by metaheuristic search algorithms

This paper discusses the identification of parameters in rotary systems, namely, the unbalance magnitude, phase and position in the rotor system. These parameters can be identified using the measured orbits in the hydrodynamic bearings. The oil film forces are evaluated in the different positions of the orbit of the journal and are applied to the model of the shaft. The model, integrated in time domain, allows with an assumed unbalance, to simulate the orbits. The objective function is basically the difference between measured and simulated orbits, and its minimum corresponds to the identified unbalance amount, phase and position along the shaft. With respect to traditional model based identification procedures, this approach using oil film forces instead of oil film linearized stiffness and damping coefficients, and unfiltered orbits instead of 1X vibration components is suitable to deal with non-linear behaviour of the system.