Generating Component-based Supervised Learning Programs From Crowdsourced Examples

We present CrowdLearn, a new system that processes an existing corpus of crowdsourced machine learning programs to learn how to generate effective pipelines for solving supervised machine learning problems. CrowdLearn uses a probabilistic model of program likelihood, conditioned on the current sequence of pipeline components and on the characteristics of the input data to the next component in the pipeline, to predict candidate pipelines. Our results highlight the effectiveness of this technique in leveraging existing crowdsourced programs to generate pipelines that work well on a range of supervised learning problems

Bayesian Networks Methods for Traffic Flow Prediction

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Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example

Data management and analysis tasks are often carried out using spreadsheet software. A popular feature in most spreadsheet platforms is the ability to define data-dependent formatting rules. These rules can express actions such as "color red all entries in a column that are negative" or "bold all rows not containing error or failure." Unfortunately, users who want to exercise this functionality need to manually write these conditional formatting (CF) rules. We introduce CORNET, a system that automatically learns such conditional formatting rules from user examples. CORNET takes inspiration from inductive program synthesis and combines symbolic rule enumeration, based on semi-supervised clustering and iterative decision tree learning, with a neural ranker to produce accurate conditional formatting rules. In this demonstration, we show CORNET in action as a simple add-in to Microsoft Excel. After the user provides one or two formatted cells as examples, CORNET generates formatting rule suggestions for the user to apply to the spreadsheet.Comment: 4 Pages, VLDB 2023 Demonstration Trac

Influence of aluminium on dimensional change of sintered 430 Ferritic Stainless Steel

Aluminium is added to decrease matrix chromium losses on 430 stainless steel sintered on nitrogen atmosphere. Three different ways were used to add a 3% (in weight) aluminium: as elemental powder, as prealloyed powder, and as intermetallic Fe-AI compound. After die pressing at densities between 6.1-6.5 g/cm3, samples were sintered on vacuum and on N2-5%H2 atmosphere in a dilatometric furnace. Therefore, dimensional change was recorded during sintering. Weight gain was obtained after nitrogen sintering on all materials due to nitrides formation. Sample expansion was obtained on all nitrogen sintered steels with Al additions. Microstructure showed a dispersion of aluminium nitrides when pre-alloyed powders are used. On the contrary, aluminium nitride areas can be found when aluminium is added as elemental powders or as Fe-AI intermetallics. Also nitrogen atmosphere leads to austenite formation and hence, on cooling, dilatometric results showed a dimensional change at austenitic-ferritic phase transformation temperature

Properties of aluminium nodules foamed with concentrated solar energy

Commercial aluminium foam filled structures and sandwich panels are available for structural applications. As alternative to these materials, small granular foamed pieces are proposed to fill structures as well as sandwich panels. On the present work, foam precursors are obtained by Powder Metallurgy (PM) route, using natural calcium carbonate as foaming agent instead of titanium hydride. Extruded precursor bars were cut into small pieces (around 4.5 mm long and 5mm in diameter). Foaming treatment was carried out on two different ways: electrical preheated furnace and by solar furnace. Foamed nodules presented a low cell size, density e.g. 0.67 g/cm3 to 0.88 g/cm3 and a height/diameter ratio between 0.72 and 0.84 as a function of precursor size. These properties depend on the foaming particle size, foaming cycle and precursor dimensions. Carbonate precursors are easily foamed by concentrated solar energy, due to the lower risk of cell collapse than with hydride precursors, resulting from cell stabilization by oxide skin formation into cells and a low degree of foamed nodules bonding

Tratamiento térmico de espumación de precursores de aluminio-silicio en horno solar de lecho fluidificado

La fabricación de espumas de aluminio de poro cerrado mediante técnicas pulvimetalúrgias, conlleva una etapa de calentamiento del material (precursor) hasta su fusión y la formación de la espuma. En el presente trabajo, como continuación de los trabajos con energía solar concentrada aplicada directamente a la espumación de precursores Al10Si+0.8%TiH2, se han sometido al tratamiento de espumación en un horno solar de lecho fluidificado con el objetivo de obtener un tratamiento en volumen más homogéneo y uniforme, de manera que se puedan determinar la viabilidad del tratamiento, el procedimiento y los tiempos de residencia necesarios para llevar a cabo este proceso de manera discontinua o preindustrial. Así se han obtenido espumas 0,68 g/cm3 de densidad sin colapso de poros o decantación de aluminio, si bien como consecuencia del calentamiento preferencial por la base del molde, las espumas obtenidas presentan un tamaño de poro ligeramente más grande en la base de la espuma

RECLAMO: virtual and collaborative honeynets based on trust management and autonomous systems applied to intrusion management

Security intrusions in large systems is a problem due to its lack of scalability with the current IDS-based approaches. This paper describes the RECLAMO project, where an architecture for an Automated Intrusion Response System (AIRS) is being proposed. This system will infer the most appropriate response for a given attack, taking into account the attack type, context information, and the trust and reputation of the reporting IDSs. RECLAMO is proposing a novel approach: diverting the attack to a specific honeynet that has been dynamically built based on the attack information. Among all components forming the RECLAMO's architecture, this paper is mainly focused on defining a trust and reputation management model, essential to recognize if IDSs are exposing an honest behavior in order to accept their alerts as true. Experimental results confirm that our model helps to encourage or discourage the launch of the automatic reaction process