A machine learning approach for detecting cognitive interference based on eye-tracking data

The Stroop test evaluates the ability to inhibit cognitive interference. This interference occurs when the processing of one stimulus characteristic affects the simultaneous processing of another attribute of the same stimulus. Eye movements are an indicator of the individual attention load required for inhibiting cognitive interference. We used an eye tracker to collect eye movements data from more than 60 subjects each performing four different but similar tasks (some with cognitive interference and some without). After the extraction of features related to fixations, saccades and gaze trajectory, we trained different Machine Learning models to recognize tasks performed in the different conditions (i.e., with interference, without interference). The models achieved good classification performances when distinguishing between similar tasks performed with or without cognitive interference. This suggests the presence of characterizing patterns common among subjects, which can be captured by machine learning algorithms despite the individual variability of visual behavior

The AXIOM platform for next-generation cyber physical systems

Cyber-Physical Systems (CPSs) are widely used in many applications that require interactions between humans and their physical environment. These systems usually integrate a set of hardware-software components for optimal application execution in terms of performance and energy consumption. The AXIOM project (Agile, eXtensible, fast I/O Module), presented in this paper, proposes a hardware-software platform for CPS coupled with an easy parallel programming model and sufficient connectivity so that the performance can scale-up by adding multiple boards. AXIOM supports a task-based programming model based on OmpSs and leverages a high-speed, inexpensive communication interface called AXIOM-Link. The board also tightly couples the CPU with reconfigurable resources to accelerate portions of the applications. As case studies, AXIOM uses smart video surveillance, and smart home living applicationsThis work is partially supported by the European Union H2020 program through the AXIOM project (grant ICT-01-2014 GA 645496) and HiPEAC (GA 687698), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, and by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). We also thank the Xilinx University Program for its hardware and software donations.Peer ReviewedPostprint (author's final draft

O-AUPA-Observatorio de agricultura urbana, periurbana y agroecología: conflictos ambientales y gobernanza alimentaria

INTA. Estación Experimental Agropecuaria Manfredi. O-AUPAFil: Giobellina, B. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. O-AUPA; ArgentinaFil: Benitez, Roger Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. O-AUPA; Argentina.Fil: Mari, Mari, Nicolás Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. AER Cruz del Eje; Argentina.Fil: Vitale, Juan Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina. O-AUPA; ArgentinaFil: Ermini, Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil. AER Santa Rosa; ArgentinaFil: Barsky, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina. O-AUPA; ArgentinaFil: Quinteros, M. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina. O-AUPA; ArgentinaFil: Lobos, D. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Celiz, Yuliana. Consejo Nacional de Investigaciones Científicas y Técnicas-INTA O-AUPA; ArgentinaFil: Bisio, C. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaFil: Pons, Diego Hernan. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Grupo de Manejo de Cultivo y Recursos Naturales; ArgentinaFil: Baldini, C. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias; Argentin

UDOO App Inventor: Introducing Novices to the Internet of Things

This paper describes the opportunities provided by the new graphical tool UDOO App Inventor (UAPPI) for enhancing the programming learning experience. With this tool, coding and programming are no longer limited to screen pixels but will incorporate real objects in the physical world. The aim of the authors' research is to develop new tools for coding alphabetization, by focusing on live programming, event programming, physical computing and overcoming syntax obstacles by using blocks programming. They describe two simple Research through Design case studies carried out with different categories of attendees in order to illustrate the potential of the UAPPI platform. In the first case, the authors render a door interactive, while in the second, they build a simple rover

Axiom voice recognition dataset

<p>The AXIOM Voice Dataset has the main purpose of gathering audio recordings from Italian natural language speakers. This voice data collection intended to obtain audio reconding sample for the training and testing of VIMAR algorithm implemented for the Smart Home scenario for the Axiom board. The final goal was to developing an efficient voice recognition system using machine learning algorithms.  A team of UX researchers of the University of Siena collected data for five months and tested the voice recognition system on the AXIOM board [1]. The data acquisition process involved natural Italian speakers who provided their written consent to participate in the research project. The participants were selected in order to maintain a cluster with different characteristics in gender, age, region of origin and background. </p