The distractor frequency effect in picture–word interference: evidence for response exclusion

In 3 experiments, subjects named pictures with low- or high-frequency superimposed distractor words. In a 1st experiment, we replicated the finding that low-frequency words induce more interference in picture naming than high-frequency words (i.e., distractor frequency effect; Miozzo & Caramazza, 2003). According to the response exclusion hypothesis, this effect has its origin at a postlexical stage and is related to a response buffer. The account predicts that the distractor frequency effect should only be present when a response to the word enters the response buffer. This was tested by masking the distractor (Experiment 2) and by presenting it at various time points before stimulus onset (Experiment 3). Results supported the hypothesis by showing that the effect was only present when distractors were visible, and if they were presented in close proximity to the target picture. These results have implications for the models of lexical access and for the tasks that can be used to study this process

Artificial turf developments and sport applications at Ghent University

In the past decades artificial turf fields have developed into a worthy alternative for natural grass in outdoor sports appliance such as football, rugby and hockey. Heavy rainfall and periods of drought can affect a natural pitch. Several sport clubs own only a limited number of pitches and therefore are obligated to make full advantage of them. This is one of the reasons why many of these clubs are changing towards fields made of artificial turf which are always available, provided that the correct materials and maintenance are considered and regulated testing procedures are followed. The installation cost may be higher but an artificial turf field can be used more frequently than its natural counterpart and therefore be more profitable on the long run due to the lower overall maintenance costs. Furthermore, natural grass fields need enough sunlight for the grass to grow and cannot grow well in desert or extreme cold environments, whereas artificial turf can be used in many environments. Ghent University has a long history in the development and testing of artificial turf , which will be highlighted in this contribution

Elegant design of carbon nanotube foams with double continuous structure for metamaterials in a broad frequency range

Carbon nanotube (CNT) foams with negative permittivity and permeability are successfully prepared by chemical vapor deposition (CVD) and post-treatment. A double negative metamaterial in the 1-1000 MHz frequency range with double continuous structure results by effectively compounding the CNT foam with a polymer material, i.e. epoxy or nanosilver silicone resin. The negative permeability is specifically attributed to the three-dimensional CNT interactions as clear from the study of the relation of the material microstructure and the macroscopic measurements. Compared to CNT foam/epoxy composites, CNT foam/nanosilver/silicone composites have a lower permeability but a more excellent electrical conductivity or permittivity. It is also shown that the carbon source time during CVD and post-pressurization can be adjusted to allow for both negative permittivity and permeability. This contribution highlights a convenient method to obtain a metamaterial in a much larger frequency range (ca. 1 to 1000 MHz) than the state-of-the-art. It thus supports the expansion of the application range of metamaterials and simplifies their preparation, which is of great significance for the wider use of these materials

Evaluating the exit pressure method for measurements of normal stress difference at high shear rates

A challenge for polymer rheology is the reliable determination of shear dependent first normal stress difference (N-1 values) at high shear rates (>10 s(-1)). Here, we evaluate the correctness of the commonly applied exit pressure method focusing on polypropylene and high and low density polyethylene melts at 200 degrees C. It is demonstrated that the linear extrapolation of pressure values toward the die exit, which is a key step in the application of the exit pressure method, is affordable to determine N-1 values despite that these extrapolated exit pressure values are characterized by a relative deviation of 25%-40%. The validity of the exit pressure method is further supported by an excellent match with rheological data from the Laun rule (exponent close to 0.7) and a representative simulation of extrudate swelling data in the width and height direction, considering tuned parameters for the Phan-Thien-Tanner constitutive model. Also, the absence of a significant viscous heating effect near the die exit is highlighted based on numerical analysis. (c) 2020 The Society of Rheology

Super tasters and mighty movers: extending The Food Friends® messages into early elementary school

2013 Spring.Includes bibliographical references.The prevalence of childhood obesity has been increasing over the last thirty years for preschool aged children, two to five years of age, as well as among early elementary aged children, six to eleven years of age. The epidemic nature of this problem has led to the creation of multiple programs and intervention targeted at preschoolers aimed at preventing these upward trends into early elementary school and adulthood. The preschool years are particularly important for the development of eating habits along with the development of gross motor skills. Behavior change has been seen within this age group following interventions, but retention of such behaviors as the children progress into kindergarten and first grade has not been as well documented. The overall purpose of this project was to develop "booster" programming for kindergarten and first grade classrooms that extends the messages from The Food Friends®, preschool nutrition and physical activity programs, into early elementary school in an effort to sustain behavior changes made in preschool. To ascertain the best method for implementing a program into the classrooms, surveys and interviews were conducted with a convenient sample of kindergarten and first grade teachers. Survey questions were mailed; follow-up telephone interviews were conducted with a subsample of respondents. Findings guided the development and implementation of the "booster" programming in kindergarten classes. Process evaluation surveys were conducted to assess the fidelity of program and guide the development of the second year of programming and modifications to Year 1. The main themes found from the formative surveys and interviews included: 1) nutrition was not a consistent lesson topic; 2) physical activity was left for gym class and/or recess; and 3) the need for nutrition and activity messages/lessons to be incorporated into academic subject areas. A 5 unit "booster" program, based on Social Cognitive Theory, was developed utilizing The Food Friends® characters and themes of 'Super Tasters' and 'Mighty Movers'. Classroom-based lessons, with accompanying posters and banners for the cafeteria and gym, were implemented in two schools from December 2011 to April 2012. Process evaluation surveys were conducted online with teachers after each unit for fidelity and overall impressions of lessons/activities; interviews were conducted one-on-one with Extension agents. Findings included: 1) all agreed that they enjoyed the "booster" program; 2) it was helpful to have an Extension agent come to the classroom; and 3) few completed lessons intended to be taught by classroom teachers. Appropriate modifications to Year 1's program guided the development of Year 2 programming, slated for implementation in 2012-13 school year. The efficacy of the "booster" programming on behaviors will be evaluated as part of a larger longitudinal study. The ability to resonate messages of trying new foods and being more active within kindergarten and first grade students will contribute to the establishment of healthful behaviors at a young age, building the foundation of lifelong healthy lifestyles

In-depth comparative evaluation of supervised machine learning approaches for detection of cybersecurity threats

This paper describes the process and results of analyzing CICIDS2017, a modern, labeled data set for testing intrusion detection systems. The data set is divided into several days, each pertaining to different attack classes (Dos, DDoS, infiltration, botnet, etc.). A pipeline has been created that includes nine supervised learning algorithms. The goal was binary classification of benign versus attack traffic. Cross-validated parameter optimization, using a voting mechanism that includes five classification metrics, was employed to select optimal parameters. These results were interpreted to discover whether certain parameter choices were dominant for most (or all) of the attack classes. Ultimately, every algorithm was retested with optimal parameters to obtain the final classification scores. During the review of these results, execution time, both on consumerand corporate-grade equipment, was taken into account as an additional requirement. The work detailed in this paper establishes a novel supervised machine learning performance baseline for CICIDS2017

Classification hardness for supervised learners on 20 years of intrusion detection data

This article consolidates analysis of established (NSL-KDD) and new intrusion detection datasets (ISCXIDS2012, CICIDS2017, CICIDS2018) through the use of supervised machine learning (ML) algorithms. The uniformity in analysis procedure opens up the option to compare the obtained results. It also provides a stronger foundation for the conclusions about the efficacy of supervised learners on the main classification task in network security. This research is motivated in part to address the lack of adoption of these modern datasets. Starting with a broad scope that includes classification by algorithms from different families on both established and new datasets has been done to expand the existing foundation and reveal the most opportune avenues for further inquiry. After obtaining baseline results, the classification task was increased in difficulty, by reducing the available data to learn from, both horizontally and vertically. The data reduction has been included as a stress-test to verify if the very high baseline results hold up under increasingly harsh constraints. Ultimately, this work contains the most comprehensive set of results on the topic of intrusion detection through supervised machine learning. Researchers working on algorithmic improvements can compare their results to this collection, knowing that all results reported here were gathered through a uniform framework. This work's main contributions are the outstanding classification results on the current state of the art datasets for intrusion detection and the conclusion that these methods show remarkable resilience in classification performance even when aggressively reducing the amount of data to learn from