557 research outputs found
TOBE: Tangible Out-of-Body Experience
We propose a toolkit for creating Tangible Out-of-Body Experiences: exposing
the inner states of users using physiological signals such as heart rate or
brain activity. Tobe can take the form of a tangible avatar displaying live
physiological readings to reflect on ourselves and others. Such a toolkit could
be used by researchers and designers to create a multitude of potential
tangible applications, including (but not limited to) educational tools about
Science Technologies Engineering and Mathematics (STEM) and cognitive science,
medical applications or entertainment and social experiences with one or
several users or Tobes involved. Through a co-design approach, we investigated
how everyday people picture their physiology and we validated the acceptability
of Tobe in a scientific museum. We also give a practical example where two
users relax together, with insights on how Tobe helped them to synchronize
their signals and share a moment
Gender Fairness within the Force Concept Inventory
Research on the test structure of the Force Concept Inventory (FCI) has
largely ignored gender, and research on FCI gender effects (often reported as
"gender gaps") has seldom interrogated the structure of the test. These
rarely-crossed streams of research leave open the possibility that the FCI may
not be structurally valid across genders, particularly since many reported
results come from calculus-based courses where 75% or more of the students are
men. We examine the FCI considering both psychometrics and gender
disaggregation (while acknowledging this as a binary simplification), and find
several problematic questions whose removal decreases the apparent gender gap.
We analyze three samples (total , ) looking for
gender asymmetries using Classical Test Theory, Item Response Theory, and
Differential Item Functioning. The combination of these methods highlights six
items that appear substantially unfair to women and two items biased in favor
of women. No single physical concept or prior experience unifies these
questions, but they are broadly consistent with problematic items identified in
previous research. Removing all significantly gender-unfair items halves the
gender gap in the main sample in this study. We recommend that instructors
using the FCI report the reduced-instrument score as well as the 30-item score,
and that credit or other benefits to students not be assigned using the biased
items.Comment: 18 pages, 3 figures, 5 tables; submitted to Phys. Rev. PE
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
Time Series Classification (TSC) has received much attention in the past two
decades and is still a crucial and challenging problem in data science and
knowledge engineering. Indeed, along with the increasing availability of time
series data, many TSC algorithms have been suggested by the research community
in the literature. Besides state-of-the-art methods based on similarity
measures, intervals, shapelets, dictionaries, deep learning methods or hybrid
ensemble methods, several tools for extracting unsupervised informative summary
statistics, aka features, from time series have been designed in the recent
years. Originally designed for descriptive analysis and visualization of time
series with informative and interpretable features, very few of these feature
engineering tools have been benchmarked for TSC problems and compared with
state-of-the-art TSC algorithms in terms of predictive performance. In this
article, we aim at filling this gap and propose a simple TSC process to
evaluate the potential predictive performance of the feature sets obtained with
existing feature engineering tools. Thus, we present an empirical study of 11
feature engineering tools branched with 9 supervised classifiers over 112 time
series data sets. The analysis of the results of more than 10000 learning
experiments indicate that feature-based methods perform as accurately as
current state-of-the-art TSC algorithms, and thus should rightfully be
considered further in the TSC literature
Ripe to be Heard: Worker Voice in the Fair Food Programme
The Fair Food Program (FFP) provides a mechanism through which agricultural workers’ collective voice is expressed, heard and responded to within global value chains. The FFP's model of worker-driven social responsibility presents an alternative to traditional corporate social responsibility. This article identifies the FFP's key components and demonstrates its resilience by identifying the ways in which the issues faced by a new group of migrant workers – recruited through a “guest-worker” scheme – were incorporated and dealt with. This case study highlights the important potential presented by the programme to address labour abuses across transnationalized labour markets while considering early replication possibilities
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing
molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin
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