38 research outputs found
A review of verbal and non-verbal human–robot interactive communication
No full textAbstractIn this paper, an overview of human–robot interactive communication is presented, covering verbal as well as non-verbal aspects. Following a historical introduction, and motivation towards fluid human–robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human–robot communication. Then, the ten desiderata are examined in detail, culminating in a unifying discussion, and a forward-looking conclusion
Characterising and Mitigating Aggregation-Bias in Crowdsourced Toxicity Annotations
No full textTraining machine learning (ML) models for natural language processing usually requires large amount of data, often acquired through crowdsourcing. The way this data is collected and aggregated can have an effect on the outputs of the trained model such as ignoring the labels which differ from the majority. In this paper we investigate how label aggregation can bias the ML results towards certain data samples and propose a methodology to highlight and mitigate this bias. Although our work is applicable to any kind of label aggregation for data subject to multiple interpretations, we focus on the effects of the bias introduced by majority voting on toxicity prediction over sentences. Our preliminary results point out that we can mitigate the majority-bias and get increased prediction accuracy for the minority opinions if we take into account the different labels from annotators when training adapted models, rather than rely on the aggregated labels.Accepted Author ManuscriptWeb Information System
CaptureBias: Supporting Media Scholars with Ambiguity-Aware Bias Representation for News Videos
No full textIn this project we explore the presence of ambiguity in textual and visual media and its influence on accurately understanding andcapturing bias in news. We study this topic in the context of supportingmedia scholars and social scientists in their media analysis. Our focuslies on racial and gender bias as well as framing and the comparisonof their manifestation across modalities, cultures and languages. In thispaper we lay out a human in the loop approach to investigate the role ofambiguity in detection and interpretation of bias.Accepted Author ManuscriptWeb Information System
Arax: A Runtime Framework for Decoupling Applications from Heterogeneous Accelerators
No full textToday, using multiple heterogeneous accelerators efficiently from
applications and high-level frameworks, such as TensorFlow and Caffe, poses
significant challenges in three respects: (a) sharing accelerators, (b)
allocating available resources elastically during application execution, and
(c) reducing the required programming effort. In this paper, we present Arax, a
runtime system that decouples applications from heterogeneous accelerators
within a server. First, Arax maps application tasks dynamically to available
resources, managing all required task state, memory allocations, and task
dependencies. As a result, Arax can share accelerators across applications in a
server and adjust the resources used by each application as load fluctuates
over time. dditionally, Arax offers a simple API and includes Autotalk, a stub
generator that automatically generates stub libraries for applications already
written for specific accelerator types, such as NVIDIA GPUs. Consequently, Arax
applications are written once without considering physical details, including
the number and type of accelerators. Our results show that applications, such
as Caffe, TensorFlow, and Rodinia, can run using Arax with minimum effort and
low overhead compared to native execution, about 12% (geometric mean). Arax
supports efficient accelerator sharing, by offering up to 20% improved
execution times compared to NVIDIA MPS, which supports NVIDIA GPUs only. Arax
can transparently provide elasticity, decreasing total application turn-around
time by up to 2x compared to native execution without elasticity support
Muscular Strength and Jumping Performance after Handball Training versus Physical Education Program for Pre-Adolescent Children
No full text The purpose was to compare a 6-mo. specific handball training program and a typical physical education program on various strength and jumping skills. The participants ( M age = 13.7 yr., SD = 1.5) were divided into the Handball Group ( n = 51) and the Physical Education Group ( n = 70). The latter performed 3 sessions/week (60 min.) including ball-handling drills, horizontal and vertical jump shots, fast break, and several defensive skills. The former performed the program provided by the Ministry of Education including track and field and other team sport drills. Analyses of covariance showed that the handball group displayed greater improvement in explosive strength of upper limbs, jumping performance, maximum isometric force of right grip, and 10-m running velocity. Handball training can significantly improve pre-adolescent performance with upper and lower limbs. Inclusion of specific handball drills in the physical education program is recommended. </jats:p
On the fairness of crowdsourced training data and Machine Learning models for the prediction of subjective properties. The case of sentence toxicity: To be or not to be #$@&%*! toxic? To be or not to be fair?
No full textTraining machine learning (ML) models for natural language processing usually requires lots of data that is often acquired through crowdsourcing. In crowdsourcing, crowd workers annotate data samples according to one or more properties, such as the sentiment of a sentence, the violence of a video segment, the aesthetics of an image, ... To ensure quality of the annotations, several workers annotate the same sample, and their annotations are combined into one unique label using aggregation techniques such as majority voting.When the property to be annotated by the workers is subjective, the workers’ annotations for one same sample might differ, but all be valid. The way the annotations are aggregated can have an effect on the fairness of the outputs of the trained model. For example only accounting for the majority vote leads to ignoring the workers’ opinions which differ from the majority and consequently being discriminative towards certain workers. Also, ML models are not always designed to account for individual opinions, for simplicity's or performance's sake. Finally, to the best of our knowledge, no method exists to assess the fairness of a ML algorithm predicting a subjective property. In this thesis we address such limitations by seeking an answer to the following research question: how can targeted crowdsourcing be used to increase the fairness of ML algorithms trained for subjective properties' prediction?We investigate how annotation aggregation via majority voting creates a dataset bias towards the majority opinion, and how this dataset bias in combination with the current limits of ML models lead to an algorithmic bias of the ML models trained with this dataset and unfairness in the model’s outputs. We assume that an ML model able to return each annotation of each user is a fair model. We propose a new evaluation method of the ML models' fairness, and a methodology to highlight and mitigate potential unfairness based on the creation of adapted training datasets and ML models. Although our work is applicable to any kind of label aggregation for any data subject to multiple interpretations, we focus on the effects of the bias introduced by majority voting for the task of predicting sentence toxicity. Our results show that the fairness evaluation method that we create enables to identify unfair algorithms and compare algorithmic fairness, and the final fairness metric is usable in the training process of ML models. The experiments on the models point out that we can mitigate the biases resulting from majority voting and increase the fairness towards the minority opinions. This is provided that the workers’ individual information and each of their annotations are taken into account when training adapted models, rather than only relying on the aggregated annotations, and that the dataset is resampled on criteria according to the favoured aspect of fairness. We also highlight that more work needs to be done to develop crowdsourcing methods to collect high-quality annotations of subjective properties, possibly at low-cost.Computer Science | Web Information System
