Multifractal Mice: Measuring Task Engagement and Readiness-to-hand via Hand Movement

2020 ◽  
Author(s):  
Daniel Bennett
Keyword(s):  
Hand Movement ◽  
Task Engagement ◽  
Computational Method ◽  
Data Driven ◽  
Prior Work ◽  
Wide Range ◽  
Input Signals ◽  
Data Driven Approach ◽  
Future Work ◽  
And Task

We introduce an unobtrusive, computational method for measuring readiness-to-hand and task-engagement during interaction."Readiness-to-hand" is an influential concept describing fluid, intuitive tool use, with attention on task rather than tool; it has longbeen significant in HCI research, most recently via metrics of tool-embodiment and immersion. We build on prior work in cognitivescience which relates readiness-to-hand and task engagement to multifractality: a measure of complexity in behaviour. We conduct areplication study (N=28), and two new experiments (N=44, N=30), which show that multifractality correlates with task-engagement and other features of readiness-to-hand overlooked in previous measures, including familiarity with task. This is the first evaluation of multifractal measures of behaviour in HCI. Since multifractality occurs in a wide range of behaviours and input signals, we support future work by sharing scripts and data (https://osf.io/2hm9u/), and introducing a new data-driven approach to parameter selection

Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

2018 ◽  
Vol 10 (02) ◽  
pp. 1840001 ◽  
Author(s):  
Catherine M. Sweeney-Reed ◽  
Slawomir J. Nasuto ◽  
Marcus F. Vieira ◽  
Adriano O. Andrade
Keyword(s):  
Empirical Mode Decomposition ◽  
Data Driven ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Data Driven Approach

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

scholarly journals A computational method to solve for the heat conduction temperature field based on data-driven approach

2021 ◽  
pp. 165-165
Author(s):  
Kun Li ◽  
Shiquan Shan ◽  
Qi Zhang ◽  
Xichuan Cai ◽  
Zhou Zhijun
Keyword(s):  
Temperature Field ◽  
Heat Conduction ◽  
Steady State ◽  
Transient State ◽  
Absolute Error ◽  
Computational Method ◽  
Data Driven ◽  
One Dimensional ◽  
Data Driven Approach ◽  
The One

In this paper, a computational method for solving for the one-dimensional heat conduction temperature field is proposed based on a data-driven approach. The traditional numerical solution requires algebraic processing of the heat conduction differential equations, and necessitates the use of a complex mathematical derivation process to solve for the temperature field. In this paper, a temperature field solution model called HTM (Hidden Temperature Method) is proposed. This model uses an artificial neural network to establish the correspondence relationship of the node temperature values during the iterative process, so as to obtain the "Data to Data" solution. In this work, one example of one-dimensional steady state and three examples of one-dimensional transient state are selected, and the calculated values are compared to those obtained by traditional numerical methods. The mean-absolute error(MAE)of the steady state is only 0.2508, and among the three transient cases, the maximum mean-square error(MSE) is only 2.6875, indicating that the model is highly accurate in both steady-state and transient conditions. This shows that the HTM simulation can be applied to the solution of the heat conduction temperature field. This study provides a basis for the further optimization of the HTM algorithm.

A non-parametric, data-driven approach to despiking ocean surface wave time series

2021 ◽  
Author(s):  
Mika P. Malila ◽  
Patrik Bohlinger ◽  
Susanne Støle-Hentschel ◽  
Øyvind Breivik ◽  
Gaute Hope ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Wave ◽  
Missing Values ◽  
Computational Cost ◽  
Ocean Surface ◽  
Data Driven ◽  
Storm Event ◽  
Wide Range ◽  
Data Driven Approach ◽  
Ocean Surface Wave

AbstractWe propose a methodology for despiking ocean surface wave time series based on a Bayesian approach to data-driven learning known as Gaussian Process (GP) regression. We show that GP regression can be used for both robust detection of erroneous measurements and interpolation over missing values, while also obtaining a measure of the uncertainty associated with these operations. In comparison with a recent dynamical phase space-based despiking method, our data-driven approach is here shown to lead to improved wave signal correlation and spectral tail consistency, although at a significant increase in computational cost. Our results suggest that GP regression is thus especially suited for offline quality control requiring robust noise detection and replacement, where the subsequent analysis of the despiked data is sensitive to the accidental removal of extreme or rare events such as abnormal or rogue waves. We assess our methodology on measurements from an array of four co-located 5-Hz laser altimeters during a much-studied storm event the North Sea covering a wide range of sea states.

Lifestyles in Amazon: Evidence from online reviews enhanced recommender system

2019 ◽  
Vol 62 (6) ◽  
pp. 689-706 ◽  
Author(s):  
Yinghui Huang ◽  
Hui Liu ◽  
Weiqing Li ◽  
Zichao Wang ◽  
Xiangen Hu ◽  
...  
Keyword(s):  
Personality Traits ◽  
Recommender System ◽  
Consumer Preference ◽  
Addition Product ◽  
Online Reviews ◽  
Data Driven ◽  
Big Five Personality ◽  
Predicting Performance ◽  
Wide Range ◽  
Data Driven Approach

Online lifestyles have been shown to reflect and affect consumers’ preferences across a wide range of online scenarios. In the context of e-commerce, it still remains unclear whether online lifestyles are practically influential in predicting consumers’ purchasing preferences across different product categories, especially considering its potential influence over the widely used personality traits. In this study, we provide the first, to the best of our knowledge, quantitative demonstration of online lifestyles in predicting consumers’ online purchasing preferences in e-commerce by using a data-driven approach. We first construct an online lifestyles lexicon including seven distinct dimensions using text mining approaches based on consumers’ language use behaviors. We then incorporate the lexicon in a typical e-commerce recommender system to predict consumers’ purchasing preferences. Experimental results on Amazon Review Dataset show that online lifestyles and all its subdimensions significantly improve preference predicting performance and outperform the widely used Big Five personality traits as a whole. In addition, product types significantly moderate the influence of online lifestyle on consumer preference. The strong empirical evidence indicates that the big e-commerce consumer data facilitates more specialized market psychographic segmentation, which advances data-driven marketing decision-making.

scholarly journals Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

2020 ◽  
Vol 476 (2243) ◽  
pp. 20190841
Author(s):  
Héctor Andrade-Loarca ◽  
Gitta Kutyniok ◽  
Ozan Öktem
Keyword(s):  
Edge Detection ◽  
High Performance ◽  
Hybrid Methods ◽  
Tomographic Reconstruction ◽  
Data Driven ◽  
Model Based ◽  
Edge Classification ◽  
Wide Range ◽  
Data Driven Approach ◽  
Sparse Approximations

Semantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox and limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method that is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model class of images, and the data-driven method of a suitably designed convolutional neural network. We show that it avoids the distracted supervision paradox and achieves high performance in semantic edge detection. In addition, our approach requires significantly fewer parameters than a pure data-driven approach. Finally, we present several applications such as tomographic reconstruction and show that our approach significantly outperforms former methods, thereby also indicating the value of such hybrid methods for biomedical imaging.

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