Is Infidelity Predictable? Using Interpretable Machine Learning to Identify the Most Important Predictors of Infidelity

2020 ◽  
Author(s):  
Laura Marika Vowels ◽  
Matthew J Vowels ◽  
Kristen P Mark
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Large Body ◽  
Predictor Variable ◽  
Well Being ◽  
Relationship Length ◽  
Three Samples ◽  
Online Infidelity ◽  
Shapley Values ◽  
Game Theoretic

Infidelity is a common occurrence in relationships and can have a devastating impact on both partners’ well-being. A large body of literature have attempted to factors that can explain or predict infidelity but have been unable to estimate the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and online infidelity and intentions toward future infidelity across three samples (two dyadic samples; N = 1846). We also used a game theoretic explanation technique, Shapley values, which allowed us to estimate the effect size of each predictor variable on infidelity. The present study showed that infidelity was somewhat predictable overall with interpersonal factors (relationship satisfaction, love, desire, relationship length) being the most predictive. The results suggest that addressing relationship difficulties early in the relationship can help prevent future infidelity.

scholarly journals Identifying the strongest self-report predictors of sexual satisfaction using machine learning

2022 ◽  
pp. 026540752110470
Author(s):  
Laura M. Vowels ◽  
Matthew J. Vowels ◽  
Kristen P. Mark
Keyword(s):  
Machine Learning ◽  
Relationship Satisfaction ◽  
Sexual Satisfaction ◽  
Predictor Variable ◽  
Well Being ◽  
Self Report ◽  
Future Research ◽  
Total N ◽  
Relationship Length ◽  
Two Samples

Sexual satisfaction has been robustly associated with relationship and individual well-being. Previous studies have found several individual (e.g., gender, self-esteem, and attachment) and relational (e.g., relationship satisfaction, relationship length, and sexual desire) factors that predict sexual satisfaction. The aim of the present study was to identify which variables are the strongest, and the least strong, predictors of sexual satisfaction using modern machine learning. Previous research has relied primarily on traditional statistical models which are limited in their ability to estimate a large number of predictors, non-linear associations, and complex interactions. Through a machine learning algorithm, random forest (a potentially more flexible extension of decision trees), we predicted sexual satisfaction across two samples (total N = 1846; includes 754 individuals forming 377 couples). We also used a game theoretic interpretation technique, Shapley values, which allowed us to estimate the size and direction of the effect of each predictor variable on the model outcome. Findings showed that sexual satisfaction is highly predictable (48–62% of variance explained) with relationship variables (relationship satisfaction, importance of sex in relationship, romantic love, and dyadic desire) explaining the most variance in sexual satisfaction. The study highlighted important factors to focus on in future research and interventions.

scholarly journals Identifying the Most Important Predictors of Sexual Satisfaction using Interpretable Machine Learning

2020 ◽  
Author(s):  
Laura Marika Vowels ◽  
Matthew J Vowels ◽  
Kristen P Mark
Keyword(s):  
Machine Learning ◽  
Relationship Satisfaction ◽  
Sexual Satisfaction ◽  
Learning Algorithm ◽  
Predictor Variable ◽  
Policy Makers ◽  
Total N ◽  
Two Samples ◽  
Game Theoretic ◽  
Variance Explained

Previous studies have found a number of different factors that are associated with sexual satisfaction but have been unable to estimate the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict sexual satisfaction across two samples (total N = 1846; includes 754 individuals forming 377 couples). We also used a game theoretic interpretation technique, which allowed us to estimate the size and direction of the effect of each predictor variable on the model outcome. The present study showed that sexual satisfaction is highly predictable (48-62% of variance explained) with relationship variables (relationship satisfaction, perception of love and sex, romantic love, dyadic desire) explaining the most variance in sexual satisfaction. The study enables researchers, policy-makers, and practitioners to target variables that are the most likely to improve sexual satisfaction in order to better people’s sexual lives.

scholarly journals AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the LifeChair Smart Cushion

2019 ◽  
Author(s):  
Katia Bourahmoune ◽  
Toshiyuki Amagasa
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Well Being ◽  
Recognition System ◽  
Training System ◽  
Sensor Data ◽  
Physical And Mental Health ◽  
Sitting Posture ◽  
Posture Monitoring ◽  
Posture Recognition

Humans spend on average more than half of their day sitting down. The ill-effects of poor sitting posture and prolonged sitting on physical and mental health have been extensively studied, and solutions for curbing this sedentary epidemic have received special attention in recent years. With the recent advances in sensing technologies and Artificial Intelligence (AI), sitting posture monitoring and correction is one of the key problems to address for enhancing human well-being using AI. We present the application of a sitting posture training smart cushion called LifeChair that combines a novel pressure sensing technology, a smartphone app interface and machine learning (ML) for real-time sitting posture recognition and seated stretching guidance. We present our experimental design for sitting posture and stretch pose data collection using our posture training system. We achieved an accuracy of 98.93% in detecting more than 13 different sitting postures using a fast and robust supervised learning algorithm. We also establish the importance of taking into account the divergence in user body mass index in posture monitoring. Additionally, we present the first ML-based human stretch pose recognition system for pressure sensor data and show its performance in classifying six common chair-bound stretches.

Uncovering the Most Important Factors for Predicting Sexual Desire using Interpretable Machine Learning

2020 ◽  
Author(s):  
Laura Marika Vowels ◽  
Matthew J Vowels ◽  
Kristen P Mark
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Sexual Desire ◽  
Learning Algorithm ◽  
Predictor Variable ◽  
Machine Learning Algorithm ◽  
Societal Factors ◽  
Interpretable Machine Learning ◽  
Several Variables ◽  
Two Samples

Previous studies have found a number of individual, relational, and societal factors that are associated with sexual desire. However, no studies to date have examined which of these variables are the most predictive of sexual desire. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict sexual desire from a large number of predictors across two samples (N = 1846; includes 754 individuals forming 377 couples). We also used a Shapley value technique to estimate the size and direction of the effect of each predictor variable on the model outcome. The model predicted around 40% of variance in dyadic and solitary desire with women’s desire being more predictable than men’s. Several variables consistently predicted sexual desire including individual, relational, and societal factors. The study provides the strongest evidence to date of the most important predictors for dyadic and solitary desire.

Application of machine learning to map the global distribution of deep-sea sediments

2020 ◽  
Author(s):  
Markus Diesing
Keyword(s):  
Machine Learning ◽  
Deep Sea ◽  
Learning Algorithm ◽  
Predictor Variable ◽  
Global Ocean ◽  
Predictor Variables ◽  
Maximum Temperature ◽  
Sea Floor ◽  
Deep Sea Sediments ◽  
Calcareous Sediment

<p>The deep-sea floor accounts for >90% of seafloor area and >70% of the Earth’s surface. It acts as a receptor of the particle flux from the surface layers of the global ocean, is a place of biogeochemical cycling, records environmental and climate conditions through time and provides habitat for benthic organisms. Maps of the spatial patterns of deep-sea sediments are therefore a major prerequisite for many studies addressing aspects of deep-sea sedimentation, biogeochemistry, ecology and related fields.</p><p>A new digital map of deep-sea sediments of the global ocean is presented. The map was derived by applying the Random Forest machine-learning algorithm to published sample data of seafloor lithologies and environmental predictor variables. The selection of environmental predictors was initially based on the current understanding of the controls on the distribution of deep-sea sediments and the availability of data. A predictor variable selection process ensured that only important and uncorrelated variables were employed in the model. The three most important predictor variables were sea-surface maximum salinity, sea-floor maximum temperature and bathymetry. The occurrence probabilities of seven seafloor lithologies (Calcareous sediment, Clay, Diatom ooze, Lithogenous sediment, Mixed calcareous-siliceous ooze, Radiolarian ooze and Siliceous mud) were spatially predicted. The final map shows the most probable seafloor lithology and an associated probability value, which may be viewed as a spatially explicit measure of map confidence. An assessment of the accuracy of the map was based on a test set of observations not used for model training. Overall map accuracy was 69.5% (95% confidence interval: 67.9% - 71.1%). The sea-floor lithology map bears some resemblance with previously published hand-drawn maps in that the distribution of Calcareous sediment, Clay and Diatom ooze are very similar. Clear differences were however also noted: Most strikingly, the map presented here does not display a band of Radiolarian ooze in the equatorial Pacific.</p><p>The probability surfaces of individual seafloor lithologies, the categorical map of the seven mapped lithologies and the associated map confidence will be made freely available. It is hoped that they form a useful basis for research pertaining to deep-sea sediments.</p>

scholarly journals A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks

AI Magazine ◽  
2019 ◽  
Vol 40 (2) ◽  
pp. 31-43 ◽  
Author(s):  
Prithviraj Dasgupta ◽  
Joseph Collins
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Critical Systems ◽  
Open Problems ◽  
Learning Techniques ◽  
Supervised Learning Algorithms ◽  
Game Theoretic ◽  
Using Data

Machine learning techniques are used extensively for automating various cybersecurity tasks. Most of these techniques use supervised learning algorithms that rely on training the algorithm to classify incoming data into categories, using data encountered in the relevant domain. A critical vulnerability of these algorithms is that they are susceptible to adversarial attacks by which a malicious entity called an adversary deliberately alters the training data to misguide the learning algorithm into making classification errors. Adversarial attacks could render the learning algorithm unsuitable for use and leave critical systems vulnerable to cybersecurity attacks. This article provides a detailed survey of the stateof-the-art techniques that are used to make a machine learning algorithm robust against adversarial attacks by using the computational framework of game theory. We also discuss open problems and challenges and possible directions for further research that would make deep machine learning–based systems more robust and reliable for cybersecurity tasks.

scholarly journals Resurrecting $$ b\overline{b}h $$ with kinematic shapes

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Christophe Grojean ◽  
Ayan Paul ◽  
Zhuoni Qian
Keyword(s):  
Machine Learning ◽  
Game Theory ◽  
Higgs Boson ◽  
Learning Algorithm ◽  
Black Box ◽  
Machine Learning Algorithm ◽  
Final State ◽  
Associated Production ◽  
Shapley Values ◽  
Boosted Decision Trees

Abstract The associated production of a $$ b\overline{b} $$ b b ¯ pair with a Higgs boson could provide an important probe to both the size and the phase of the bottom-quark Yukawa coupling, yb. However, the signal is shrouded by several background processes including the irreducible Zh, Z →$$ b\overline{b} $$ b b ¯ background. We show that the analysis of kinematic shapes provides us with a concrete prescription for separating the yb-sensitive production modes from both the irreducible and the QCD-QED backgrounds using the $$ b\overline{b}\gamma \gamma $$ b b ¯ γγ final state. We draw a page from game theory and use Shapley values to make Boosted Decision Trees interpretable in terms of kinematic measurables and provide physics insights into the variances in the kinematic shapes of the different channels that help us complete this feat. Adding interpretability to the machine learning algorithm opens up the black-box and allows us to cherry-pick only those kinematic variables that matter most in the analysis. We resurrect the hope of constraining the size and, possibly, the phase of yb using kinematic shape studies of $$ b\overline{b}h $$ b b ¯ h production with the full HL-LHC data and at FCC-hh.

scholarly journals Dysgraphia detection through machine learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Peter Drotár ◽  
Marek Dobeš
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Written Expression ◽  
Learning Approach ◽  
Adaptive Boosting ◽  
Adaboost Algorithm ◽  
Machine Learning Approach ◽  
Automated Procedures

AbstractDysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.

scholarly journals Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design

2021 ◽  
Vol 12 ◽  
Author(s):  
Keisuke Izumi ◽  
Kazumichi Minato ◽  
Kiko Shiga ◽  
Tatsuki Sugio ◽  
Sayaka Hanashiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rating Scale ◽  
Learning Algorithm ◽  
Electrodermal Activity ◽  
Well Being ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Body Motion ◽  
Study Results ◽  
The Relationship

Introduction: Mental disorders are a leading cause of disability worldwide. Depression has a significant impact in the field of occupational health because it is particularly prevalent during working age. On the other hand, there are a growing number of studies on the relationship between “well-being” and employee productivity. To promote healthy and productive workplaces, this study aims to develop a technique to quantify stress and well-being in a way that does not disturb the workplace.Methods and analysis: This is a single-arm prospective observational study. The target population is adult (>20 years old) workers at companies that often engage in desk work; specifically, a person who sits in front of a computer for at least half their work hours. The following data will be collected: (a) participants' background characteristics; (b) participants' biological data during the 4-week observation period using sensing devices such as a camera built into the computer (pulse wave data extracted from the facial video images), a microphone built into their work computer (voice data), and a wristband-type wearable device (electrodermal activity data, body motion data, and body temperature); (c) stress, well-being, and depression rating scale assessment data. The analysis workflow is as follows: (1) primary analysis, comprised of using software to digitalize participants' vital information; (2) secondary analysis, comprised of examining the relationship between the quantified vital data from (1), stress, well-being, and depression; (3) tertiary analysis, comprised of generating machine learning algorithms to estimate stress, well-being, and degree of depression in relation to each set of vital data as well as multimodal vital data.Discussion: This study will evaluate digital phenotype regarding stress and well-being of white-collar workers over a 4-week period using persistently obtainable biomarkers such as heart rate, acoustic characteristics, body motion, and electrodermal activity. Eventually, this study will lead to the development of a machine learning algorithm to determine people's optimal levels of stress and well-being.Ethics and dissemination: Collected data and study results will be disseminated widely through conference presentations, journal publications, and/or mass media. The summarized results of our overall analysis will be supplied to participants.Registration: UMIN000036814

scholarly journals The Emotional Toll of Inflammatory Bowel Disease: Using Machine Learning to Analyze Online Community Forum Discourse

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Robert Lerrigo ◽  
Johnny T R Coffey ◽  
Joshua L Kravitz ◽  
Priyanka Jadhav ◽  
Azadeh Nikfarjam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Online Community ◽  
Learning Algorithm ◽  
Well Being ◽  
Psychosocial Impacts ◽  
Community Forum ◽  
Inflammatory Bowel ◽  
The Impact

Abstract Background Patients with inflammatory bowel disease are using online community forums (OCFs) to seek emotional support. The impact of OCFs on well-being and their emotional content are unknown. Methods We used an unsupervised machine learning algorithm to identify the thematic content of 51,591 public, online posts from the Crohn’s & Colitis Foundation Community Forum. Results We identified 10,702 (20.8%) posts expressing: gratitude (40%), anxiety/fear (20.8%), empathy (18.2%), anger/frustration (13.4%), hope (13.2%), happiness (10.0%), sadness/depression (5.8%), shame/guilt (2.5%), and/or loneliness (2.5%). A common subtheme was the importance of fostering social support. Conclusions High-throughput, machine learning-directed analysis of OCFs may help identify psychosocial impacts of inflammatory bowel disease on patients and their caregivers.

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