What if Social Robots Look for Productive Engagement?

AbstractIn educational HRI, it is generally believed that a robots behavior has a direct effect on the engagement of a user with the robot, the task at hand and also their partner in case of a collaborative activity. Increasing this engagement is then held responsible for increased learning and productivity. The state of the art usually investigates the relationship between the behaviors of the robot and the engagement state of the user while assuming a linear relationship between engagement and the end goal: learning. However, is it correct to assume that to maximise learning, one needs to maximise engagement? Furthermore, conventional supervised models of engagement require human annotators to get labels. This is not only laborious but also introduces further subjectivity in an already subjective construct of engagement. Can we have machine-learning models for engagement detection where annotations do not rely on human annotators? Looking deeper at the behavioral patterns and the learning outcomes and a performance metric in a multi-modal data set collected in an educational human–human–robot setup with 68 students, we observe a hidden link that we term as Productive Engagement. We theorize a robot incorporating this knowledge will (1) distinguish teams based on engagement that is conducive of learning; and (2) adopt behaviors that eventually lead the users to increased learning by means of being productively engaged. Furthermore, this seminal link paves way for machine-learning models in educational HRI with automatic labelling based on the data.

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Machine Learning in Futures Markets

Journal of Risk and Financial Management ◽

10.3390/jrfm14030119 ◽

2021 ◽

Vol 14 (3) ◽

pp. 119

Author(s):

Fabian Waldow ◽

Matthias Schnaubelt ◽

Christopher Krauss ◽

Thomas Günter Fischer

Keyword(s):

Machine Learning ◽

Futures Markets ◽

Learning Models ◽

Cross Sectional ◽

Data Set ◽

Statistical Arbitrage ◽

Out Of Sample ◽

Sample Testing ◽

Arbitrage Strategy ◽

Machine Learning Models

In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

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High performance logistic regression for privacy-preserving genome analysis

BMC Medical Genomics ◽

10.1186/s12920-020-00869-9 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Martine De Cock ◽

Rafael Dowsley ◽

Anderson C. A. Nascimento ◽

Davis Railsback ◽

Jianwei Shen ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Genome Analysis ◽

Local Area Network ◽

Local Area ◽

Activation Function ◽

Area Network ◽

Learning Models ◽

Data Set ◽

Machine Learning Models

Abstract Background In biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography. When collaboratively training machine learning models with the cryptographic technique named secure multi-party computation, the price paid for keeping the data of the owners private is an increase in computational cost and runtime. A careful choice of machine learning techniques, algorithmic and implementation optimizations are a necessity to enable practical secure machine learning over distributed data sets. Such optimizations can be tailored to the kind of data and Machine Learning problem at hand. Methods Our setup involves secure two-party computation protocols, along with a trusted initializer that distributes correlated randomness to the two computing parties. We use a gradient descent based algorithm for training a logistic regression like model with a clipped ReLu activation function, and we break down the algorithm into corresponding cryptographic protocols. Our main contributions are a new protocol for computing the activation function that requires neither secure comparison protocols nor Yao’s garbled circuits, and a series of cryptographic engineering optimizations to improve the performance. Results For our largest gene expression data set, we train a model that requires over 7 billion secure multiplications; the training completes in about 26.90 s in a local area network. The implementation in this work is a further optimized version of the implementation with which we won first place in Track 4 of the iDASH 2019 secure genome analysis competition. Conclusions In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure multi-party computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.

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Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis

10.20944/preprints202103.0284.v2 ◽

2021 ◽

Author(s):

Maicon Herverton Lino Ferreira da Silva Barros ◽

Geovanne Oliveira Alves ◽

Lubnnia Morais Florêncio Souza ◽

Élisson da Silva Rocha ◽

João Fausto Lorenzato de Oliveira ◽

...

Keyword(s):

Machine Learning ◽

Clinical Symptoms ◽

Treatment Decision ◽

Gradient Boosting ◽

Original Form ◽

Learning Models ◽

Data Set ◽

Risk Of Death ◽

Increased Risk ◽

Machine Learning Models

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1,139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-layer Perceptron (MLP) models is the best model to predict the cure class.

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Learning to Identify At-Risk Students in Distance Education Using Interaction Counts

Revista de Informática Teórica e Aplicada ◽

10.22456/2175-2745.62211 ◽

2016 ◽

Vol 23 (2) ◽

pp. 124 ◽

Cited By ~ 2

Author(s):

Douglas Detoni ◽

Cristian Cechinel ◽

Ricardo Araujo Matsumura ◽

Daniela Francisco Brauner

Keyword(s):

Machine Learning ◽

At Risk ◽

At Risk Students ◽

Drop Out ◽

Support Vector ◽

Learning Models ◽

Data Set ◽

Student Dropout ◽

Vector Machines ◽

Machine Learning Models

Student dropout is one of the main problems faced by distance learning courses. One of the major challenges for researchers is to develop methods to predict the behavior of students so that teachers and tutors are able to identify at-risk students as early as possible and provide assistance before they drop out or fail in their courses. Machine Learning models have been used to predict or classify students in these settings. However, while these models have shown promising results in several settings, they usually attain these results using attributes that are not immediately transferable to other courses or platforms. In this paper, we provide a methodology to classify students using only interaction counts from each student. We evaluate this methodology on a data set from two majors based on the Moodle platform. We run experiments consisting of training and evaluating three machine learning models (Support Vector Machines, Naive Bayes and Adaboost decision trees) under different scenarios. We provide evidences that patterns from interaction counts can provide useful information for classifying at-risk students. This classification allows the customization of the activities presented to at-risk students (automatically or through tutors) as an attempt to avoid students drop out.

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A publicly available crystallisation data set and its application in machine learning

CrystEngComm ◽

10.1039/c7ce00738h ◽

2017 ◽

Vol 19 (27) ◽

pp. 3737-3745 ◽

Cited By ~ 8

Author(s):

Max Pillong ◽

Corinne Marx ◽

Philippe Piechon ◽

Jerome G. P. Wicker ◽

Richard I. Cooper ◽

...

Keyword(s):

Machine Learning ◽

Learning Models ◽

Data Set ◽

Machine Learning Models

A publicly available crystallisation database for clusters of highly similar compounds is used to build machine learning models.

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On the Influence of Contextual Features for the Identification of Complex Words

International Journal of Semantic Computing ◽

10.1142/s1793351x17400207 ◽

2017 ◽

Vol 11 (04) ◽

pp. 497-511

Author(s):

Elnaz Davoodi ◽

Leila Kosseim ◽

Matthew Mongrain

Keyword(s):

Machine Learning ◽

Natural Language ◽

Target Word ◽

Supervised Machine Learning ◽

Learning Models ◽

Data Set ◽

Contextual Features ◽

Complex Words ◽

Machine Learning Models

This paper evaluates the effect of the context of a target word on the identification of complex words in natural language texts. The approach automatically tags words as either complex or not, based on two sets of features: base features that only pertain to the target word, and contextual features that take the context of the target word into account. We experimented with several supervised machine learning models, and trained and tested the approach with the 2016 SemEval Word Complexity Data Set. Results show that when discriminating base features are used, the words around the target word can supplement those features and improve the recognition of complex words.

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Applying Machine Learning Techniques to Predict the Properties of Energetic Materials

10.26434/chemrxiv.5883157.v2 ◽

2018 ◽

Cited By ~ 1

Author(s):

Daniel Elton ◽

Zois Boukouvalas ◽

Mark S. Butrico ◽

Mark D. Fuge ◽

Peter W. Chung

Keyword(s):

Machine Learning ◽

Energetic Materials ◽

Molecular Structures ◽

Machine Learning Techniques ◽

Small Data ◽

Detonation Pressure ◽

Learning Models ◽

Data Set ◽

Learning Techniques ◽

Machine Learning Models

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, bag of bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with 309 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

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Effectiveness, Explainability and Reliability of Machine Meta-Learning Methods for Predicting Mortality in Patients with COVID-19: Results of the Brazilian COVID-19 Registry

10.1101/2021.11.01.21265527 ◽

2021 ◽

Author(s):

Bruno Barbosa Miranda de Paiva ◽

Polianna Delfino Pereira ◽

Claudio Moises Valiense de Andrade ◽

Virginia Mara Reis Gomes ◽

Maria Clara Pontello Barbosa Lima ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

State Of The Art ◽

Laboratory Data ◽

Machine Learning Algorithms ◽

Training Data ◽

Learning Models ◽

Learning Methods ◽

Meta Learning ◽

Machine Learning Models

Objective: To provide a thorough comparative study among state ofthe art machine learning methods and statistical methods for determining in-hospital mortality in COVID 19 patients using data upon hospital admission; to study the reliability of the predictions of the most effective methods by correlating the probability of the outcome and the accuracy of the methods; to investigate how explainable are the predictions produced by the most effective methods. Materials and Methods: De-identified data were obtained from COVID 19 positive patients in 36 participating hospitals, from March 1 to September 30, 2020. Demographic, comorbidity, clinical presentation and laboratory data were used as training data to develop COVID 19 mortality prediction models. Multiple machine learning and traditional statistics models were trained on this prediction task using a folded cross validation procedure, from which we assessed performance and interpretability metrics. Results: The Stacking of machine learning models improved over the previous state of the art results by more than 26% in predicting the class of interest (death), achieving 87.1% of AUROC and macroF1 of 73.9%. We also show that some machine learning models can be very interpretable and reliable, yielding more accurate predictions while providing a good explanation for the why. Conclusion: The best results were obtained using the meta learning ensemble model Stacking. State of the art explainability techniques such as SHAP values can be used to draw useful insights into the patterns learned by machine-learning algorithms. Machine learning models can be more explainable than traditional statistics models while also yielding highly reliable predictions. Key words: COVID-19; prognosis; prediction model; machine learning

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The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data

SOIL ◽

10.5194/soil-6-565-2020 ◽

2020 ◽

Vol 6 (2) ◽

pp. 565-578

Author(s):

Wartini Ng ◽

Budiman Minasny ◽

Wanderson de Sousa Mendes ◽

José Alexandre Melo Demattê

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Soil Properties ◽

Sample Size ◽

Training Sample ◽

Calibration Data ◽

Learning Models ◽

Data Set ◽

Calibration Data Set ◽

Machine Learning Models

Abstract. The number of samples used in the calibration data set affects the quality of the generated predictive models using visible, near and shortwave infrared (VIS–NIR–SWIR) spectroscopy for soil attributes. Recently, the convolutional neural network (CNN) has been regarded as a highly accurate model for predicting soil properties on a large database. However, it has not yet been ascertained how large the sample size should be for CNN model to be effective. This paper investigates the effect of the training sample size on the accuracy of deep learning and machine learning models. It aims at providing an estimate of how many calibration samples are needed to improve the model performance of soil properties predictions with CNN as compared to conventional machine learning models. In addition, this paper also looks at a way to interpret the CNN models, which are commonly labelled as a black box. It is hypothesised that the performance of machine learning models will increase with an increasing number of training samples, but it will plateau when it reaches a certain number, while the performance of CNN will keep improving. The performances of two machine learning models (partial least squares regression – PLSR; Cubist) are compared against the CNN model. A VIS–NIR–SWIR spectra library from Brazil, containing 4251 unique sites with averages of two to three samples per depth (a total of 12 044 samples), was divided into calibration (3188 sites) and validation (1063 sites) sets. A subset of the calibration data set was then created to represent a smaller calibration data set ranging from 125, 300, 500, 1000, 1500, 2000, 2500 and 2700 unique sites, which is equivalent to a sample size of approximately 350, 840, 1400, 2800, 4200, 5600, 7000 and 7650. All three models (PLSR, Cubist and CNN) were generated for each sample size of the unique sites for the prediction of five different soil properties, i.e. cation exchange capacity, organic carbon, sand, silt and clay content. These calibration subset sampling processes and modelling were repeated 10 times to provide a better representation of the model performances. Learning curves showed that the accuracy increased with an increasing number of training samples. At a lower number of samples (< 1000), PLSR and Cubist performed better than CNN. The performance of CNN outweighed the PLSR and Cubist model at a sample size of 1500 and 1800, respectively. It can be recommended that deep learning is most efficient for spectra modelling for sample sizes above 2000. The accuracy of the PLSR and Cubist model seems to reach a plateau above sample sizes of 4200 and 5000, respectively, while the accuracy of CNN has not plateaued. A sensitivity analysis of the CNN model demonstrated its ability to determine important wavelengths region that affected the predictions of various soil attributes.

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