scholarly journals Scaling up psychology via Scientific Regret Minimization

2020 ◽  
Vol 117 (16) ◽  
pp. 8825-8835
Author(s):  
Mayank Agrawal ◽  
Joshua C. Peterson ◽  
Thomas L. Griffiths
Keyword(s):  
Model Building ◽  
Traditional Approach ◽  
Large Datasets ◽  
Machine Learning Algorithms ◽  
Regret Minimization ◽  
Moral Decisions ◽  
Systematic Methodology ◽  
Public Collection ◽  
Better Than ◽  
Large Machine

Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets. One traditional approach is to analyze the residuals of models—the biggest errors they make in predicting the data—to discover what might be missing from those models. However, once a dataset is sufficiently large, machine learning algorithms approximate the true underlying function better than the data, suggesting, instead, that the predictions of these data-driven models should be used to guide model building. We call this approach “Scientific Regret Minimization” (SRM), as it focuses on minimizing errors for cases that we know should have been predictable. We apply this exploratory method on a subset of the Moral Machine dataset, a public collection of roughly 40 million moral decisions. Using SRM, we find that incorporating a set of deontological principles that capture dimensions along which groups of agents can vary (e.g., sex and age) improves a computational model of human moral judgment. Furthermore, we are able to identify and independently validate three interesting moral phenomena: criminal dehumanization, age of responsibility, and asymmetric notions of responsibility.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

scholarly journals Why a Virtual Assistant for Moral Enhancement When We Could have a Socrates?

2021 ◽  
Vol 27 (4) ◽  
Author(s):  
Francisco Lara
Keyword(s):  
Artificial Intelligence ◽  
Virtual Reality ◽  
New Technologies ◽  
Personal Autonomy ◽  
Moral Enhancement ◽  
Moral Decisions ◽  
Educational Methodology ◽  
Better Than

AbstractCan Artificial Intelligence (AI) be more effective than human instruction for the moral enhancement of people? The author argues that it only would be if the use of this technology were aimed at increasing the individual's capacity to reflectively decide for themselves, rather than at directly influencing behaviour. To support this, it is shown how a disregard for personal autonomy, in particular, invalidates the main proposals for applying new technologies, both biomedical and AI-based, to moral enhancement. As an alternative to these proposals, this article proposes a virtual assistant that, through dialogue, neutrality and virtual reality technologies, can teach users to make better moral decisions on their own. The author concludes that, as long as certain precautions are taken in its design, such an assistant could do this better than a human instructor adopting the same educational methodology.

scholarly journals Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4618
Author(s):  
Francisco Oliveira ◽  
Miguel Luís ◽  
Susana Sargento
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Real Data ◽  
Emerging Technology ◽  
Machine Learning Algorithms ◽  
Base Stations ◽  
Aerial Vehicle ◽  
Positioning Algorithm ◽  
The Military ◽  
Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

scholarly journals Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

2021 ◽  
Vol 11 (11) ◽  
pp. 4742
Author(s):  
Tianpei Xu ◽  
Ying Ma ◽  
Kangchul Kim
Keyword(s):  
Ensemble Learning ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Prediction System ◽  
Churn Prediction ◽  
Customer Churn ◽  
Feature Grouping ◽  
Recognition Systems ◽  
The Cost ◽  
Better Than

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

scholarly journals Algorithmic and human prediction of success in human collaboration from visual features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Saveski ◽  
Edmond Awad ◽  
Iyad Rahwan ◽  
Manuel Cebrian
Keyword(s):  
Machine Learning ◽  
Visual Cues ◽  
Success Factors ◽  
Group Performance ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Adventure Game ◽  
Group Success ◽  
The Relationship ◽  
Better Than

AbstractAs groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues.

Application of machine learning algorithms to predict permeability in tight sandstone formations

Nafta-Gaz ◽  
2021 ◽  
Vol 77 (5) ◽  
pp. 283-292
Author(s):  
Tomasz Topór ◽  
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Learning Algorithms ◽  
Confining Pressure ◽  
Machine Learning Algorithms ◽  
Core Material ◽  
Confining Stress ◽  
Tight Sandstone ◽  
Oil And Gas Exploration ◽  
Better Than

The application of machine learning algorithms in petroleum geology has opened a new chapter in oil and gas exploration. Machine learning algorithms have been successfully used to predict crucial petrophysical properties when characterizing reservoirs. This study utilizes the concept of machine learning to predict permeability under confining stress conditions for samples from tight sandstone formations. The models were constructed using two machine learning algorithms of varying complexity (multiple linear regression [MLR] and random forests [RF]) and trained on a dataset that combined basic well information, basic petrophysical data, and rock type from a visual inspection of the core material. The RF algorithm underwent feature engineering to increase the number of predictors in the models. In order to check the training models’ robustness, 10-fold cross-validation was performed. The MLR and RF applications demonstrated that both algorithms can accurately predict permeability under constant confining pressure (R2 0.800 vs. 0.834). The RF accuracy was about 3% better than that of the MLR and about 6% better than the linear reference regression (LR) that utilized only porosity. Porosity was the most influential feature of the models’ performance. In the case of RF, the depth was also significant in the permeability predictions, which could be evidence of hidden interactions between the variables of porosity and depth. The local interpretation revealed the common features among outliers. Both the training and testing sets had moderate-low porosity (3–10%) and a lack of fractures. In the test set, calcite or quartz cementation also led to poor permeability predictions. The workflow that utilizes the tidymodels concept will be further applied in more complex examples to predict spatial petrophysical features from seismic attributes using various machine learning algorithms.

scholarly journals TEACHING SERBIAN LEARNERS ENGLISH PHRASAL VERBS: THE TRADITIONAL APPROACH VS. THE COGNITIVE LINGUISTIC APPROACH

2018 ◽  
Vol 8 (8) ◽  
pp. 175
Author(s):  
Srđan M. Gajdoš
Keyword(s):  
Traditional Approach ◽  
Control Group ◽  
Cognitive Approach ◽  
Phrasal Verbs ◽  
Direct Translation ◽  
Cognitive Linguistic ◽  
Approach To Teaching ◽  
Linguistic Approach ◽  
Experimental Group ◽  
Better Than

This study examines the results obtained by using the traditional and the cognitive approach to teaching phrasal verbs. The control group was taught phrasal verbs using the traditional way i.e. by providing a direct translation into Serbian. In the experimental group the author presented the verbs by explaining the meanings of the very particles and the meanings they develop. Both groups were given a test immediately after they received input. They were also tested on the meanings of untaught phrasal verbs three weeks later. Utilising the cognitive approach helped the students learn the phrasal verbs more successfully. The students who knew various meanings of the particles were able to understand the meanings of the whole phrasal verbs better. The experimental group was able to predict the meanings of the untaught phrasal verbs in the delayed test better than the control group.

scholarly journals Unsupervised Outlier Detection in Multidimensional Data

2021 ◽  
Author(s):  
Atiq Rehman ◽  
Samir Brahim Belhaouari
Keyword(s):  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Multidimensional Data ◽  
High Dimensions ◽  
Comprehensive Performance ◽  
Benchmark Datasets ◽  
Distance Vector ◽  
Detection Schemes ◽  
Unsupervised Outlier Detection ◽  
Better Than

Abstract Detection and removal of outliers in a dataset is a fundamental preprocessing task without which the analysis of the data can be misleading. Furthermore, the existence of anomalies in the data can heavily degrade the performance of machine learning algorithms. In order to detect the anomalies in a dataset in an unsupervised manner, some novel statistical techniques are proposed in this paper. The proposed techniques are based on statistical methods considering data compactness and other properties. The newly proposed ideas are found efficient in terms of performance, ease of implementation, and computational complexity. Furthermore, two proposed techniques presented in this paper use only a single dimensional distance vector to detect the outliers, so irrespective of the data’s high dimensions, the techniques remain computationally inexpensive and feasible. Comprehensive performance analysis of the proposed anomaly detection schemes is presented in the paper, and the newly proposed schemes are found better than the state-of-the-art methods when tested on several benchmark datasets.

Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

2022 ◽  
pp. 27-50
Author(s):  
Rajalaxmi Prabhu B. ◽  
Seema S.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Model Building ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

scholarly journals Formation of the Competences of Social Interaction at the Technical University

2018 ◽  
Vol 193 ◽  
pp. 05016
Author(s):  
Nadezhda Miloradova ◽  
Alexander Ishkov
Keyword(s):  
Social Interaction ◽  
Technical Education ◽  
Traditional Approach ◽  
Personal Responsibility ◽  
Federal State ◽  
Educational Standards ◽  
The Social ◽  
State Educational ◽  
Engineering Training ◽  
Better Than

The article is devoted to the matters of the formation of universal competences as the basis of higher technical education taking into account the requirements of the Federal State Educational Standards of Higher Education FSES HE 3+ and FSES HE 3++. The necessity of integration of social and humanitarian disciplines into the process of professional engineering training is proved. The authors describe their work on the development of the training course |Principles of Legislation and Social Interaction in Construction”, which united the disciplines “Psychology”, “Sociology”, “Political science”, “Law”. Authors have suggested to use the concept "Me" as the key element of this course, emphasizing personal responsibility of an individual for the social interaction. The authors present the results of the comparative analyses of the progress of the students, who studied the new course and those, who studied traditional separate disciplines. They prove that the results, gained within the competence approach, are significantly better, than the results, possible in case of the use of the traditional approach.

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