scholarly journals Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four body interactions

2021 ◽  
Author(s):  
Behnam Parsaeifard ◽  
Stefan Goedecker
Keyword(s):  
Machine Learn

Can a Machine Learn Through Customer Sentiment?: A Cost-Aware Approach to Predict Support Ticket Escalations

IEEE Software ◽  
2019 ◽  
Vol 36 (5) ◽  
pp. 38-45 ◽  
Author(s):  
Colin Werner ◽  
Ze Shi Li ◽  
Daniela Damian
Keyword(s):  
Machine Learn

scholarly journals Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education

2019 ◽  
Vol 9 (3) ◽  
pp. 184 ◽  
Author(s):  
Meng-Leong How ◽  
Wei Loong David Hung
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Thinking Skills ◽  
Supervised Machine Learning ◽  
Controlled Experiments ◽  
Machine Learn ◽  
Science Technology ◽  
Steam Education ◽  
Computer Scientists ◽  
And Mathematics

In science, technology, engineering, arts, and mathematics (STEAM) education, artificial intelligence (AI) analytics are useful as educational scaffolds to educe (draw out) the students’ AI-Thinking skills in the form of AI-assisted human-centric reasoning for the development of knowledge and competencies. This paper demonstrates how STEAM learners, rather than computer scientists, can use AI to predictively simulate how concrete mixture inputs might affect the output of compressive strength under different conditions (e.g., lack of water and/or cement, or different concrete compressive strengths required for art creations). To help STEAM learners envision how AI can assist them in human-centric reasoning, two AI-based approaches will be illustrated: first, a Naïve Bayes approach for supervised machine-learning of the dataset, which assumes no direct relations between the mixture components; and second, a semi-supervised Bayesian approach to machine-learn the same dataset for possible relations between the mixture components. These AI-based approaches enable controlled experiments to be conducted in-silico, where selected parameters could be held constant, while others could be changed to simulate hypothetical “what-if” scenarios. In applying AI to think discursively, AI-Thinking can be educed from the STEAM learners, thereby improving their AI literacy, which in turn enables them to ask better questions to solve problems.

scholarly journals Detection of Malpractice in E-exams by Head Pose and Gaze Estimation

2021 ◽  
Vol 16 (08) ◽  
pp. 47
Author(s):  
Chirag S Indi ◽  
Varun Pritham ◽  
Vasundhara Acharya ◽  
Krishna Prakasha
Keyword(s):  
Online Education ◽  
State Of The Art ◽  
Eye Gaze ◽  
Visual Aids ◽  
Head Pose ◽  
Machine Learn ◽  
Hybrid Classifier ◽  
Use Of Technology ◽  
System Use ◽  
Transmission Quality

Examination malpractice is a deliberate wrong doing contrary to official examina-tion rules designed to place a candidate at unfair advantage or disadvantage. The proposed system depicts a new use of technology to identify malpractice in E-Exams which is essential due to growth of online education. The current solu-tions for such a problem either require complete manual labor or have various vulnerabilities that can be exploited by an examinee. The proposed application en-compasses an end-to-end system that assists an examiner/evaluator in deciding whether a student passes an online exam without any probable attempts of mal-practice or cheating in e-exams with the help of visual aids. The system works by categorizing the student’s VFOA (visual focus of attention) data by capturing the head pose estimates and eye gaze estimates using state-of-the-art machine learn-ing techniques. The system only requires the student (test-taker) to have a func-tioning internet connection along with a webcam to transmit the feed. The exam-iner is alerted when the student wavers in his VFOA, from the screen greater than X, a predefined threshold of times. If this threshold X is crossed, the appli-cation will save the data of the person when his VFOA is off the screen and send it to the examiner to be manually checked and marked whether the action per-formed by the student was an attempt at malpractice or just momentary lapse in concentration. The system use a hybrid classifier approach where two different classifiers are used, one when gaze values are being read successfully (which may fail due to various reasons like transmission quality or glare from his specta-cles), the model falls back to the default classifier which only reads the head pose values to classify the attention metric, which is used to map the student’s VFOA to check the likelihood of malpractice. The model has achieved an accuracy of 96.04 percent in classifying the attention metric.

scholarly journals A Transferable Machine-Learning Model of the Electron Density

2018 ◽  
Author(s):  
Clemence Corminboeuf ◽  
Michele Certiotti ◽  
benjamin meyer ◽  
Alberto Fabrizio ◽  
Andrea Grisafi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Charge Density ◽  
Small Molecules ◽  
Electron Density ◽  
Structure Data ◽  
Learning Model ◽  
Linear Scaling ◽  
Machine Learn ◽  
Machine Learning Model

<p>We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost.</p>

scholarly journals In medicine, how do we machine learn anything real?

Patterns ◽  
2022 ◽  
Vol 3 (1) ◽  
pp. 100392
Author(s):  
Marzyeh Ghassemi ◽  
Elaine Okanyene Nsoesie
Keyword(s):  
Machine Learn

A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap

2020 ◽  
Vol 172 ◽  
pp. 109286 ◽  
Author(s):  
Abhirup Patra ◽  
Rohit Batra ◽  
Anand Chandrasekaran ◽  
Chiho Kim ◽  
Tran Doan Huan ◽  
...  
Keyword(s):  
Information Fusion ◽  
Machine Learn ◽  
Fusion Approach

scholarly journals A Transferable Machine-Learning Model of the Electron Density

2018 ◽  
Author(s):  
Clemence Corminboeuf ◽  
Michele Certiotti ◽  
benjamin meyer ◽  
Alberto Fabrizio ◽  
Andrea Grisafi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Charge Density ◽  
Small Molecules ◽  
Electron Density ◽  
Structure Data ◽  
Learning Model ◽  
Linear Scaling ◽  
Machine Learn ◽  
Machine Learning Model

<p>We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost.</p>

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