scholarly journals Meet Your Robotic Coworkers

2019 ◽  
Vol 141 (08) ◽  
pp. 32-37
Author(s):  
Carlos M. González
Keyword(s):  
Machine Learning ◽  
Hot Spots ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Metal Loss ◽  
Special Report ◽  
Inspection Robots ◽  
The Relationship

Engineers are already using inspection robots to identify hot spots on refinery exhausts, check boilers for metal loss and corrosion, and examine machinery without disassembling it. Yet, despite their sophisticated sensors and machine learning algorithms, few can do anything about a problem once they spot it. It still takes a human technician to maintain or repair a structure. This is beginning to change. A handful of inspection robots have begun to evolve into robots that can do maintenance tasks and even make the occasional repair on the spot. This special report delves deeper into how this will change the relationship between robots and workers.

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.

scholarly journals Optimized Classification Predictions with a New Index Combining Machine Learning Algorithms

2018 ◽  
Vol 27 (03) ◽  
pp. 1850012 ◽  
Author(s):  
Androniki Tamvakis ◽  
Christos-Nikolaos Anagnostopoulos ◽  
George Tsirtsis ◽  
Antonios D. Niros ◽  
Sofie Spatharis
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Individual Performance ◽  
Optimal Combination ◽  
Machine Learning Algorithms ◽  
Combined Effect ◽  
Different Types ◽  
Open Issue ◽  
The Relationship ◽  
Selection Of

Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search.

scholarly journals EEG correlation at a distance: A re-analysis of two studies using a machine learning approach

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 43 ◽  
Author(s):  
Marco Bilucaglia ◽  
Luciano Pederzoli ◽  
William Giroldini ◽  
Elena Prati ◽  
Patrizio Tressoldi
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Eeg Activity ◽  
Linear Discriminant ◽  
Machine Learning Approach ◽  
Sensorial Stimulation ◽  
The Relationship ◽  
Electroencephalogram Eeg ◽  
Linear Discriminant Classifier

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration. The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz. Methods and Results: Applying a ‘linear discriminant classifier’ to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner. In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner. Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional “mental telecommunications” between physically and sensorially separated participants.

scholarly journals Spatial Simulation Modeling of Settlement Distribution Driven by Random Forest: Consideration of Landscape Visibility

2020 ◽  
Vol 12 (11) ◽  
pp. 4748
Author(s):  
Minrui Zheng ◽  
Wenwu Tang ◽  
Akinwumi Ogundiran ◽  
Jianxin Yang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Simulation Modeling ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Simulation Models ◽  
Machine Learning Algorithms ◽  
Spatial Simulation ◽  
Study Region ◽  
The Relationship

Settlement models help to understand the social–ecological functioning of landscape and associated land use and land cover change. One of the issues of settlement modeling is that models are typically used to explore the relationship between settlement locations and associated influential factors (e.g., slope and aspect). However, few studies in settlement modeling adopted landscape visibility analysis. Landscape visibility provides useful information for understanding human decision-making associated with the establishment of settlements. In the past years, machine learning algorithms have demonstrated their capabilities in improving the performance of the settlement modeling and particularly capturing the nonlinear relationship between settlement locations and their drivers. However, simulation models using machine learning algorithms in settlement modeling are still not well studied. Moreover, overfitting issues and optimization of model parameters are major challenges for most machine learning algorithms. Therefore, in this study, we sought to pursue two research objectives. First, we aimed to evaluate the contribution of viewsheds and landscape visibility to the simulation modeling of - settlement locations. The second objective is to examine the performance of the machine learning algorithm-based simulation models for settlement location studies. Our study region is located in the metropolitan area of Oyo Empire, Nigeria, West Africa, ca. AD 1570–1830, and its pre-Imperial antecedents, ca. AD 1360–1570. We developed an event-driven spatial simulation model enabled by random forest algorithm to represent dynamics in settlement systems in our study region. Experimental results demonstrate that viewsheds and landscape visibility may offer more insights into unveiling the underlying mechanism that drives settlement locations. Random forest algorithm, as a machine learning algorithm, provide solid support for establishing the relationship between settlement occurrences and their drivers.

scholarly journals EEG correlation at a distance: A re-analysis of two studies using a machine learning approach

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 43
Author(s):  
Marco Bilucaglia ◽  
Luciano Pederzoli ◽  
William Giroldini ◽  
Elena Prati ◽  
Patrizio Tressoldi
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Eeg Activity ◽  
Linear Discriminant ◽  
Machine Learning Approach ◽  
Sensorial Stimulation ◽  
The Relationship ◽  
Electroencephalogram Eeg ◽  
Linear Discriminant Classifier

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration. The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz. Methods and Results: Applying a ‘linear discriminant classifier’ to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner. In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner. Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional “mental telecommunications” between physically and sensorially separated participants.

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