scholarly journals Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning

2021 ◽  
Vol 11 (9) ◽  
pp. 4143
Author(s):  
Wenzheng Ying ◽  
Wenchi Shou ◽  
Jun Wang ◽  
Weixiang Shi ◽  
Yanhui Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Initial Step ◽  
Video Camera ◽  
Functional Method ◽  
Activity Analysis ◽  
Skeleton Extraction ◽  
High Importance ◽  
Machine Learning Classifiers ◽  
Productivity Decline

Scaffolding serves as one construction trade with high importance. However, scaffolding suffers from low productivity and high cost in Australia. Activity Analysis is a continuous procedure of assessing and improving the amount of time that craft workers spend on one single construction trade, which is a functional method for monitoring onsite operation and analyzing conditions causing delays or productivity decline. Workface assessment is an initial step for activity analysis to manually record the time that workers spend on each activity category. This paper proposes a method of automatic scaffolding workface assessment using a 2D video camera to capture scaffolding activities and the model of key joints and skeleton extraction, as well as machine learning classifiers, were used for activity classification. Additionally, a case study was conducted and showed that the proposed method is a feasible and practical way for automatic scaffolding workface assessment.

scholarly journals Like trainer, like bot? Inheritance of bias in algorithmic content moderation

2017 ◽  
Author(s):  
Reuben Binns ◽  
Michael Veale ◽  
Max Van Kleek ◽  
Nigel Shadbolt
Keyword(s):  
Machine Learning ◽  
Training Data ◽  
The Internet ◽  
Men And Women ◽  
Machine Learning Classifiers ◽  
Ethical Choices ◽  
Test Sets ◽  
Networked Publics ◽  
Important Objective

The internet has become a central medium through which 'networked publics' express their opinions and engage in debate. Offensive comments and personal attacks can inhibit participation in these spaces. Automated content moderation aims to overcome this problem using machine learning classifiers trained on large corpora of texts manually annotated for offence. While such systems could help encourage more civil debate, they must navigate inherently normatively contestable boundaries, and are subject to the idiosyncratic norms of the human raters who provide the training data. An important objective for platforms implementing such measures might be to ensure that they are not unduly biased towards or against particular norms of offence. This paper provides some exploratory methods by which the normative biases of algorithmic content moderation systems can be measured, by way of a case study using an existing dataset of comments labelled for offence. We train classifiers on comments labelled by different demographic subsets (men and women) to understand how differences in conceptions of offence between these groups might affect the performance of the resulting models on various test sets. We conclude by discussing some of the ethical choices facing the implementers of algorithmic moderation systems, given various desired levels of diversity of viewpoints amongst discussion participants.

scholarly journals Towards Benthic Habitat 3D Mapping Using Machine Learning Algorithms and Structures from Motion Photogrammetry

2020 ◽  
Vol 12 (1) ◽  
pp. 127 ◽  
Author(s):  
Hassan Mohamed ◽  
Kazuo Nadaoka ◽  
Takashi Nakamura
Keyword(s):  
Machine Learning ◽  
Video Camera ◽  
Support Vector ◽  
Benthic Habitat ◽  
3D Mapping ◽  
Benthic Habitats ◽  
Significant Information ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

The accurate classification and 3D mapping of benthic habitats in coastal ecosystems are vital for developing management strategies for these valuable shallow water environments. However, both automatic and semiautomatic approaches for deriving ecologically significant information from a towed video camera system are quite limited. In the current study, we demonstrate a semiautomated framework for high-resolution benthic habitat classification and 3D mapping using Structure from Motion and Multi View Stereo (SfM-MVS) algorithms and automated machine learning classifiers. The semiautomatic classification of benthic habitats was performed using several attributes extracted automatically from labeled examples by a human annotator using raw towed video camera image data. The Bagging of Features (BOF), Hue Saturation Value (HSV), and Gray Level Co-occurrence Matrix (GLCM) methods were used to extract these attributes from 3000 images. Three machine learning classifiers (k-nearest neighbor (k-NN), support vector machine (SVM), and bagging (BAG)) were trained by using these attributes, and their outputs were assembled by the fuzzy majority voting (FMV) algorithm. The correctly classified benthic habitat images were then geo-referenced using a differential global positioning system (DGPS). Finally, SfM-MVS techniques used the resulting classified geo-referenced images to produce high spatial resolution digital terrain models and orthophoto mosaics for each category. The framework was tested for the identification and 3D mapping of seven habitats in a portion of the Shiraho area in Japan. These seven habitats were corals (Acropora and Porites), blue corals (H. coerulea), brown algae, blue algae, soft sand, hard sediments (pebble, cobble, and boulders), and seagrass. Using the FMV algorithm, we achieved an overall accuracy of 93.5% in the semiautomatic classification of the seven habitats.

Role of Feature Engineering and Classifier Selection for Machine Learning Predictions

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Makarand Velankar ◽  
Vaibhav Khatavkar ◽  
Vinayak Jagtap ◽  
Parag Kulkarni
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Prediction Accuracy ◽  
Feature Engineering ◽  
Machine Learning Classifiers ◽  
Classifier Selection ◽  
Learning Classifiers ◽  
Proposed Model

Features play a crucial role in several computational tasks. Feature values are input to machine learning algorithms for the prediction. The prediction accuracy depends on various factors such as selection of dataset, features and machine learning classifiers. Various feature selection and reduction approaches are experimented with to obtain better accuracies and reduce the computational overheads. Feature engineering is designing new features suitable for a specific task with the help of domain knowledge. The challenges in feature engineering are presented for the computational music domain as a case study. The experiments are performed with different combinations of feature sets and machine learning classifiers to test the accuracy of the proposed model. Music emotion recognition is used as a case study for the experimentation. Experimental results for the task of music emotion recognition provide insights into the role of features and classifiers in prediction accuracy. Different machine learning classifiers provided varied results, and the choice of a classifier is also an important decision to be made in the proposed model. The engineered features designed with the help of domain experts improved the results. It emphasizes the need for feature engineering for different domains for prediction accuracy improvement. Approaches to design an optimized model with the appropriate feature set and classifier for machine learning tasks are presented.

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