scholarly journals Automatic Web Service Tagging Using Machine Learning and WordNet Synsets

2011 ◽  
pp. 46-59 ◽  
Author(s):  
Zeina Azmeh ◽  
Jean-Rémy Falleri ◽  
Marianne Huchard ◽  
Chouki Tibermacine
Keyword(s):  
Machine Learning ◽  
Web Service

scholarly journals Web Service for Point Cloud Supported Robot Programming Using Machine Learning

2022 ◽  
pp. 253-262
Author(s):  
Kristina Enes
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Web Service ◽  
Production System ◽  
Production Line ◽  
Robot Programming ◽  
Programming Skills ◽  
Image Processing Algorithms ◽  
Processing Algorithms

AbstractIn industrial automation, the use of robots is already standard. But there is still a lot of room for further automation. One such place where improvements can be made is in the adjustment of a production system to new and unknown products. Currently, this task includes the reprogramming of the robot and a readjustment of the image processing algorithms if sensors are involved. This takes time, effort, and a specialist, something especially small and middle-sized companies shy away from. We propose to represent a physical production line with a digital twin, using the simulated production system to generate labeled data to be used for training in a deep learning component. An artificial neural network will be trained to both recognize and localize the observed products. This allows the production line to handle both known and unknown products more flexible. The deep learning component itself is located in a cloud and can be accessed through a web service, allowing any member of the staff to initiate the training, regardless of their programming skills. In summary, our approach addresses not only further automation in manufacturing but also the use of synthesized data for deep learning.

scholarly journals Qualitative: Python Tool for MT Quality Estimation Supporting Server Mode and Hybrid MT

2016 ◽  
Vol 106 (1) ◽  
pp. 147-158 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Web Service ◽  
State Of The Art ◽  
Training Process ◽  
Quality Estimation ◽  
Rule Based ◽  
Efficient Communication ◽  
Service Mode ◽  
Online Web

Abstract We are presenting the development contributions of the last two years to our Python opensource Quality Estimation tool, a tool that can function in both experiment-mode and online web-service mode. The latest version provides a new MT interface, which communicates with SMT and rule-based translation engines and supports on-the-fly sentence selection. Additionally, we present an improved Machine Learning interface allowing more efficient communication with several state-of-the-art toolkits. Additions also include a more informative training process, a Python re-implementation of QuEst baseline features, a new LM toolkit integration, an additional PCFG parser and alignments of syntactic nodes.

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