Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Wireless Personal Communications
Published in: Wireless Personal Communications 4/2021

01-02-2021

An Optimal Approach to Enhance Context Aware Description Administration Service for Cloud Robots in a Deep Learning Environment

Authors: R. Subha, Anandakumar Haldorai, Arulmurugan Ramu

Published in: Wireless Personal Communications | Issue 4/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

As the advancements in the field of artificial intelligence technologies continue to grow, robots are being built by the researchers as an attempt to render services to the people. In this regard, the robots can communicate effectively with the people and be able to complete all the tasks adequately given to them. These service robots while being developed requires the dialogue services to be developed to interact effectively with human beings providing far better user experience. Thus, the robot been built can provide domain-specific knowledge as well as able to provide consultations in various domains. We in this paper adopted a service-oriented approach for developing context-aware communication services for the cloud robot. The proposed work aims at training the context-aware model developed. The context-aware model is responsible for answering the questions put forward by the users and possess the ability to exploit the answers corresponding to the questions presented. An integrated framework is used to combine the contextual information and communication services. The performance of the proposed model can be evaluated based on Inverse Rank Mean (IRM). Evolutionary testing methods are used for testing the data in the proposed model. The results thus obtained shows the effectiveness of the proposed approach.
previous article Efficient Resource Management on Cloud Using Energy and Power Aware Dynamic Migration (EPADM) of VMs
next article Improvement of Transmission Control Protocol for High Bandwidth Applications

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Waibel, M., et al. (2011). RoboEarth: A world wide web for robots. IEEE Robotics and Automation Magazine, 18(2), 69–82.CrossRef
2.
Mohanarajah, G., Hunziker, D., D’Andrea, R., & Waibel, M. (2015). Rapyuta: A cloud robotics platform. IEEE Transactions on Automation Science and Engineering, 12(2), 481–493.CrossRef
3.
Remy, S. L., & Blake, M. B. (2010). Distributed service-oriented robotics. IEEE Internet Computing, 15(2), 70–74.CrossRef
4.
Osentoski, S., Jay, G., Crick, C., Pitzer, B., DuHadway, C., & Jenkins, O. C. (2011). Robots as web services: Reproducible experimentation and application development using rosjs. In Proceedings IEEE international conference on robotics and automation (pp. 6078–6083).
5.
Huang, J.-Y., Lee, W.-P., Yang, T.-H., & Ko, C.-S. (2017). Resource sharing for cloud robots: Service reuse and collective map building. In Proceedings of the 18th IEEE international conference on advanced robotics (pp. 303–309).
6.
7.
Feng, M., Xiang, B., Glass, M. R., Wang, L., & Zhou, B. (2015). Applying deep learning to answer selection: A study and an open task. In 2015 IEEE workshop on automatic speech recognition and understanding (ASRU) (pp. 813–820). IEEE.
8.
Hu, B., Lu, Z., Li, H., & Chen, Q. (2014). Convolutional neural network architectures for matching natural language sentences. In Proceedings advance neural information processing system (pp. 2042–2050).
9.
Chen, H., Liu, X., Yin, D., & Tang, J. (2017). A survey on dialogue systems: Recent advances and new frontiers. ACM SIGKDD Explorations Newsletter, 19(2), 25–35.CrossRef
10.
Gao, J., Galley, M., & Li, L. (2018). Neural approaches to conversational AI. In Proceedings of the 41st international ACM SIGIR conference on research & development in information retrieval (pp. 1371–1374).
11.
Sordoni A., et al. (2015). A neural network approach to context-sensitive generation of conversational responses. In Proceedings of the conference on North American chapter of the association for computational linguistics: human language technologies (pp. 196–205).
12.
Ghosh, S., Vinyals, O., Strope, B., Roy, S., Dean, T., & Heck, L. (2016). Contextual LSTM (CLSTM) models for large scale NLP tasks. arXiv preprint arXiv:​1602.​06291.
13.
Arumugam, R., Enti, V. R., Bingbing, L., Xiaojun, W., Baskaran, K., Kong, F. F., et al. (2010). DAvinCi: A cloud computing framework for service robots. In 2010 IEEE international conference on robotics and automation (pp. 3084–3089). IEEE.
14.
Crick, C., Jay, G., Osentoski, S., & Jenkins, O. C. (2012). ROS and rosbridge: Roboticists out of the loop. In Proceedings of the 7th ACM/IEEE International Conference on Human-Robot Interaction (pp. 493–494).
15.
Koubaa, A. (2014). A service-oriented architecture for virtualizing robots in robot-as-a-service clouds. In Proceedings international conference on architecture of computer system (pp. 196–208).
16.
Du, Z., He, L., Chen, Y., Xiao, Y., Gao, P., & Wang, T. (2017). Robot cloud: Bridging the power of robotics and cloud computing. Future Generation Computer Systems, 74, 337–348.CrossRef
17.
Pereira, A. B. M., & Bastos, G. S. (2017). ROSRemote, using ROS on cloud to access robots remotely. In Proceedings of the 18th IEEE international conference advance robotics (pp. 284–289).
18.
Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015). A survey of research on cloud robotics and automation. IEEE Transactions on Automation Science and Engineering, 12(2), 398–409.CrossRef
19.
Saha, O., & Dasgupta, P. (2018). A comprehensive survey of recent trends in cloud robotics architectures and applications. Robotics, 7(3), 47.CrossRef
20.
Wan, J., Tang, S., Yan, H., Li, D., Wang, S., & Vasilakos, A. (2016). Cloud robotics: Current status and open issues. IEEE Access, 4, 2797–2807.CrossRef
21.
Yang, T.-H., & Lee, W.-P. (2016). Intelligent service reconfiguration for home robots. In Proceedings of the advance reconfigurable mechanics robots II (pp. 735–745). Cham: Springer.
22.
Perera, V., et al. (2015). Learning task knowledge from dialog and web access. Robotics, 4(2), 223–252.CrossRef
23.
Thomason, J., Zhang, S., Mooney, R., & Stone, P. (2015). Learning to interpret natural language commands through human-robot dialog. In Proceedings of the 24th international conference on artificial intelligence (pp. 1923–1929).
24.
Kowsigan, M., & Balasubramanie, P. (2019). An efficient performance evaluation model for the resource clusters in cloud environment using continuous time Markov chain and Poisson process. Cluster Computing, 22(5), 12411–12419.CrossRef
25.
Shang, L., Lu, Z., & Li, H. (2015). Neural responding machine for short-text conversation. In Proceedings of the 53rd annual meeting of the association for computational linguistic (Vol. 1, pp. 1577–1586).
26.
Serban, I. V., Sordoni, A., Bengio, Y., Courville, A., & Pineau, J. (2015). Hierarchical neural network generative models for movie dialogues. arXiv preprint arXiv:​1507.​04808, 7(8), 434–441.
27.
Wen, T.-H., Gašić, M., Mrkšić, N., Su, P.-H., Vandyke, D., & Young, S. (2015). Semantically conditioned LSTM-based natural language generation for Spoken dialogue systems. In Proceedings conference on empirical methods in natural language processing (pp. 1711–1721).
28.
Zhou, H., Huang, M., & Zhu, X. (2016). Context-aware natural language generation for spoken dialogue systems. In Proceedings of the 26th international conference on computations linguistics, technical papers (pp. 2032–2041).
29.
Serban, I. V., Sordoni, A., Bengio, Y., Courville, A., & Pineau, J. (2016). Building end-to-end dialogue systems using generative hierarchical neural network models. In Proceedings of the AAAI (pp. 3776–3784).
30.
Xing, C., Wu, Y., Wu, W., Huang, Y., & Zhou, M. (2018). Hierarchical recurrent attention network for response generation. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 5610–5617).
31.
Zhou, H., Huang, M., Zhang, T., Zhu, X., & Liu, B. (2018). Emotional chatting machine: Emotional conversation generation with internal and external memory. In Proceedings of the 32th AAAI conference on artificial intelligence (pp. 730–738).
32.
Sarkar, T., Salauddin, M., KumarHazra, S., & Chakraborty, R. (2020). A novel data science application approach for classification of nutritional composition, instrumental colour, texture and sensory analysis of bael fruit (Aegle marmelos (L) correa). International Journal of Intelligent Networks, 1, 59–66.CrossRef
33.
Zhang, R., Wang, Z., & Mai, D. (2018). Building emotional conversation systems using multi-task Seq2Seq learning. In Proceedings of the natural CCF conference on natural languages processing and Chinese computing (pp. 612–621).
34.
Kalpana Devi, M., & Umamaheswari, K. (2020). Modified Artificial Bee Colony with firefly algorithm based spectrum handoff in cognitive radio network. International Journal of Intelligent Networks, 1, 67–75.CrossRef
35.
Mohan, K., & Palanisamy, B. (2019). A novel resource clustering model to develop an efficient wireless personal cloud environment. Turkish Journal of Electrical Engineering and Computer Sciences, 27(3), 2156–2169.CrossRef
Metadata
Title
An Optimal Approach to Enhance Context Aware Description Administration Service for Cloud Robots in a Deep Learning Environment
Authors
R. Subha
Anandakumar Haldorai
Arulmurugan Ramu
Publication date
01-02-2021
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 4/2021
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-08073-3

Other articles of this Issue 4/2021

Wireless Personal Communications 4/2021 Go to the issue

OriginalPaper

Resource and Cost Aware Glowworm Mapreduce Optimization Based Big Data Processing in Geo Distributed Data Center

OriginalPaper

Optimized Pipelined Fast Fourier Transform Using Split and Merge Parallel Processing Units for OFDM

Editorial

Big Cloud Innovation for Sustainable Wireless Management

OriginalPaper

System Level Protection Against Side-Channel Attack Using High Performance Virtual Secure Circuit for Cryptographic Processor

OriginalPaper

An Efficient Public Auditing Scheme for Cloud Storage with Secure Access Control and Resistance Against DOS Attack by Iniquitous TPA

OriginalPaper

Weighted Ternary Tree Approach for Secure Group Communication Among Mobile Applications