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Natural Language Processing For Human Computer Interaction

Natural Language Processing For Human Computer Interaction

Journal
Impact Score 5.94

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Special Issue Information

Submission Deadline: 30-10-2021
Journal Impact Score: 5.94
Journal Name: Computers & Electrical Engineering
Publisher: Computers & Electrical Engineering

Special Issue Call for Papers


Overview



Natural Language Processing (NLP) is a scientific discipline that aids computers to understand human languages seamlessly. The ultimate objective of the NLP techniques is to extract meaningful information from human languages. On the other hand, Human-Computer Interaction (HCI) is a multidisciplinary research area that works on the principles of computer science, cognitive science, and human factors engineering. The prime focus of HCI is on how humans and computers interact with each other. A good interface becomes a crucial part of our day-to-day activities, whereas a poorly designed HCI results in unexpected problems.



Over the past decades, the NLP techniques have had a wide range of applications towards HCI, both from the theoretical as well as the practical perspectives. NLP simplifies the process of human-machine interaction and enhances user experiences in a qualitative way. However, with technological innovations, computers have become more affordable and accessible among a wide variety of users, which creates the requirement of user-friendly and robust interfaces regardless of the user-expertise and nature of the human language. This is because it is often difficult to process information using NLP, which makes it harder for the computer to understand. This ultimately results in the complicated use of machine interfaces such as windows, icons, and menus. In such cases, bringing in advances to NLP to simplify HCI processes forms the ideal solution. Thus, we can conclude that NLP is a vital tool for the support of HCI in future, but there exist numerous challenges (context setting, semantic extraction, etc.) which have to be addressed. It is envisioned that exploring more in this context will help the computer interfaces efficiently process NLP and proactively complete human tasks without much of their involvement.



 



The aim of this special section is to offers a unique platform to gather novel and innovative research works from the fields of NLP and HCI. It aims to discover the fundamental progress of NLP in HCI along with further exploring future directions of research in HCI using NLP.



 



Topics:



- Smart analysis of sentiments using NLP for HCI



- NLP based innovative interaction techniques for HCI



- Advances in intelligent user interfaces for HCI using NLP techniques



- Mixed and augmented reality for HCI



- Design paradigms, metaphors, & use cases of NLP for HCI



- Advances in speech recognition and synthesis for HCI



- Innovative methods of information extraction using NLP for HCI



- Advances in semantic processing for HCI



- Trends in ontology for the efficient design of HCI systems



- Innovation machine translation algorithms for HCI



- Advances in machine learning for NLP and HCI



- NLP for computer mediated interaction in HCI systems



Submission Guidelines:



New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time grame of the special issue.



Before submission, authors should carefully read the Guide for Authors available at



https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors



Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-hci2” under the “Issues” tab.



For additional questions, contact the Main Guest Editor.



 



Schedule



Paper Submission Deadline: October 30, 2021



Author Notification: December 18, 2021



Revised Papers Submission: February 25, 2022



Final Acceptance: April 20, 2022



Publication: Sep. 2022



 



Guest Editors:



Dr. Carlos Enrique Montenegro Marin (Managing Guest Editor)



District University Francisco José de Caldas,



Bogotá, Colombia



Email: [email protected]



Google Scholar:https://scholar.google.com/citations?user=ejJHY40AAAAJ&hl=es



Research Gate: https://www.researchgate.net/profile/Carlos_Marin4



Dr. Carlos Enrique Montenegro Marin received the Diploma of Advanced Studies degree from the Pontifical University of Salamanca, in 2008, the M.Sc. degree in Information and Communication Systems from the Universidad Distrital Francisco José de Caldas, and the Ph.D. degree in Systems and Computer Services for the Internet from the University of Oviedo, Asturias, Spain, in 2012. He was classified with the highest recognition of research by Colciencias in 2017 (Senior Researcher). He is the director of the GIIRA research group of the University District, a group that also received the highest recognition by Colciencias. He is currently a Systems Engineer. His skills and expertise are in the areas of Java Programming, Cloud Computing, Web Development, Object-Oriented Programming, Grid Computing, LMS, Virtualization, Software Engineering, and Linux Administration.



Dr. Xuyun Zhang



Senior Lecturer, Department of Computing



Macquarie University, Australia



Email: [email protected]



Google Scholar: https://scholar.google.com/citations?user=wbF6HL8AAAAJ&hl=en



Research Gate: https://researchers.mq.edu.au/en/persons/xuyun-zhang



Dr. Xuyun Zhang is currently working as a senior lecturer in Department of Computing at Macquarie University in Australia. He worked as a lecturer in The University of Auckland during 2016 - 2019 and a postdoc researcher in NICTA (National ICT Australia, now Data61, CSIRO) during 2014 - 2016. He got his PhD degree in Computer Science and Technology from University of Technology Sydney (UTS), Australia in 2014, and his Master’s and Bachelor’s degrees in the same major from Nanjing University, China in 2011 and 2008. He is an early/mid-career researcher with an international track record of research in areas including scalable and secure machine learning, big data privacy and cyber security, big data mining and analytics, cloud/edge/service computing and IoT, etc.



Dr. Nallappan Gunasekaran



Department of Mathematical Sciences,



Shibaura Institute of Technology, Saitama 337-8570, Japan.



Email: [email protected]



Google Scholar: https://scholar.google.com/citations?user=4MhMsUkAAAAJ&hl=en



Research Gate: https://www.researchgate.net/scientific-contributions/Nallappan-Gunasekaran-2161197345



Dr. Nallappan Gunasekaran received his Ph.D. degree in mathematics from Thiruvalluvar University, Vellore, India, in 2017. He completed his B.Sc. degree from the Mahendra Arts and Science College, Namakkal, Affiliated to Periyar University, Salem, India, in 2009, the master’s degree in mathematics from the Jamal Mohamed College, Affiliated to Bharathidasan University, Trichy, India, in 2012, and the Master of Philosophy degree in mathematics (cryptography) from Bharathidasan University, in 2013. He was a Junior Research Fellow with the Department of Science and Technology-Science and Engineering Research Board (DST-SERB), Government of India, New Delhi, India. He was also a Postdoctoral Research Fellow with the Research Center for Wind Energy Systems, Kunsan National University, Gunsan, South Korea, from May 2017 to October 2018. He is currently a Postdoctoral Research Fellow with the Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama, Japan. He has authored or coauthored of more than 30 research articles in various SCI journals. His research interests include complex-valued neural networks, complex dynamical networks, control theory, stability analysis, sampled-data control, multiagent systems, T-S fuzzy theory, cryptography, and so on. He serves as a Reviewer for various SCI journals.

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