-------- Forwarded Message -------- Subject: [AISWorld] CFP: HICSS-52 Machine Learning, Robotic and Toy Computing Minitrack Date: Wed, 25 Apr 2018 02:16:41 +0000 From: Patrick Hung Patrick.Hung@uoit.ca To: aisworld@lists.aisnet.org aisworld@lists.aisnet.org
Apologies for cross-postings. Please send it to interested colleagues and students. Many Thanks!
Call For Papers
The 52nd Hawaii International Conference on System Sciences (HICSS-52) Grand Wailea, Maui, Hawaii, January 8-11, 2019 ========================================================================== Machine Learning, Robotic and Toy Computing Minitrack
Machine learning employs software tools from advanced analytics disciplines such as data mining, predictive analytics, and text based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures or non-linear transformations. At the same time, the processing and analysis of machine learning applications present methodological and technological challenges. Further machine learning applications are advantaged by a rise in sensing technologies as witnessed in both the number of sensors and the rich diversity of sensors ranging from cell phones, personal computers, and health tracking appliances to Artificial Intelligence (AI) technologies designed to give contextual, semantic data to entities in an ubiquitous environment that previously could not contribute intelligence to key decisions and smart devices. Recently AI technologies have been applied to robotic and toy computing. Robotic computing is one branch of AI technologies, and their synergistic interactions, that enable and are enabled by robots. Robots now can easily capture a user's physical activity state (e.g., walking, standing, running, etc.) and store personalized information (e.g., face, voice, location, activity pattern, etc.) through the camera, microphone, and sensors by AI technologies. Toy computing is a recently developing concept which transcends the traditional toy into a new area of computer research using AI technologies. A toy in this context can be effectively considered a computing device or peripheral called Smart Toys. We invite research and industry papers related to these specific challenges and others that are driving innovation in machine learning, robotic and toy computing. With a general focus on machine learning, robotic and toy computing, this mini-track covers related topics in machine learning, robotic and toy computing such as:
* Data Modeling and Implementation * Analytics and Algorithms * Business Models * Delivery, Deployment, and Maintenance * Real-time Processing Technologies and Online Transactions * Conceptual and Technical Architecture * Visualization Technologies * Modeling and Implementation * Security, Privacy, and Trust * Industry Standards and Solution Stacks * Provenance Tracking Frameworks and Tools * Software Repositories * Organizations Best Practices * Case Studies (e.g., smart toys, healthcare, financial, aviation, etc.)
Extended versions of accepted papers will be invited for submission by specific journal special issue in Electronic Commerce Research and Applications by Elsevier (https://www.journals.elsevier.com/electronic-commerce-research-and-applicati...).
Deadline for authors to submit papers: *** June 15, 2018 (11:59 PM, Hawaii time) ***
Submission link: http://www.hicss.org
Minitrack Co-Chairs:
Patrick C. K. Hung Faculty of Business and Information Technology, University of Ontario Institute of Technology, Canada Department of Electronic Engineering, National Taipei University of Technology, Taiwan patrick.hung@uoit.ca
Shih-Chia Huang Department of Electronic Engineering, National Taipei University of Technology, Taiwan schuang@ntut.edu.tw
Sarajane Marques Peres School of Arts, Sciences and Humanities, University of Sao Paulo, Brazil sarajane@usp.br
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