RAIBS 2016 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 2
Title:

A Fuzzy Modelling Approach of Emotion for Affective Computing Systems
Authors:

Charalampos Karyotis, Faiyaz Doctor, Rahat Iqbal, Anne James and Victor Chang
Abstract: In this paper we present a novel affective modelling approach to be utilised by Affective Computing systems. This approach is a combination of the well known Arousal Valence model of emotion and the newly introduced Affective Trajectories Hypothesis. An adaptive data driven fuzzy method is proposed in order to extract personalized emotion models, and successfully visualise the associations of these models’ basic elements, to different emotional labels, using easily interpretable fuzzy rules. Namely we explore how the combinations of arousal, valence, prediction of the future, and the experienced outcome after this prediction, enable us to differentiate between different emotional labels. We use the results obtained from a user study consisting of an online survey, to demonstrate the potential applicability of this affective modelling approach, and test the effectiveness and stability of its adaptive element, which accounts for individual differences between the users. We also propose a basic architecture in order for this approach to be used effectively by AC systems, and finally we present an implementation of a personalised learning system which utilises the suggested framework. This implementation is tested through a pilot experimental session consisting of a tutorial on fuzzy logic which was conducted under an activity-led and problem based learning context.
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Paper Nr: 5
Title:

Big Data Services Security and Security Challenges in Cloud Environment
Authors:

Raed Alsufyani, Khursand Jama, Yulin Yao, Muthu Ramachandran and Victor Chang
Abstract: This paper explores security issues of storage in the cloud and the methodologies that can be used to improve the security level. This study is concluded with a discussion of current problems and the future direction of cloud computing. Big data analysis can also be classified into memory level analysis, business intelligence (BI) level analysis, and massive level analysis. This research paper is based on cloud computing security and data storage issues that organizations face when they upload their data to the cloud in order to share it with their customers. Most of these issues are acknowledged in this paper, and there is also discussion of the various perspectives on cloud computing issues.
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Paper Nr: 6
Title:

Internet of Things Platform and Services for Connected Cars
Authors:

Chungki Woo, Jihyun Jung, Jang Euitack, Jongwoong Lee, Jaewook Kwon and Daeyoung Kim
Abstract: In recent years, the connected car market has been expanding. Various car manufacturers are trying to provide Internet of things (IoT) services by collecting and analysing sensing data from cars. However, there is not a well-defined standardized IoT platform to handle the big data for the various car OEM companies or service providers. To resolve this issue, we propose a globally standardized IoT platform for connected cars based on Global Standard 1 (GS1). We extended and remodelled Electronic Product Code global (EPCglobal), one of GS1 standards, and developed a new IoT platform framework called open-language for IoT (Oliot). Then, based on the framework, we modelled car events and developed some hardware and software modules to capture, store, and share the event data. We also implemented demonstration services using the shared data for verification. This research can provide a new ecosystem to the connected car industries and service providers to enable standardized handling and processing of big data. As a result, it will be much easier to create and provide a greater variety of services and combinations of services.
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Paper Nr: 7
Title:

IDAC: A Sensor-based Model for Presence Control and Idleness Detection in Brazilian Companies
Authors:

Rodrigo da Rosa Righi, Gustavo Rostirolla, Eduardo Souza dos Reis, Gabriel Fischer, Victor Chang and Muthu Ramachandran
Abstract: This article proposes a new model named IDAC for idleness detection and automatic clocking in Brazilian companies. Based on the studies and the gaps identified in related work, we highlight the model features and how it interacts with sensors, providing idleness detection based on the historical movement of the employees. We developed a prototype that was evaluated through simulation, taking into account the architectural plant and the employees behavior of five real Brazilian companies. The results reveals the benefits of using IDAC both at the owner (control and productivity) and employees (the clocking actions occurs automatically) levels.
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Short Papers
Paper Nr: 1
Title:

Business Intelligence and Data Analytics (BI&DA) to Support the Operation of Smart Grid - Business Intelligence and Data Analytics (BI&DA) for Smart Grid
Authors:

G. Escobedo, Norma Jacome and G. Arroyo-Figueroa
Abstract: Smart Grid is the modernization of electrical networks using intelligent systems and information technologies. The growing interest that the smart grid is attracting and its multidisciplinary nature motivate the need for solutions coming from different fields of knowledge. Due to the complexity, and heterogeneity of the smart grid and the high volume of information to be processed, Business Intelligence and Data Analytics (BI&DA) appear to be some of the enabling technologies for its future development and success. The aim of this article is proposed a framework for the development of BI&DA techniques applied to the different issues that arise in the smart grid development. As case study the paper presents the applications of BI&DA in database of processes security for Distribution System. The goal is to have available and timely information to make better decisions, to reduce the number of accidents and incidents. This work is therefore devoted to summarize the most relevant challenges addressed by the smart grid technologies and how BI&DA systems can contribute to their achievement.
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