Data Intelligence in the Context of Big Data: A Survey (1-27)
        Hicham M. Safhi, Bouchra Frikh, Badr Hirchoua, Brahim Ouhibi, and Ismail Khalil
Mining Big Data is the capability of finding new useful information in complex massive datasets, that may be continuously changing and may have varied data types. Big data is helpful only when it is transformed into knowledge or useful information. Data Intelligence is about transforming data into information, information into knowledge, and knowledge into value. It refers to the intelligent interaction with data in a rich, semantically meaningful ways, where data is used to learn and to obtain knowledge. However, extracting valuable information from this data by following the classical Knowledge Discovery process reveals new previously unknown challenges, due to Big Data properties. These challenges have received a lot of attention in recent years, and still need more and more contribution and research. A large number of publications have yielded a plethora of proposed methods and algorithms. In this paper, we provide a comprehensive literature review on Big Data current status. We present the Data Intelligence framework in the context of Big Data from data acquisition until insight extraction, we highlight its main issues, and identify its progress in both technological and algorithmic perspectives. We summarize and analyse relevant research papers in the field, collected from different scientific databases. This investigation will help researchers to understand the current status of Data Intelligence, discover new research opportunities, and gain information about this field.

Entropy Based Personalized Learning Management System (Pelms) – An Approach Towards Business and IT Education (28-42)
        Dinesh Kumar Saini
Development of personalized e-Learning Management Systems (PeLMS) using advance modelling techniques is crucial to achieve personalization and adaptation in content deliveries. This paper delves on some important issues related to the integration of PeLMS with Semantic Overlay Networks (SON). Developing a prototype for a Business Statistics course delivery, a Semantic Web based PeLMS using Topic Maps, Ontology, Classification rules, and ISA Algorithm has been prescribed. Considering classification as one of the important aspects in the content organisation in PeLMS, this study proposes a new classifier, based upon the maximum entropy principle. It is argued that the most similar items in a learning object repository space can be classified together, on the statistical basis, to build a PeLMS. The ISA algorithm has been proposed to enable this classification. The paper also presents three key Learning Object Models for the organization of contents and suggests how an optimal level of personalization that can be ensured by maintaining the entropy within the system. Mechanisms such as normalization and time complexity have also been suggested to ensure personalized and optimal content delivery.

Cultural and Psychological Factors in Cyber-Security (43-56)
        Tzipora Halevi, Nasir Memon, James Levis, Ponnurangam Kumaraguru, Sumit Arora, Nikita Dagar, Fadi Aloul,
         and Jay Chen
Increasing cyber-security presents an ongoing challenge to security professionals. Research continuously suggests that online users are a weak link in information security. This research explores the relationship between cyber-security and cultural, personality and demographic variables. This study was conducted in four different countries and presents a multi-cultural view of cyber-security. In particular, it looks at how behaviour, self-efficacy and privacy attitude are affected by culture compared to other psychological and demographics variables (such as gender and computer expertise). It also examines what kind of data people tend to share online and how culture affects these choices. This work supports the idea of developing personality based UI design to increase users’ cyber-security. Its results show that certain personality traits affect the user cyber-security related behaviour across different cultures, which further reinforces their contribution compared to cultural effects.

Recognizing and Exploring Azulejos on Historic Buildings’, Facades by Combining Computer Vision and Geolocation in Mobile Augmented Reality Applications (57-74)
        Carlos Santos, Tiago Araújo, Paulo Chagas Junior, Bianchi Meiguins, and Nelson Neto
Mobile augmented reality (MAR) applications assist users in navigating and exploring their actual surroundings, displaying virtual contents that correspond to objects and scenes in the real world. However, despite the growing popularity of these applications, some experiences can be frustrating when users are unable to correctly recognize Points of Interest (POI), objects, or places they want to visit or obtain more information. The misleading recognition can occur due to imprecise Global Positioning System (GPS) data or a lack of QR codes for interaction. Hence, this article presents a proposal that combines pattern recognition in images with geolocation information to improve the accuracy of the identification of POIs. The usage scenario is the identification of azulejos (tiles) on the facades of historic buildings in the city of Belém of Pará, Brazil. This issue is relevant based on similarities between azulejos and its huge amount of different types, whose variety of designs and colors of geometric forms can make the identification a hard task. The used methods to extract the azulejos’ features were the co-occurrence matrix combined with color percentage, and the global positioning data to increase the accuracy of classification because similar azulejos can be geographically far apart. Tests were conducted using six machine learning algorithms (neural network, decision tree, k-nearest neighbors, naive Bayes, random forest, and support vector machine) of different paradigms. The first results show that the pattern recognition in images combined with geolocation information is a promising approach for better identification of the POIs in MAR applications.

SAHL: A Touchscreen Mobile Launcher for Arab Elderly (75-99)
        Muna Al-Razgan and Hend S. Al-Khalifa
Mobile phones are becoming a great necessity for elderly people; the features they provide supported by rich functionality made them one of the indispensable gadgets used in their daily life. However, as mobile phones get more advanced and their interfaces become more complicated, new design recommendations and guidelines need to be developed to serve the elderly needs. In this project we distilled guidelines and design recommendations targeting elderly users' needs. Then, we used these guidelines to implement a prototype user interface that takes Arab elderly requirements into considerations. We then evaluated the developed interface on a set of Arab elderly people to determine its appropriateness for the target audience.