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Virtual reality (VR) has become a popular choice for education, industrial simulation, entertainment and healthcare applications. User navigation is an essential propriety of virtual applications. However, novice audiences often face the difficulty of engaging with the virtual surrounding environment. This work presents a novel design of a deep learning-based navigation solution to improve the quality of user experience and the engagement with virtual content. We compare two navigation methods avatar-based and arrows-based guidance, both of which are driven by a recurrent neural network (RNN) model. We capture participants’ mobility and eye-gaze to compare the impact of different navigation affects on users’ engagement in VR applications.

The social experience is an important part of art exhibitions. This demo introduces an eye-gaze based generative art prototype for virtual reality (VR) art exhibitions. Our work extends the visitors' experience from individual art exploration to become content co-creators. The design generates live community artworks based on all visitors' visual interactions with VR paintings. During our VR exhibition at a public gallery, over 100 visitors participated in the new creative process for community-generated artworks.

This paper discusses the design of a business model dedicated for IoT applications that would be deployed on top of cloud and fog resources. This business model features 2 constructs, flow (specialized into data and collaboration) and placement (specialized into processing and storage). On the one hand, the flow construct is about who sends what and to whom, who collaborates with whom, and what restrictions exist on what to send, to whom to send, and with whom to collaborate. On the other hand, the placement construct is about what and how to fragment, where to store, and what restrictions exist on what and how to fragment, and where to store. The paper also discusses the development of a system built-upon a deep learning model that recommends how the different flows and placements should be formed. These recommendations consider the technical capabilities of cloud and fog resources as well as the networking topology connecting these resources to things.

Due to the rapid development in the technology, embedded systems have an effective part in controlling and managing variety of hardware and software systems. These systems plan to solve problems that make embedded systems become more complex than before. The majority of embedded systems are currently intended to be more interactive with the environment and process data in real-time in order to fulfil requirements that recruited for. Thus, this requires a high speed of processing data to produce an output. Hence multicore processors are usually employed in embedded system design. To ensure that embedded systems as processing units work perfectly to finish a task with expecting the Worst-Case Execution Time (WCET). A considerable number of researches have been done to handle cache memory organization for multicore processor units in real-time embedded systems. To this end, this paper presents a study of cache management techniques in real-time embedded systems.

This paper discusses the design of a business model dedicated for IoT applications that would be deployed on top of cloud and fog resources. This business model features 2 constructs, flow (specialized into data and collaboration) and placement (specialized into processing and storage). On the one hand, the flow construct is about who sends what and to whom, who collaborates with whom, and what restrictions exist on what to send, to whom to send, and with whom to collaborate. On the other hand, the placement construct is about what and how to fragment, where to store, and what restrictions exist on what and how to fragment, and where to store. The paper also discusses the development of a system built-upon a deep learning model that recommends how the different flows and placements should be formed. These recommendations consider the technical capabilities of cloud and fog resources as well as the networking topology connecting these resources to things.

Internet of Things (IoT), one of the fastest growing Information and Communication Technologies (ICT), is playing a major role in provisioning contextualized, smart services to end-users and organizations. To sustain this role, many challenges must be tackled with focus in this paper on the design and development of thing composition. The complex nature of today’s needs requires groups of things, and not separate things, to work together to satisfy these needs. By analogy with other ICTs like Web services, thing composition is specified with a model that uses dependencies to decide upon things that will do what, where, when, and why. Two types of dependencies are adopted, regular that schedule the execution chronology of things and special that coordinate the operations of things when they run into obstacles like unavailability of resources to use. Both resource use and resource availability are specified in compliance with Allen’s time intervals upon which reasoning takes place. This reasoning is technically demonstrated through a system extending EdgeCloudSim and backed with a set of experiments.