We live in a complex world characterised by complex people, complex times, and complex social, technological, economic, and ecological environments. The broad aim of our work is to investigate the use of ICT technologies for solving pressing problems in smart cities and societies. Specifically, in this paper, we introduce the concept of deep journalism, a data-driven deep learning-based approach, to discover and analyse cross-sectional multi-perspective information to enable better decision making and develop better instruments for academic, corporate, national, and international governance. We build three datasets (a newspaper, a technology magazine, and a Web of Science dataset) and discover the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool, DeepJournal (Version 1.0), that implements our proposed approach. We elaborate on 89 transportation parameters and hundreds of dimensions, reviewing 400 technical, academic, and news articles. The findings related to the multi-perspective view of transportation reported in this paper show that there are many important problems that industry and academia seem to ignore. In contrast, academia produces much broader and deeper knowledge on subjects such as pollution that are not sufficiently explored in industry. Our deep journalism approach could find the gaps in information and highlight them to the public and other stakeholders.

Variant approaches used to release scents in most recent olfactory displays rely on time for decision making. The applicability of such an approach is questionable in scenarios like video games or virtual reality applications, where the specific content is dynamic in nature and thus not known in advance. All of these are required to enhance the experience and involvement of the user while watching or participating virtually in 4D cinemas or fun parks, associated with short films. Recently, associating the release of scents to the visual content of the scenario has been studied. This research enhances one such work by considering the auditory content along with the visual content. Minecraft, a computer game, was used to collect the necessary dataset with 1200 audio segments. The Inception v3 model was used to classified the sound and image dataset. Further ground truth classification on this dataset resulted in four classes: grass, fire, thunder, and zombie. Higher accuracies of 91% and 94% were achieved using the transfer learning approach for the sound and image models, respectively.

Timing synchronization has a vital role in swarm drones' network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones' network, master drones' (MD) neighbor drone's timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intraswarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.INDEX TERMS Drones' Network, Timing synchronization, unmanned aerial vehicle (UAV), Cluster, Swarm.

Although olfaction can enhance the user’s experience in virtual environments, the approach is not widely utilized by virtual contents. This is because the olfaction displays are either not aware of the content in the virtual world or they are application specific. Enabling wide context awareness is possible through the use of image recognition via machine learning. Screenshots from the virtual worlds can be analyzed for the presence of virtual scent emitters, allowing the olfactory display to respond by generating the corresponding smells. The Convolutional Neural Network (CNN), using Inception Model for image recognition was used for training the system. To evaluate the performance of the accuracy of the model, we trained it on a computer game called Minecraft. The results and performance of the model was 97% accurate, while in some cases the accuracy reached 99%.

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