Abstract: . The new era of industrial automation has been developed and implemented quickly and it is impacting different areas on society. Especially in recent years, much progress has been made in this area, leading to some people talking about the fourth industrial revolution. Every day factories are more connected and able to communicate and interact in real time between industrial systems. There is a need to flexibilization on the shop floor to promote a higher customization of products in a short life cycle and service-oriented architecture is a good option to materialize this. This chapter discusses challenges of this new revolution, also known as Industry 4.0, addressing the introduction of modern communication and computing technologies to maximize interoperability across all the different existing systems. Moreover, it will cover technologies that support this new industrial revolution and discuss impacts, possibilities, needs and adaptation.

Abstract: With the popularization of automobiles, more and more algorithms have been proposed in the last few years for the anomalous trajectory detection. However, existing approaches, in general, deal only with the data generated by GPS devices, which need a great deal of pre-processing works. Moreover, without the consideration of region's local characteristics, those approaches always put all trajectories even though with different source and destination regions together. Therefore, in this paper, we devise a novel framework for anomalous trajectory detection between regions of interest by utilizing the data captured by Automatic Number Plate Recognition(ANPR) system. Our framework consists of three phases: abstraction, detection, classification, which is specially engineered to exploit both spatial and temporal features. In addition, extensive experiments have been conducted on a large-scale real-world datasets and the results show that our framework can work effectively.

Abstract: An effective nature disaster early warning system often relies on widely deployed sensors, simulation based predicting components, and a deci-sion making system. In many cases, the simulation components require advanced infrastructures such as Cloud for performing the computing tasks. However, effectively customizing the virtualized infrastructure from Cloud based time critical constraints and locations of the sensors, and scaling it based on dynamic loads of the computation at runtime is still difficult. The suitability of a Dynamic Real-time Infrastructure Planner (DRIP) that handles the provisioning within cloud environ-ments of the virtual infrastructure for time-critical applications is demonstrated with respect to disaster early warning systems. The DRIP system is part of the SWITCH project (Software Workbench for Interac-tive, Time Critical and Highly self-adaptive Cloud applications).

Abstract: In the paper we propose ontology based scientific visualization tools to calibrate and monitor various IoT devices in a uniform way. We suggest using ontologies to describe associated controllers, chips, sensors and related data filters, visual objects and graphical scenes to provide self-service solutions for IoT developers and device makers. High-level interface of these solutions enables composing data flow diagrams defining both the behavior of the IoT devices and rendering features. According to the data flow diagrams and the set of ontologies the firmware for IoT devices is automatically generated incorporating both the data visualization and device behavior code. After the firmware loading, it's possible to connect to these devices using desktop computer or smartphone/tablet, get the visualization client code over HTTP, monitor the data and calibrate the devices taking into account monitoring results. To monitor the distributed IoT networks a new visualization model based on circle graph is presented. We demonstrate the implementation of suggested approach within ontology based scientific visualization system SciVi. It was tested in a real world project of an interactive Permian Antiquities Museum exhibition creating.

Abstract: As the mobile device gaining increasing popularity, Acoustic Speech Recognition on it is becoming a leading application. Unfortunately, the limited battery and computational resources on a mobile device highly restrict the potential of Speech Recognition systems, most of which have to resort to a remote server for better performance. To improve the performance of local Speech Recognition, we propose C-1-G-2-Blstm. This model shares Convolutional Neural Network’s ability of learning local feature and Recurrent Neural Network’s ability of learning sequence data’s long ependence. Furthermore, by adopting the Gated Convolutional Neural Network instead of a traditional CNN, we manage to greatly improve the models capacity. Our tests demonstrate that C-1-G-2-Blstm can achieve a high accuracy at 90.6% on the Google SpeechCommands data set, which is 6.4% higher than the state-of-art methods.

Abstract: Based on the construction of Shandong Normal University’s smart authentication system, this paper researches the key technologies of Open Authorization(OAuth) protocol, which allows secure authorization in a simple and standardized way from third-party applications accessing online services. Through the analysis of OAuth2.0 standard and the open API details between different applications, and concrete implementation procedure of the smart campus authentication platform, this paper summarizes the research methods of building the smart campus application system with existing educational resources in cloud computing environment. Through the conducting of security experiments and theoretical analysis, this system has been proved to run stably and credibly, flexible, easy to integrate with existing smart campus services, and efficiently improve the security and reliability of campus data acquisition. Also, our work provides a universal reference and significance to the authentication system construction of the smart campus.

Abstract: Due to the development of IoT solutions, we can observe the constantly growing number of these devices in almost every aspect of our lives. The machine learning may improve their intelligence and smartness. Unfortunately, the highly regarded programming libraries consume to much resources to be ported to the embedded processors. Thus, in the paper the concept of source code generation of machine learning models is presented as well as the generation algorithms for commonly used machine learning methods. The concept has been proven in the use cases.