Autonomous Vehicles (AVs) are becoming prevalent in our society, from self-driving cars and autonomous shuttle buses on urban roads to delivery robots on the pavements and within buildings. These emerging applications have generated a huge interest in the concept of Mobility-on-Demand (MoD) and specifically, Autonomous MoD (AMoD). In this work, we highlight the initiatives of the Singapore-MIT Alliance for Research and Technology (SMART) in the area of AMoD. We discuss the fundamental building blocks of AMoD systems, the solutions and algorithms that we have developed and successfully deployed for public trials since late 2014, and the challenges that we have encountered during the process.

Existing person re-identification (re-id) models mainly focus on still-image-based module, namely matching person images across non-overlapping camera views. Since video sequence contains much more information than still images and can be easily achieved by tracking algorithms in practical applications, the video re-id has attracted increasing attention in recent years. Distance learning is crucial for a re-id system. However, the computed distances in traditional video-based methods are easily distracted by the randomness of data distribution, especially with small sample size for training. To preferably distinguish different people, a novel regularised hull distance learning video-based person re-id method is proposed. It is advantageous in two aspects: robustness is guaranteed due to expanded video samples by regularised affine hull with limited ones, discriminability is ensured due to penalised hard negative samples more severely. Hence, the discriminability and robustness of the learnt metric are strengthened. Comparisons with the state-of-the-art video-based methods as well as related methods on PRID 2011, iLIDS-VID and MARS datasets demonstrate the superiority of the authors’ method.

Academic emotions can produce a great impact on the learning effect. Normally, emotions are expressed externally in the students' facial expressions, speech and behaviour. In this paper, the focus is on automatic academic emotion inference based on facial expressions in online learning. Considering the lack of training samples for the inference algorithm, a spontaneous facial expression database is established. It includes the facial expressions of five common academic emotions and consists of two subsets: a video clip database and an image database. A total of 1,274 video clips and 30,184 images from 82 students are included in the database. The samples are labelled by both the participants and external coders. An extensive analysis is carried out on the image database using a convolutional neural network (CNN)-based algorithm to infer self-annotation. Some data augmentation algorithms are applied to improve the algorithm performance. Additionally, an adaptive data augmentation algorithm based on spatial transformer network is introduced, which can remove some confounding factors in the original images. The algorithm can obviously improve the inference performance, which has been proven by comparing some evaluation indicators before and after adoption. Such a database will certainly accelerate the application of affective computing in the educational field.

This study aims to develop a robotic system human–machine interaction (HMI) to facilitate surgical training through visual and kinaesthetic feedbacks. This is motivated by the pressing need for effective surgical training and the unaddressed gaps in existing surgical training simulator for minimally invasive procedures. This study establishes the design concept and scope for development to facilitate the required HMI training model. Subsequently, implementation and demonstration of the model is carried out with analytical experiments to assess the feasibility of the proposed concept. The design concept of the robotic system for training is demonstrated through an user experiment. Results suggest viability and observable benefits in the authors’ proposed kinaesthetic HMI guidance for the trainee. Potential impact of this study includes the development of a novel training paradigm that engages trainees through collaborative training facilitated by human trainers and active kinaesthetic simulation. Although motivated by surgical training applications, the concept developed in this study can potentially be extended for general motor skill learning.

This paper reports on IoT4Kids, a study exploring the privacy, security and safety implications of children programming the Internet of Things. The study focuses on the BBC micro:bit as one device that allows children to create rudimentary IoT devices. Prior publications have described the first stage of this study, which involved workshops with child participants. This paper instead focuses on the second stage of the project, which involved conducting key informant interviews with representatives from our project partners in order to understand the risks children face with interacting with programmable IoT devices. We describe themes that emerged from these interviews, along with implications for the study and for future work in this area.

Educational progress in universities is of an encompassing conception. It encloses several institutes, cultural aids, sports facilities, public libraries, etc. These elements could be included within either gated or non-gated campuses. This research aims at achieving advanced and integrated managerial policy all over the universities of the kingdom. Therefore, it has been necessary, to conduct thorough studies in consideration of the potential, specialisations and resources of these universities for an advanced educational process. Speaking of the educational facilities of an individual university campus is different from studying the entire university campuses in Bahrain all together. Thereupon, applying sustainability enforces the urban expertise to anticipate for the integration between the universities right there. Under these circumstances, e-management should have been formerly existent, thanks to its accurate and comprehensive output. Abiding by such adequate resultants would naturally end up in appropriate implementation of the respective networks, realising a well-planned educational progress in Bahrain.

Owing to factors such as globalisation and the extensive use of the Internet, people are increasingly interested in learning a second and even a third language. From an academic perspective, the process of foreign language learning is defined by methodologies and supported by technology. Language learning applications are composed of learning exercises, which are pedagogical tools to introduce new language concepts (new vocabulary, grammar etc.). The development of this type of applications is complex due to the diversity of language learning methodologies, the variety of execution environments (web, mobile and desktop) and the number of different technologies that can be used. This study proposes a model-driven approach developing software to support different language learning processes. These processes consist of different learning exercises running on different platforms. The authors’ proposal describes a metamodel that defines the entities and their relationships to define learning exercises for learning applications. This metamodel enables designers to support the development of language learning applications. This study illustrates the expressiveness and reuses power of the proposal by modelling learning activities from two different learning processes (Lexiway and Duolingo).

West Texas A&M University established a strategic initiative to develop the Internet of Things across its 176-acre campus that connects more than 42 different buildings, including 11 residence halls and a 2,393-acre working ranch that is dedicated to faculty and student research in agriculture, livestock, crop and land management, and the development and research of wind energy. The University has recently adopted the NIST Cybersecurity Framework, which is now used by 30 percent of U.S. organizations and is projected to reach 50 percent by 2020 [1]. This security policy framework, combined with a new information security governance model, has enabled the University to embark on other IoT initiatives that relate to mobile and wireless communications such as smart-connected transportation, smart-connected parking, and the deployment of a LoRaWAN network to support faculty research projects. Through these IoT initiatives, West Texas A&M University will be able to transfer its insights and ideas to other similar regions in the nation, and even beyond our national borders.

In this study two library services, current awareness service (CAS) and selective dissemination of information service (SDI), provided by University of Bahrain, were considered as examples to develop system prototype. The developed system uses Smartphone Augmented Reality (AR) technology to deliver up-to-date information about library services in an interactive way using multimedia to increase researchers' (faculty members and students) awareness. The potentials and other perspectives of the proposed system were discussed. Although it can be further investigated, the proposed system could be considered as a new model for current awareness services of any digital library. Findings revealed that Smartphone AR system provides information in a real environment, giving researchers instant assistance and awareness of their needs, and is a good personal learning tool. However, to make the delivery of Smartphone AR system more effective, suggestions for further investigations are provided like Measuring and reviewing acceptance to the application in terms of Awareness, timeliness, coverage ratio, and usage then collected and summarized by focus groups' thoughts and reactions.

This study deals with the development of a photovoltaic (PV) electronic emulator (PVEE), operating integrated with a graphic computing platform, for teaching topics related to PV systems in either undergraduate or postgraduate courses. The PVEE is deployed by a DC–DC buck converter to emulate on its output terminals several characteristic curves of a specific PV system, in which climate conditions such as solar irradiation and temperature are taken into account. Emulation of the PV module characteristic curves is embedded in a digital signal controller, which is based on a mathematical model that represents the electric circuit of the PV cell. The educational platform makes possible the following tasks: (i) define and change the PV cell model, (ii) define the number of PV modules, (iii) define and change the solar irradiation and temperature that the PV modules are subjected to, and (iv) visualise voltage and current quantities that are synthesised by the PVEE. From experimental tests, the PVEE is evaluated considering climatic variations involving solar irradiation, temperature, and load transients, whose purpose is to validate the theoretical development. Furthermore, the DC–DC boost converter is employed to perform the maximum power point tracking. Experimental results demonstrate the effectiveness of the proposed system.