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The smart revolution has penetrated in a wide range of applications. Smart campus, as the high-end form of education systems, deploys cutting-edge information and communication technologies to enhance the effectiveness and efficiency of campus services. Under the pandemic of COVID-19, smart campus has shown unprecedented importance owing to its remote, personalized, and ubiquitous features. All these factors have made smart campus an ongoing intense research topic in recent years, whereas existing reviews on smart campus were conducted in earlier years and thus an update is imperatively needed to investigate and summarize the emerging knowledge, technologies, and applications in this context. This paper conducts a systematic review on smart campus technologies and applications, and then strategically classifying them into different domains to investigate the current research pattern. Moreover, adhering to the human-centered principle of smart campus development, a human-centered case study has been carried out and presented in this paper to evaluate the consistency and adherence of current research trend to the stakeholders needs and interests.

Phishing attacks are still seen as a significant threat to cyber security, and large parts of the industry rely on anti-phishing simulations to minimize the risk imposed by such attacks. This study conducted a large-scale anti-phishing training with more than 31000 participants and 144 different simulated phishing attacks to develop a data-driven model to classify how users would perceive a phishing simulation. Furthermore, we analyze the results of our large-scale anti-phishing training and give novel insights into users’ click behavior. Analyzing our anti-phishing training data, we find out that 66% of users do not fall victim to credential-based phishing attacks even after being exposed to twelve weeks of phishing simulations. To further enhance the phishing awareness-training effectiveness, we developed a novel manifold learning-powered machine learning model that can predict how many people would fall for a phishing simulation using the several structural and state-of-the-art NLP features extracted from the emails. In this way, we present a systematic approach for the training implementers to estimate the average “convincing power” of the emails prior to rolling out. Moreover, we revealed the top-most vital factors in the classification. In addition, our model presents significant benefits over traditional rule-based approaches in classifying the difficulty of phishing simulations. Our results clearly show that anti-phishing training should focus on the training of individual users rather than on large user groups. Additionally, we present a promising generic machine learning model for predicting phishing susceptibility.

Considering the difficult maneuverability of the hovercraft and the driving burden of the pilot, an automatic tracking system including an intuitive human-computer interface and an adaptive chattering-free full-order terminal sliding mode (ACFTSM) controller is designed for humans (non-engineers in particular). The human-computer interface is easy to be understood and used. For the design of the controller, four degrees of freedom (DOF) model of hovercraft is established. And based on the four DOF model, the desired velocities are proposed by a TSM manifold to achieve the finite-time trajectory tracking. Then, the desired velocities can be tracked by the designed CFTSM controller which has superior properties, such as fast and finite-time convergence, highly robust to the disturbances, and low sensitivity to the system parameter variations. Moreover, both the chattering and singularity problems of TSM are resolved by the CFTSM method. By the well combining with radial basis function neural networks in this paper, the new method, named ACFTSM, also has an ability to deal with the strong nonlinearity and uncertainty of hovercraft's complex model. The controller guarantees the fast and finite-time convergence of velocities and position tracking errors. And numerical simulations are implemented to demonstrate the robustness and superiority of the designed controller.

With the continuous innovation and development of agent technology, the application of smart technique is gradually changing to intelligent and human-centered technology. Also, intelligent technique is involved in many fields, and software defined network (SDN) is one of them. SDN is one of the most popular and most promising new technologies in the current network area, which has particular advantages over other traditional networks. However, most of the research work focus on the efficiency and function of SDN, while the access control and ciphertext search function in SDN have not been taken into full consideration. In this paper, we propose a new smart system based on SDN. It also equips with the functions of fine-grained access control and searchable encryption function, which combines a novel attribute-based searchable encryption scheme (ABSES) with SDN architecture. Our construction realizes inter-domain information sharing, fine-grained access control, and ciphertext searchable function. The proposed ABSES effectively ensures the security of ciphertext stored in data center, and that users cannot obtain unauthorized information or illegal network resources without any certification. It reduces network bandwidth and local resources, while improving the scalability and flexibility of SDN access control at the meantime. Finally, our ABSES is proved to be correct and secure under chosen-keyword attack. The comparison with other representative attribute-based searchable encryption schemes demonstrates that our ABSES has somewhat better performance. The proposed new smart system can be used in human life and add more convenience to our daily life.