The non-contact healthcare system is a system that can avoid germ infection, and can also provide comfortable and convenient health care services for the caregivers. In the current thermal imaging research, local or small area images are used to represent the overall temperature information of the participants, but the temperature feature of the face should not only be used in a small part and ignore other parts. The facial thermal image can show clear temperature feature but is not conducive to meaningful feature extraction. Therefore, this research proposed a novel facial thermal image feature extraction method, which is used facial landmarks to detect and cut 12 blocks to establish a new feature matrix based on color mean values and standard deviation values. It can establish clear features on facial thermal images. The core part of the proposed healthcare system is the use of a deep learning framework, which is based on CAFFE under the DIGITS platform. The CAFFE runs the classic CNN, GoogLeNet. Based on the acquired images and new feature types, four models were trained, which were used for the raw RGB image, raw thermal image, RGB feature image, and thermal feature image. In the experiment, 800 images were used for training and validation, and 200 images were used for testing. An additional 40 images were used for random testing. The experimental results show that RGB images cannot be effectively used, thermal images can effectively predict the health status, and thermal feature images have the highest prediction accuracy.

Existing edge computing architectures do not support the updating of neural network models, nor are they optimized for storing, updating, and transmitting different neural network models to a large number of IoT devices. In this paper, a cloud-edge smart IoT architecture for speeding up the deployment of neural network models with transfer learning techniques is proposed. A new model deployment and update mechanism based on the share weight characteristic of transfer learning is proposed to address the model deployment issues associated with the significant number of IoT devices. The proposed mechanism compares the feature weight and parameter difference between the old and new models whenever a new model is trained. With the proposed mechanism, the neural network model can be updated on IoT devices with just a small quantity of data sent. Utilizing the proposed collaborative edge computing platform, we demonstrate a significant reduction in network bandwidth transmission and an improved deployment speed of neural network models. Subsequently, the service quality of smart IoT applications can be enhanced.

Traditional teacher-centered teaching focused on classroom lectures has met numerous challenges in recent years, which has motivated teachers to make use of design thinking for their classes. This study made use of design thinking in an iOS programming course to find ways to improve the students’ critical thinking, learning motivation and solve practical issues through mobile APP development. In the program, students were required to answer situational motivation questionnaires before and after design thinking is implemented in the course. Results showed that the values of the Cronbach’s Alpha for the initial course expectation in both iterations were reliable. Subsequently, when students feel that the solutions are recognized and supported by other students and teachers in the course, there is an upward trend in the curves of their external regulation, identified regulation, and intrinsic motivation. This study successfully designed the course content and evaluation methods that have shown significant increase in the students’ motivation through the incorporation of design thinking in the mobile application programming course.

With the growing popularity of social network and video sharing services, video sharing behaviors have become increasingly socializing. People can produce and share high-quality videos at any time and place using high-speed mobile networks and multimedia IoT devices, e.g., Android Things and smart TV Boxes. The multimedia IoT devices require higher bandwidth, memory, and computing resources to process and transmit multimedia contents such as audio, video, and image. In this paper, we propose a social-aware P2P video transmission strategy for multimedia IoT devices. In the proposed architecture, multimedia IoT devices are communicated using a peer-to-peer network. The peers in our work are represented by multimedia IoT devices with user interactions. Since users have their own friend list, the social links can be classified into different priority classes in accordance with their social relationships, e. g., family, friends, and others. The proposed strategy adopts weighted fair queue (WFQ) for P2P video transmission according to different queuing priority classes. Each priority class is given a weighted factor according to social relationship, current download progress, and mutual resource sharing contributions. By leveraging the inherent trust associated with social links, the proposed strategy can reduce the impact of free riders and give users good video sharing and watching experiences with multimedia IoT devices.

In the rapid development of the information technology age, many travelers search for travel articles through the Internet. These travel articles include the experience and knowledge of traveler, which can be used as a reference for tourism planning and attraction selection. At present, the most travel experience and knowledge is available in online travel reviews (OTR). OTR and eWOM (electronic word-of-mouth) contain a lot of knowledge of consumers and travelers. Many travelers often look for OTR content through virtual communities, blogs, and search engine, but the search results often cause information overload problems. In addition, through virtual communities, blogs, and search engines, an OTR search still requires using keywords. However, most travelers cannot know the name of the attraction; therefore, travelers cannot use the correct keywords to search. That causes travelers to be unable to get enough information from OTR and unable to make the best travel plan. Therefore, this study focuses on the ontology-based tourist knowledge representation and recommendation method. And the study is to search for popular attractions from the OTR content and construct a tourist knowledge structure for these travelers. When the tourists do not need to know the keywords of the popular attraction name, they just need to get their current location; and then ORT content will recommend the next attraction to the traveler, which helps the traveler make the correct travel decision. The evaluation result showed that the method proposed in this study can help the travelers to quickly make the travel decision and is better than the traditional searching methods.