Artificial Intelligence (AI) technologies have been progressing constantly and being more visible in different aspects of our lives. One recent phenomenon is ChatGPT, a chatbot with a conversational artificial intelligence interface that was developed by OpenAI. As one of the most advanced artificial intelligence applications, ChatGPT has drawn much public attention across the globe. In this regard, this study examines ChatGPT in education, among early adopters, through a qualitative instrumental case study. Conducted in three stages, the first stage of the study reveals that the public discourse in social media is generally positive and there is enthusiasm regarding its use in educational settings. However, there are also voices who are approaching cautiously using ChatGPT in educational settings. The second stage of the study examines the case of ChatGPT through lenses of educational transformation, response quality, usefulness, personality and emotion, and ethics. In the third and final stage of the study, the investigation of user experiences through ten educational scenarios revealed various issues, including cheating, honesty and truthfulness of ChatGPT, privacy misleading, and manipulation. The findings of this study provide several research directions that should be considered to ensure a safe and responsible adoption of chatbots, specifically ChatGPT, in education.

Product design experts depend on online customer reviews as a source of insight to improve product design. Previous works used aspect-based sentiment analysis to extract insight from product reviews. However, their approaches for requirements elicitation are less flexible than traditional tools such as interviews and surveys. They require costly data labeling or pre-labeled datasets, lack domain knowledge integration, and focus more on sentiment classification than flexible aspect-opinion analysis. Related works lack effective mechanisms for probing the customer feedback of complex configurable products. This study proposes a generic graph-based opinion mining and analysis method for product design improvement. First, a customer feedback data preprocessing and annotation pipeline that can incorporate designer-specified domain knowledge is proposed. Second, an intuitive opinion-aware labeled property graph data model is designed to ingest preprocessed feedback data and perform ad hoc opinion analysis. Applying the generic model to a real-world dataset demonstrates superior functionality and flexibility compared to related works. A wider range of analyses is supported in a single model without repeating data preprocessing and modeling. Specifically, the proposed method supports regular and comparative aspect-opinion analysis, aspect satisfaction/influence ranking, opinion trend extraction, and targeted aspect-opinion summarization.

Process management is practical in the state-of-the-art of Internet of things research. However, this has become a bottleneck in recent years since an extreme amount of heterogeneous items have to be recorded and traced with radio frequency identification (RFID) tags. In a typical application of process synthesis management, each item will be involved with multiple processes but when those processes are interconnected, an extremely complex network emerges and has to be managed. Existing works on processing management systems, however, are always case-based and only focus on specific application domains. Thus, the general applied processing management model is rather limited. In this paper, we summarize the characteristics of the RFID application domains, and by abstraction, we propose an innovation of the RFID processing model. In this model, each RFID data were deemed as an operation record and all their processing services are logically interconnected and organized as a procedure graph. In addition, we abstract and summarize the basic RFID data processes into a few types. The advantage of this design is that the basic RFID data processing logic can be preprogrammed and in a real domain, the system can be dynamically programmed by automatically constructing the procedure graph nodes with those basic processes and mapping the interconnection logic according to the topology of the graph. As the last part of this paper, we designed a prototype system for medical instruments infection control to demonstrate our approach.

In this paper, we investigate the problem of protecting short-range wireless power from being stolen by rogue devices. We propose an architecture and a generic encryption model in the context of certificateless cryptography for secure wireless power transfer systems. Furthermore, we present insightful research problems which are useful for future research work in this paradigm. The model has two phases: first, an authenticating phase that ensures certificateless authentication and key exchange between the power transmitter (the charging unit) and the power receiver (the device to be charged); Second, an encryption phase which makes use of the obtained session key in the first phase. Simply put, the obtained key is used to generate sequence of switching frequencies in both party devices (encrypt the energy) to allow for secure energy transmission and reception(decryption). In this way, other devices without the knowledge of the session key are prevented from harvesting the transmitted energy from the transmitter to the receiver.