Users increasingly expect conversational agents (CAs) to communicate effectively by adjusting language strategies to different contexts and providing personalized responses. Using a 2 (context: service vs. emergency) × 3 (second-person pronoun usage: informal “you,” formal “you,” vs. no pronoun) between-subjects design, this study investigated how second-person pronouns used by CAs influenced users’ perceptions through an online survey with 1242 valid responses. To facilitate the customized design of CAs, demographic factors (e.g., age, gender) are also considered. Results indicated that subtle shifts in pronoun usage triggered significant differences in psychological consequences, with both forms of “you” providing fewer benefits than omitting pronouns across contexts. Users in the emergency context exhibited lower purchase willingness than those in the service context. Age, gender, and education also significantly predicted users’ perceptions. Overall, the findings highlight the psychological impacts of second-person pronouns and inform the design of interaction strategies for CAs.

The past decade has witnessed the rapid development of deep neural networks (DNNs) for automatic modulation classification (AMC). However, most of the available works learn signal features from only a single domain via DNNs, which is not reliable enough to work in uncertain and complex electromagnetic environments. In this brief, a new cross-domain signal transformer (CDSiT) is proposed for AMC, to explore the latent association between different domains of signals. By constructing a signal fusion bottleneck (SFB), CDSiT can implicitly fuse and classify signal features with complementary structures in different domains. Extensive experiments are performed on RadioML2016.10A and RadioML2018.01A, and the results show that CDSiT outperforms its counterparts, particularly for some modulation modes that are difficult to classify before. Through ablation experiences, we also verify the effectiveness of each module in CDSiT.

The purpose of this research is to develop a methodology that combines the quantitative and the qualitative analysis to generate personas for products on social platforms. The user data on social platforms contain massive information relating to the lifestyle people have and the products people use or are interested. By analyzing the specific content generated by users on social platforms, e.g., content involving the term “tablet,” it is possible to reveal how the users consider or use the product, “tablet.” By analyzing the users’ homepages, the information relating to the users’ daily life can be found. We collected 276, 675 pieces of relevant data regarding the product, “tablet,” from 12, 965 online users on China’s widely used social media platforms. Then automatic user segments and the profiles of each group were generated and structured by natural language processing technology. The results of these quantitative analyses were then qualitatively examined by manual analyses, which provide additional insights and detailed descriptions on the automatically generated persona profiles. In this study, six personas representing distinct user types were created. The mixed method of combining the quantitative and qualitative methods makes the generation of personas faster and more insightful. The generated personas can represent real user behavior and characteristics and can provide insights into the products, which also can provide support on designing new products and optimizing existing products.

Although autonomous vehicles (AVs) have been proved to have potential to reduce traffic accidents, people often hold skepticism toward the safety of AVs. The critical question is that what drives people to perceive and expect the safety of AVs. In this paper, a structural model is constructed, comprising risk sensitivity, risk-related feelings, perceived and expected safety. A survey of 185 respondents was conducted, and the results were analyzed by using PLS-SEM. Our study provided a deeper understanding of mental reasons for safety judgments in AVs by exploring how risk sensitivity and feelings explain safety judgment in AVs and establish the connections between them. It is found that perceived safety was influenced by feelings of worry more than dread, but there was no significant correlation between perceived safety and expected safety. New possible research ideas of AV’s safety study are discussed in the paper.

Driving is the most complex human-computer interaction for many individuals. The user experience (UX) and usability of driving are key issues in determining driving safety and user acceptance. Therefore several studies on both issues have been performed toward formulating beneficial driver-vehicle interactions and for future intelligent vehicles and autonomous environments. To provide an insight into these studies, this present study conducts bibliometric analyses to obtain information about when and where the research was conducted by whom and how mainstream content has evolved over the years. BibExcel and CiteSpace were utilized for the performance and co-word network analyses. A total of 2498 documents from 2000– 2019 in the field were filtered from the “Core Set” of Web of Science (WoS) for quantitative analysis. This study offers researchers a comprehensive understanding of UX and usability of driving studies in the last decades and research directions for the future.

Design knowledge plays a key role in the design of a good automotive user interface. In this paper, we propose a qualitative field study and design approach to develop a design knowledge system for automotive user interface. The methods used are based on contextual design and similar concepts from the area of User Centered Design (UCD). Using the data from field study and design as the knowledge content, we developed a web-based design knowledge system: Transportation User Interface Design Knowledge System (TUI) that consists of user, design, and scenario modules. Designers and engineers can use the system to identify drivers' needs, generate design ideas, and help them enhance the automotive user interface. The system has been adopted in one automotive design firm in China, and one actual interface has been designed with the help of the system. The success of the adoption of the system is also discussed in this paper.

Collaborative product design is a problem-solving process where designers' knowledge and expertise is always tactic and implicit, in the area of which knowledge acquiring is the difficult problem. This paper depicts the concept of design scenario and the process and pattern of design knowledge acquiring in a cognitive approach. Through a scenario-based collaborative product design cognitive experiment based on protocol analysis and sketch analysis, a model of scenario-based knowledge and acquiring in product design is proposed. The model considers that design knowledge acquiring is a process of scenario moving which includes the two types of spontaneous design knowledge acquiring method: concept-driven knowledge acquiring and data-driven knowledge acquiring. On the basis of the model, a scenario-based CAD product design system - CBID is developed to improve the ability and quality of collaborative product design of Chinese corporations