Assessing pilot trainees’ Situation Awareness (SA) is critical for aviation safety and efficiency. This study aimed to evaluate the effectiveness of three distinct eye-tracking metrics, gaze-based metrics, gaze patterns, and gaze entropy, in measuring the SA levels of pilot trainees. A between-subject experiment compared two trainee groups: one received standard training, and the other received training enhanced with vibrotactile feedback. Participants’ eye movements were recorded using wearable eye-tracking glasses before and after training. The experimental group showed improved flight performance, advanced gaze-based metrics, more organized gaze patterns, and increased gaze entropy. These results demonstrate that eye-tracking metrics can effectively reflect SA changes, with gaze entropy significantly correlating with SA measurements and indicating higher SA levels. The study highlights the feasibility of using eye-tracking to evaluate SA in pilot training. Future research should explore varied task complexities and extended training durations to validate these findings and assess long-term impacts.

Efficient resume parsing is critical for global hiring, yet the absence of dedicated benchmarks for evaluating large language models (LLMs) on multilingual, structure-rich resumes hinders progress. To address this, we introduce ResumeBench, the first privacy-compliant benchmark comprising 2,500 synthetic resumes spanning 50 templates, 30 career fields, and 5 languages. These resumes are generated through a human-in-the-loop pipeline that prioritizes realism, diversity, and privacy compliance, which are validated against real-world resumes. This paper evaluates 24 state-of-the-art LLMs on ResumeBench, revealing substantial variations in handling resume complexities. Specifically, top-performing models like GPT-4o exhibit challenges in cross-lingual structural alignment while smaller models show inconsistent scaling effects. Code-specialized LLMs underperform relative to generalists, while JSON outputs enhance schema compliance but fail to address semantic ambiguities. Our findings underscore the necessity for domain-specific optimization and hybrid training strategies to enhance structural and contextual reasoning in LLMs.

Empathic medical conversation is central to patient-centered care within Narrative Medicine. However, difficulties, such as physicians’ limited empathic capabilities and lack of time, impede the practice. Research on real-time, on-site empathic medical exchanges has been limited in exploring technology to assist and enhance physicians’ capabilities. This paper proposed the Empathic Opportunity Perception and Distinction (EOPD) framework for building physician-AI collaboration based on Intelligence Augmentation (IA) for empathic conversations. The EOPD integrates two multi-modal machine learning (ML) models based on facial and verbal cues, presenting a physician-AI interaction framework and three distinctive visualization components: emotional reference, opportunity reminding and keyword collection, and situation understanding. To assess EOPD's effectiveness and gauge physicians’ and patients’ receptiveness, a prototype system named EMVIS ( EM otional VIS ualization ) was designed and developed. Results from the study demonstrated improvements in physicians’ empathy efforts and perceived empathy performance when using EMVIS, particularly for junior physicians. Physicians and patients held positive attitudes towards EMVIS, with patients expressing a high expectation that EMVIS would improve the physician-patient relationship. The research showed the efficacy of the multi-modal ML models in supporting complex affective empathy and EMVIS in facilitating and complementing empathy concerns. It highlighted the tailored support to junior and senior physicians and emphasized physician-AI collaboration to maintain user autonomy and mitigate potential biases. Future research should explore extensive system applications, tailor visual and interactive support for physicians, and implement adaptive and reflective ML models to improve the effectiveness and efficiency of empathy communications.

Ultrasonic mid-air haptics (UMH) has emerged as a promising technology for facial haptic applications, offering the advantage of contactless and high-resolution feedback. Despite this, previous studies have fallen short in thoroughly investigating individuals’ responses to UMH on the face. To bridge this gap, this study compares UMH feedback on various facial sites using the lateral modulation (LM) method. This method allows us to explore the impact of two LM parameters -frequency and amplitude - on both perceptual (intensity) and emotional (valence and arousal) responses. With 24 participants, positive relationships between LM amplitude and perceived intensity and arousal were observed, and the effect of LM frequency varied across facial sites. These findings not only contribute to the development of design guidelines and potential applications for UMH on the face, but also provide insights aimed to enhance the effectiveness and overall user experience in haptic interactions across diverse facial sites.

Considering that a significant portion of the current pedestrian population has limited exposure to automated vehicles (AVs), it is crucial to have a reliable instrument for assessing pedestrians’ initial trust in AVs. Using a survey of 436 pedestrians, this study developed and validated a PITQA (Pedestrians’ Initial Trust Questionnaire for AVs) scale using partial least squares structural equation modeling (PLS-SEM). The proposed scale will be valuable in monitoring the progression of trust over time and considering trust-related factors during the design process. The results revealed that seven key constructs significantly contribute to predicting initial trust between pedestrians and AVs. These constructs include propensity to trust, perceived statistical reliability, dependability and competence, perceived predictability, familiarity, authority/subversion, care/harm, and sanctity/degradation. These shed light on how the trust propensity of individuals, different trust/trustworthiness attributes might constitute different aspects of initial trust in the pedestrian-AV context. The developed scale can be a potentially useful tool for future research endeavors concerning trust calibration and the design of AVs specifically tailored for vulnerable road users.

Patient-physician communication is a crucial aspect of clinical diagnoses and treatments. However, there are barriers to effective empathic practices, including consciousness, busy working rhythms, and difficulties recognising patients’ implicit emotional expressions. While previous research has attempted to support asynchronous medical conversations, this study has explored the use of emotion visualisation techniques for synchronous, face-to-face medical encounters. After interviewing doctors to understand user requirements, an emotion-visualisation prototype, EMVIS, was created. The prototype was evaluated in a study with 31 patients and 37 healthcare providers within different specialist groups using a contextualised Technology Acceptance Model (TAM) and follow-up interviews. The results indicated that patients and physicians were generally accepting of emotion visualisation for medical encounters. Patients were more interested in their physicians’ attitudes and intentions, while physicians accepted the visualisation, but their requirements differed according to their skill levels and specialities. Hence, four supportive factors - emotional empathy, careful attention, human connection, and reflective conversation - elicited information on how EMVIS contributed to medical conversations. Five future opportunities for the emotion visualisation of medical conversations were discussed in respect of the human factors and potential requirements. These include communicating uncertainty, addressing user diversity, providing explanatory information, managing attention, and supporting negotiations.

Abstract Despite the potential importance of emotional aspects in information seeking, there is a lack of adequate attention to emotions' role in facilitating serendipitous information encountering. This paper contributes to this research gap by investigating the role of emotions during the process of perceiving and experiencing serendipitous information encountering in a controlled laboratory setting. The results show that applying a sketch game can stimulate participants' emotions. Our findings indicate that participants are more likely to experience serendipitous information encountering under the influence of positive emotions. This study contributes to an understanding of the relationship between emotions and the perception of serendipitous information encountering. The implications of the possibilities of facilitating positive emotions to induce serendipitous information encountering are discussed.

Automated vehicles (AVs) are one critical application area of artificial intelligence (AI). However, a lack of appropriate trust can be a major barrier to successfully introducing AVs into the market. The objective of this study is to summarize and synthesize the existing literature to gain a greater understanding of factors that will potentially influence the development of pedestrians’ trust in AVs over time. Since AVs will become part of a larger infrastructure system that influences more than just AV users, they should also be accepted by pedestrians and other road users. There is a need for pedestrians to form appropriate levels of trust toward AVs to achieve safe interaction with such vehicles in circumstances characterized by uncertainty and vulnerability. Consequently, factors relevant to the building of this appropriate trust must be understood. By integrating the reviewed empirical studies and related theories, a theoretical model has been proposed and developed, comprising three layers of variability in pedestrian-AV trust (dispositional trust, situational trust, and learned trust). Given that this is an emerging field of research, much still remains unknown, and this review identifies several gaps in current knowledge for each layer of trust, as well as providing suggestions for consideration in future studies. Additionally, the proposed model of pedestrian-AV trust can be useful to transportation researchers, practitioners, designers, and AV manufacturers for designing AVs and related transportation systems for the purposes of successfully integrating AVs into society, and calibrating pedestrians’ trust to the appropriate level.

Cross-cultural design requires designers to understand other foreign cultures, selecting suitable cultural elements, and finally incorporate them into product design. Traditionally, this process is time-consuming and relies to a significant extent on designers’ cultural awareness and design skills. This article proposes a new tool for designers to select and integrate cultural elements in the cross-cultural design process. The proposed approach utilizes state-of-the-art deep learning techniques, which begins by automatically selecting the most suitable style image from all cultural image candidates. Then, the deep-learning-based style transfer technique is introduced to automatically produce a design image that has the same content as the uploaded design content image, and also has the cultural style of the selected style image. To the best of our knowledge, this is the first work that extends deep learning techniques to facilitate cross-cultural design. The tool received positive feedback in a usability evaluation. The empirical results show that our approach can effectively increase designers’ cultural awareness in respect of four cultural element dimensions (color, material, pattern and form). It is an innovative and efficient tool to help designers with idea generation and fast prototyping, although some participants argued that the tool would only assist designers, rather than replace humans.

An intelligent cleaning vehicle improves the efficiency of road cleaning to a great extent. In this case, road garbage recognition is fundamental and crucial. Usually, the pebbles are too small, and the sand has an inconspicuous boundary and unfixed shape on the roads. These issues make the stones and sand too hard to be detected by a garbage recognition system. Hence, we designed a novel semantic segmentation network that acquires the areas and categories of road garbage. We designed a Deep Supervision and High Fusion (DSHF) block to improve the road garbage segmentation accuracy. The designed block with a backbone network of HFCN and UNet++ is comparable with a model that only has either a deep supervision block or a high fusion block. According to the collected road garbage segmentation dataset comprising four categories (stones, leaves, sand and bottles), the model we designed has improved the metric value of MPA by 3% over the state-of-art methods and achieved the highest MIoU (Mean Intersection over Union) with the value of 77.92%. The results of our experiments show that the semantic segmentation of road garbage is feasible and that the proposed DSHF block is practical for improving the segmentation effect of road garbage.

The relationship between intellectual property rights (IPRs) and the development of creativity is always a controversial topic. However, it has seldom been explored from the user-centered design (UCD) perspective. This paper describes how the UCD approach has been employed to develop Design Patent Retrieval Application (acronym: DsPLAi), a mobile app aimed to integrate IPRs related information into early design processes to enhance designers’ IP practice and to facilitate the creative process. Interview studies were first conducted to identify end-users’ understanding of IPRs and related practices. Next, participatory design workshops with designers and IP processionals were organized to understand the interaction between the two parties and their needs, thereby deriving requirements for DsPLAi. A prototype of the app was developed and evaluated with ten industrial designers. The prototype received positive feedback in the usability evaluation. The empirical results showed that the provision of IPRs related information at an early stage could be helpful to the design process and that the designers were positive about the use of DsPLAi in their daily design routines.

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the U-I Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD's characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results.

Previous studies have investigated the experiences and characteristics of holiday decision-making among groups of travelers. This study adds to the knowledge of group trip holiday planning through exploring influential factors (including the individual and group characteristics of travelers), and linking those with their intragroup interactions when planning a group trip. A total of 261 usable questionnaires were collected across two university campuses in the UK and China. The survey employed a retrospective approach, asking participants to recall one of their past group trip planning experiences within the previous 3 months. This study found that intragroup interactions during a group trip planning process are influenced both by tourists’ individual factors, such as age, gender, and nationality, and by group characteristics, such as group size, common interest, group type, and group travel style. This study shows that common interest is the most influential factor in terms of its positive impact on group collaboration, feeling of connectedness, strength of preparation, and flexibility and spontaneity during group trip planning process. Further, in general, Chinese groups tend to spend less time on planning their trips before departure, but focus more on the details of the itinerary. Finally, the implications for technologies that are designed to facilitate the group trip planning process, with a view to enhancing the level of group enjoyment, are discussed based on the findings in this study.

Purpose This paper presents an initial development of a personal data attitude (PDA) measurement instrument based on established psychometric principles. The aim of the research was to develop a reliable measurement scale for quantifying and comparing attitudes towards personal data that can be incorporated into cybersecurity behavioural research models. Such a scale has become necessary for understanding individuals’ attitudes towards specific sets of data, as more technologies are being designed to harvest, collate, share and analyse personal data. Design/methodology/approach An initial set of 34 five-point Likert-style items were developed with eight subscales and administered to participants online. The data collected were subjected to exploratory and confirmatory factor analyses and MANOVA. The results are consistent with the multidimensionality of attitude theories and suggest that the adopted methodology for the study is appropriate for future research with a more representative sample. Findings Factor analysis of 247 responses identified six constructs of individuals’ attitude towards personal data: protective behaviour, privacy concerns, cost-benefit, awareness, responsibility and security. This paper illustrates how the PDA scale can be a useful guide for information security research and design by briefly discussing the factor structure of the PDA and related results. Originality/value This study addresses a genuine gap in research by taking the first step towards establishing empirical evidence for dimensions underlying personal data attitudes. It also adds a significant benchmark to a growing body of literature on understanding and modelling computer users’ security behaviours.

Advances in ubiquitous technologies have changed the way humans interact with the world around them. Technology has the power not only to inform and perform but also to further peoples' experiences of the world. It has enhanced the methodological approaches within the CHI research realm in terms of data gathering (e.g. via wearable sensors) and sharing (e.g. via self-reflection methods). While such methodologies have been mainly adopted in isolation, exploring the implications and the synergy of them has yet to be fully explored. This workshop brings together a multidisciplinary group of researchers to explore and experience the use of wearable sensors with self-reflection as a multi-method approach to conduct research and fully experience the world on-the-go.