Due to privacy concerns, open dialogue datasets for mental health are primarily generated through human or AI synthesis methods. However, the inherent implicit nature of psychological processes, particularly those of clients, poses challenges to the authenticity and diversity of synthetic data. In this paper, we propose ECAs (short for Embodied Conversational Agents), a framework for embodied agent simulation based on Large Language Models (LLMs) that incorporates multiple psychological theoretical principles. Using simulation, we expand real counseling case data into a nuanced embodied cognitive memory space and generate dialogue data based on high-frequency counseling questions. We validated our framework using the D4 dataset. First, we created a public ECAs dataset through batch simulations based on D4. Licensed counselors evaluated our method, demonstrating that it significantly outperforms baselines in simulation authenticity and necessity. Additionally, two LLM-based automated evaluation methods were employed to confirm the higher quality of the generated dialogues compared to the baselines.

The fourth workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, eXplainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a key concern of Responsible and Human-Centred AI, emphasising HCI techniques that make opaque AI models more understandable to people. XAIxArts offers a distinctive lens to examine explainability through creative and artistic domains. The previous workshops explored the landscape and the speculative futures of AI in creative processes. To respond to emerging challenges and contribute to creative and societal transformation more broadly, this workshop focuses on the operationalisation of XAI in the Arts. Specifically, we will: i) critically reflect on emerging practices that encourage diversity and inclusivity in XAI; ii) collectively ideate a library of missing projects to encourage future collaborations and speculations; iii) scope the development of a resource hub for open XAIxArts projects to archive tangible XAI interventions and facilitate future community building with the wider discourse on Human-Centred AI.

The third workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts.XAI is a key concern of Responsible and Human-Centred AI, emphasising the use of HCI techniques to explore how to make complicated and opaque AI models more understandable to people.The previous workshops moved from mapping the landscape of XAI for the Arts to co-developing an XAIxArts manifesto.To continue driving discourse on XAIxArts, the anticipated outcomes of this workshop are: i) fresh insights into the evolving challenges of AI bias, lack of transparency and barriers to inclusivity through discussion of current and emerging XAIxArts practices; ii) co-developed speculative futures which expand XAIxArts discourse beyond post-hoc rationalisations of AI decisions into the imaginative possibilities of AI as an interlocutor in the creative process; iii) plans for a co-developed proposal of an edited book on XAIxArts; and iv) community expansion and engagement in wider discourses on Responsible and Human-Centred AI.

Assigning discriminable and harmonic colors to samples according to their class labels and spatial distribution can generate attractive visualizations and facilitate data exploration. However, as the number of classes increases, it is challenging to generate a high-quality color assignment result that accommodates all classes simultaneously. A practical solution is to organize classes into a hierarchy and then dynamically assign colors during exploration. However, existing color assignment methods fall short in generating high-quality color assignment results and dynamically aligning them with hierarchical structures. To address this issue, we develop a dynamic color assignment method for hierarchical data, which is formulated as a multi-objective optimization problem. This method simultaneously considers color discriminability, color harmony, and spatial distribution at each hierarchical level. By using the colors of parent classes to guide the color assignment of their child classes, our method further promotes both consistency and clarity across hierarchical levels. We demonstrate the effectiveness of our method in generating dynamic color assignment results with quantitative experiments and a user study.

This second workshop on explainable AI for the Arts (XAIxArts) brings together a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a core concern of Human-Centred AI and relies heavily on HCI techniques to explore how to make complex and difficult to understand AI models more understandable to people. Our first workshop explored the landscape of XAIxArts and identified emergent themes. To move the discourse on XAIxArts forward and to contribute to Human-Centred AI more broadly this workshop will: i) bring researchers together to expand the XAIxArts community; ii) collect and critically reflect on current and emerging XAIxArts practice; iii) co-develop a manifesto for XAIxArts; iv) co-develop a proposal for an edited book on XAIxArts; v) engage with the wider discourse on Human-Centred AI.

The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually difficult to understand the underlying causes and make improvements. To tackle this issue, we propose a visual analysis method, FSLDiagnotor. Given a set of base learners and a collection of samples with a few shots, we consider two problems: 1) finding a subset of base learners that well predict the sample collections; and 2) replacing the low-quality shots with more representative ones to adequately represent the sample collections. We formulate both problems as sparse subset selection and develop two selection algorithms to recommend appropriate learners and shots, respectively. A matrix visualization and a scatterplot are combined to explain the recommended learners and shots in context and facilitate users in adjusting them. Based on the adjustment, the algorithm updates the recommendation results for another round of improvement. Two case studies are conducted to demonstrate that FSLDiagnotor helps build a few-shot classifier efficiently and increases the accuracy by 12% and 21%, respectively.