Virtual assistants (VAs) are increasingly positioned not just as tools, but as potential social companions—capable of offering either emotional or informational support. Yet, how these forms of support should adapt to varying task difficulties and embodiment styles remains underexplored. We conducted two user studies with cognitive and physical tasks to investigate how support type (emotional vs. informational) shapes user perceptions across variations in task difficulty (easy vs. hard) and embodiment (non-embodied vs. embodied). In Study 1, emotional support positively influenced users’ impressions of VA in easy tasks, while informational support was more effective in difficult tasks. In Study 2, participants also preferred emotional support for easy tasks, but differences between support types were less pronounced for difficult tasks. Notably, embodiment exerted no significant influence in either study. These findings underscore the role of context in shaping effective support strategies, offering design insights for VAs as social companions.

Collage is a creative art form that uses diverse material scraps as a base unit to compose a single image. Although pixel-wise generation techniques can reproduce a target image in collage style, it is not a suitable method due to the solid stroke-by-stroke nature of the collage form. While some previous works for stroke-based rendering produced decent sketches and paintings, collages have received much less attention in research despite their popularity as a style. In this paper, we propose a method for learning to make collages via reinforcement learning without the need for demonstrations or collage artwork data. We design the collage Markov Decision Process (MDP), which allows the agent to handle various materials and propose a model-based soft actor-critic to mitigate the agent’s training burden derived from the sophisticated dynamics of collage. Moreover, we devise additional techniques such as active material selection and complexity-based multi-scale collage to handle target images at any size and enhance the results’ aesthetics by placing relatively more scraps in areas of high complexity. Experimental results show that the trained agent appropriately selected and pasted materials to regenerate the target image into a collage and obtained a higher evaluation score on content and style than pixel-wise generation methods. Code is available at https://github.com/northadventure/CollageRL.

Temporal data are prevalent, where one or several time attributes present. It is challenging to identify the temporal attributes from heterogeneous sources. The reason is that the same attribute could contain distinct values in different time spans, whereas different attributes may have highly similar timestamps and alike values. Existing studies on schema matching seldom explore the temporal information for matching attributes. In this paper, we argue to order the values in an attribute A by some time attribute T as a time series. To learn deep temporal features in the attribute pair (T, A), we devise an auto-encoder to embed the transitions of values in the time series into a vector. The temporal attribute matching (TAM) is thus to evaluate matching distance of two temporal attribute pairs by comparing their transition vectors. We show that computing the optimal matching distance is NP-hard, and present an approximation algorithm. Experiments on real datasets demonstrate the superiority of our proposal in matching temporal attributes compared to the generic schema matching approaches.

Structured entities are commonly abstracted, such as from XML, RDF or hidden-web databases. Direct retrieval of various structured entities is highly demanded in data lakes, e.g., given a JSON object, to find the XML entities that denote the same real-world object. Existing approaches on evaluating structured entity similarity emphasize too much the structural inconsistency. Indeed, entities from heterogeneous sources could have very distinct structures, owing to various information representation conventions. We argue that the retrieval could be more tolerant to structural differences and focus more on the contents of the entities. In this paper, we first identify the unique challenge of parent-child (containment) relationships among structured entities, which unfortunately prevent the retrieval of proper entities (returning parents or children). To solve the problem, a novel hierarchy smooth function is proposed to combine the term scores in different nodes of a structured entity. Entities sharing the same structure, namely an entity family, are employed to learn the coefficient in aggregating the scores, and thus distinguish/prune the parent or child entities. Remarkably, the proposed method could cooperate with both the bag-of-words (BOW) and word embedding models, successful in retrieving unstructured documents, for querying structured entities. Extensive experiments on real datasets demonstrate that our proposal is effective and efficient.