ABSTRACT The core challenge of fine‐grained image recognition (FGIR) tasks is distinguishing highly similar subclasses within the same base category. Most CNN‐based deep learning methods typically focus on extracting information from local regions while overlook the inherent structure between subclasses and the complex relationships between features. This paper presents a two‐stage feature selection method based on partial order analysis (POA) and heterogeneity evaluation (HE) for FGIR tasks, guiding the model to focus on distinctive features while reducing uncertainty caused by interfering information. Specifically, in the POA stage, clustering first groups similar subcategories into a medium‐granularity category. Formal concept analysis then models their hierarchical partial order, identifying “shared features” among subcategories and “exclusive features” unique to each. This structured representation highlights key contrastive cues. In the HE stage, a novel heterogeneity index is introduced to measure the fluctuation of low‐level features within each fine‐grained category. This index guides the model to suppress pseudo‐discriminative features with high heterogeneity, mitigating the impact of noisy and unstable information on decision‐making. We perform comprehensive experiments on three commonly used benchmark datasets (CUB‐200‐2011, Stanford Cars, and FGVC‐Aircraft). Experimental results show that the proposed method outperforms classic FGIC methods, validating the effectiveness of our approach.

Mobile wayfinding and guide apps have become indispensable tools for navigating unfamiliar urban spaces. Such applications address targeted, "just-in-time" queries, but are not optimally designed for multi-point expeditions that can quickly build route and survey-level familiarity with a neighbourhood. We first conducted an experimental simulation involving a homebuying scenario to assess the usefulness of a popular mobile wayfinding and search application (Google Maps) for exploring a neighbourhood. We then designed a prototype application called Block Party that addresses a number of limitations of Google Maps for this purpose, and evaluated it in a second replica study. The results suggested that application designs that facilitate switching among distinct but synchronized navigation views such as Block Party might support more efficient usage and the selection of task-appropriate views, leading to better overall spatial awareness.

Given the widespread adoption of mobile devices and the private personal and work information they carry, casual or deliberate shoulder surfing is an increasing concern with these devices. We iteratively designed a tablet interface that detects when people nearby are looking at the screen, providing awareness through glyph notifications, and response through visual protections, and evaluated its use in two experiments. The results indicate that mobile content privacy management systems such as ours could help alleviate the cognitive and social burden of managing mobile device privacy in dynamic settings. We identify physical privacy behaviours and preferences that can inform the design of privacy notification and management protocols on mobile devices. We argue that such systems require subtlety so as not to advertise the users' intention for privacy, flexibility in addressing dynamic privacy needs and trustworthiness to promote adoption.