Smart homes equipped with ambient sensors enable privacy conscious monitoring of Activities of Daily Living (ADLs), but produce event-triggered sensor logs that differ fundamentally from regularly sampled time series. These data are discrete, symbolic, irregular, and spatially grounded, exhibiting strong structural dependencies across sensor states and locations. Collecting sufficiently diverse labeled data with adequate structural coverage remains challenging, motivating the need for realistic synthetic data generation. Existing time-series generation methods are designed for continuous or regularly sampled temporal signals and are poorly aligned with smart-home sensor data, where realism fundamentally requires jointly preserving coherent statistical patterns, physical feasibility, and activity-level semantic consistency. We propose ADLGen, a unified framework for synthesizing symbolic, event-triggered sensor sequences. ADLGen integrates a representation tailored to symbolic event data, a generation process that enforces spatial constraints while balancing diversity and coherence, and an LLM-based semantic evaluation and refinement stage for identifying and correcting behavioral inconsistencies, with efficient offline deployment through compact local models. Experiments show that ADLGen synthesizes sequences that closely match real data while improving intrinsic realism, downstream activity recognition, rare-activity learning, and cross-home generalization over strong baselines.

With the rapid development of facial recognition, the research field of near infrared (NIR) face recognition, which is less sensitive to illumination levels, has attracted increased attention. Unfortunately, directly applying the face recognition model trained using visible light (VIS) data to NIR face data does not produce a satisfactory performance. This is due to the domain bias between the NIR images and the VIS images. To this end, we created the Outdoor NIR-VIS Face (ONVF) database and Indoor NIR Face (INF) database to increase the number of near infrared facial images for system training and evaluation. In this paper, we propose an efficient NIR face recognition method, which consists of face detection and alignment, NIR-VIS image translation and face embedding. The NIR-VIS image conversion model is capable of transforming near-infrared facial images into their corresponding VIS images whilst maintaining sufficient identity information to enable existing VIS facial recognition models to perform recognition. Extensive experiments using the INF dataset and the CSIST database have demonstrated that the proposed method yields a consistent and competitive performance for near infrared face recognition.