smart building engineers

Abstract: Multimodal 3D Human Pose Estimation (3D HPE) combines complementary information from RGB, LiDAR, and mmWave radar, but models trained on correlated observations from the same individuals, raise privacy risks overlooked by record level analysis. We present a unified framework for multimodal 3D HPE that couples kinematics-induced sensor fusion with subject level privacy auditing and private training. First, our multimodal model aligns modality specific joint representation, injects skeletal structure and adaptively aggregates complementary sensor evidence for accurate pose prediction. Second, we formulate a black-box subject membership inference attack for 3D HPE, complemented by an empirical pointwise maximal leakage analysis, which characterizes how individual attack score outcomes change inference about the membership outcome. Third, we instantiate user-level differential privacy via Action Temporal Stratification, a population weighted within-subject sampling strategy that enforces action and temporal coverage. We evaluate our framework on the MM-Fi dataset across three diverse experimental protocols. Source-code will be released upon acceptance.

Abstract: As AI increasingly assumes operational control, human-computer interaction is shifting from operating systems through explicit interfaces to inhabiting intelligent environments. This raises a fundamental question: when users no longer directly manipulate a system, what mediates their relationship with intelligent technologies? We introduce environmental interfaces: designed environmental conditions that shape human-AI relationships through ambient, holistic, and evaluative pathways rather than explicit functional interaction. Using autonomous vehicle cabins as a revealing context, we conducted a within-subject experiment with 24 participants (216 observations), manipulating lighting and scent in a simulated autonomous driving environment. Three findings emerged. First, perceived atmosphere accounted for 66.5% of the variance in overall journey experience, showing that environmental conditions can function as an interface rather than merely a supporting design element. Second, multisensory processing followed a hierarchical architecture: individual sensory appraisals were initially independent, while cross-modal integration emerged during higher-order environmental evaluation. Third, olfactory stimuli influenced affective responses and experience evaluation more strongly than visual stimuli, challenging the visual dominance of automotive interaction design. Environmental quality and sensory congruency also predicted trust in the autonomous system, suggesting that passengers may use environmental cues as proxy signals when direct assessment of AI competence is difficult. These findings establish environmental interfaces as a distinct interaction modality and point to a broader transition from designing interfaces for operating intelligent systems to designing environments for inhabiting them.

Abstract: Digital twins (DTs) have emerged as a key technology at the core of digital transformation, yet their engineering practice remains too narrowly focused on engineered and natural systems. This paper argues that four system dimensions must be explicitly recognized in DT engineering: Engineered, Natural/Biological, Human, and Social. Each dimension brings distinct properties, modeling requirements, and ethical obligations that fundamentally shape what a DT must represent and how it must be built. We further argue that as DTs extend into the Human and Social dimensions, the emphasis should shift from automated control toward decision support and occupant empowerment. We illustrate our arguments using a smart building as a running example and identify four open research challenges for multi-dimensional DT engineering.

Abstract: Embodied agents need to convey information to surrounding infrastructure, but their active communication interfaces may be unavailable, constrained, or intentionally inactive.Their ability to manipulate physical states offers a complementary path: messages can be encoded in deliberately selected configurations and recovered through infrastructure sensing. This principle underlies embodied communication. Yet physical differences do not guarantee distinguishable messages: a single sensing viewpoint may leave ambiguities that repeated sensing cannot resolve. This paper develops networked embodied communication, where distributed access points (APs) jointly observe message-bearing scatterer positions under fixed illumination.Under a correlated Gaussian sensing model, we characterize the additional distinguishability supplied by receive APs, establish exact redundancy conditions, and reveal how distinctions absent from individual observations can emerge through cross-AP statistical relationships. We then establish the exact asymptotic optimal maximum-error behavior of a finite alphabet under repeated independent sensing. The largest group of indistinguishable messages determines the error floor; once all messages are distinguishable, the minimum pairwise Chernoff information determines the error exponent. For a given alphabet, receiver cooperation can therefore eliminate an error floor that repetition at any individual AP cannot overcome. Building on these results, we derive finite-budget reliability conditions and jointly design the receive AP set and message-bearing positions. Numerical results show that the proposed search closely approaches exact benchmarks on reduced instances with substantially fewer candidate evaluations than exhaustive enumeration, while receiver cooperation reduces the sensing intervals needed to guarantee reliable decoding.