OBJECTIVE: To develop and evaluate a time-aware data mining pipeline that integrates accelerometry-based Physical Activity (PA), static dietary intake, and sociodemographic factors to predict Healthy Weight Status (HWS), and identify features with predictive importance for HWS. METHODS: We propose TimePAD, Time-Based Physical Activity and Dietary Intake data mining, a three-stage prediction pipeline that integrates time-based PA representations, static dietary intake, and sociodemographic variables to predict HWS categories. Stage 1 derives intensity-specific hourly PA sequences and learns time-of-day PA embeddings using a Transformer encoder trained with masked Self-Supervised Learning (SSL) reconstruction. Stage 2 combines the learnt PA embeddings with static dietary variables and participant characteristics derived from the Food Frequency Questionnaire (FFQ). Stage 3 trains prediction models and ranks predictors by their predictive importance for HWS. TimePAD was evaluated on a real-world dataset of 206 adolescents (10-16 years) with 7-day continuous wrist-worn accelerometer data, FFQ records, and sociodemographic attributes, using 10-fold cross-validation and comparison to an ARIMA-based feature engineering baseline. RESULTS: TimePAD achieves an accuracy of 82.90% and an F1-score of 67.92% for HWS prediction, outperforming the best baseline (ARIMA-based PA features + diet, 77.51% accuracy). Across experiments, time-of-day light PA (LPA) features were consistently ranked as important predictors. Other key features include time-of-day Moderate-to-Vigorous Activity (MVPA) and Sedentary (SED) levels, Tanner stage, total weekly sleep time, age (in months), weekly fruit intake, weekly vegetable and legume intake, and intake of sugar-sweetened beverages. DISCUSSION: TimePAD contributes a pipeline for learning from the time-of-day structure in wearable PA time series and integrating it with static dietary and contextual data for prediction and feature analysis. The findings suggest that LPA is likely to have a significant association with HWS, calling for further attention and investigation to better understand the role of LPA in overall health outcomes. This illustrates the potential benefits of TimePAD in modelling PA with dietary intake context in shaping healthy behaviours.

Soft-thresholding has been widely used in neural networks. Its basic network structure is a two-layer convolution neural network with soft-thresholding. Due to the network's nature of nonlinear and nonconvex, the training process heavily depends on an appropriate initialization of network parameters, resulting in the difficulty of obtaining a globally optimal solution. To address this issue, a convex dual network is designed here. We theoretically analyze the network convexity and prove that the strong duality holds. Extensive results on both simulation and real-world datasets show that strong duality holds, the dual network does not depend on initialization and optimizer, and enables faster convergence than the state-of-the-art two-layer network. This work provides a new way to convexify soft-thresholding neural networks. Furthermore, the convex dual network model of a deep soft-thresholding network with a parallel structure is deduced.

Efficient visual perception using mobile systems is crucial, particularly in unknown environments such as search and rescue operations, where swift and comprehensive perception of objects of interest is essential. In such real-world applications, objects of interest are often situated in complex settings, making the selection of the 'Next Best' view based solely on maximizing visibility gain suboptimal. We argue that incorporating semantics-providing a higher-level interpretation of perception-can significantly contribute to the selection of viewpoints for various perception tasks. In this study, we formulate a novel information gain that integrates both visibility and semantic gain in a unified form to select the semantic-aware Next-Best-View. We also design an adaptive strategy with termination criterion to facilitate the two-stage search-and-acquisition manoeuvre on multiple objects of interest aided by a multi-degree-of-freedoms (Multi-DoFs) mobile system. To evaluate our approach, we introduce several semantically relevant reconstruction metrics, including perspective directivity and the region of interest (ROI)-to-full reconstruction volume ratio. Simulation experiments demonstrate that our approach outperforms the existing methods by up to 27.46% in the ROI-to-full reconstruction volume ratio and 0.88234 in average perspective directivity. Furthermore, the planned motion trajectory exhibits better perceiving coverage toward the target.

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present InaudibleKey, an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey exploits the acoustic channel frequency response of two legitimate devices as a common secret to generating keys. InaudibleKey employs several novel technologies to significantly improve its performance. We conduct extensive experiments to evaluate the proposed system in different real environments. Compared to state-of-the-art works, InaudibleKey improves key generation rate by 3 times, extends pairing distance by 3.2 times, and reduces information reconciliation counts by 2.5 times. Security analysis demonstrates that InaudibleKey is resilient to a number of malicious attacks. We also implement InaudibleKey on modern smartphones and resource-limited IoT devices. Results show that it is energy-efficient and can run on both powerful and resource-limited IoT devices without incurring excessive resource consumption.

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, our system exploits channel frequency response of two legitimate devices as a common secret to generate keys. Extensive experiments are conducted to evaluate the proposed system in different real environments. Evaluation results show that the proposed system can generate the same secret key for two mobile devices with high probability.