University of Macau

About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.

SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations

Abstract: The problem of formulating solutions immediately and comparing them rapidly for billboard placements has plagued advertising planners for a long time, owing to the lack of efficient tools for in-depth analyses to make informed decisions. In this study, we attempt to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle this problem using large-scale GPS trajectory data. In particular, we present SmartAdP, an interactive visual analytics system that deals with the two major challenges including finding good solutions in a huge solution space and comparing the solutions in a visual and intuitive manner. An interactive framework that integrates a novel visualization-driven data mining model enables advertising planners to effectively and efficiently formulate good candidate solutions. In addition, we propose a set of coupled visualizations: a solution view with metaphor-based glyphs to visualize the correlation between different solutions; a location view to display billboard locations in a compact manner; and a ranking view to present multi-typed rankings of the solutions. This system has been demonstrated using case studies with a real-world dataset and domain-expert interviews. Our approach can be adapted for other location selection problems such as selecting locations of retail stores or restaurants using trajectory data.

Recurrent Broad Learning Systems for Time Series Prediction

Abstract: The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series. A sparse autoencoder is used to extract the features from the input instead of the randomly initialized weights. In this way, the RBLS retains the merit of fast computing and fits for processing sequential data. Motivated by the idea of “fine-tuning” in deep learning, the weights in the RBLS can be updated by conjugate gradient methods if the prediction errors are large. We exhibit the merits of our proposed model on several chaotic time series. Experimental results substantiate the effectiveness of the RBLS. For chaotic benchmark datasets, the RBLS achieves very small errors, and for the real-world dataset, the performance is satisfactory.

Formulation Development and Bioavailability Evaluation of a Self-Nanoemulsified Drug Delivery System of Oleanolic Acid

Abstract: This study aims to formulate and evaluate bioavailability of a self-nanoemulsified drug delivery system (SNEDDS) of a poorly water-soluble herbal active component oleanolic acid (OA) for oral delivery. Solubility of OA under different systems was determined for excipient selection purpose. Four formulations, where OA was fixed at the concentration of 20 mg/g, were prepared utilizing Sefsol 218 as oil phase, Cremophor EL and Labrasol as primary surfactants, and Transcutol P as cosurfactant. Pseudo-ternary phase diagrams were constructed to identify self-emulsification regions for the rational design of SNEDDS formulations. Sefsol 218 was found to provide the highest solubility among all medium-chained oils screened. Efficient self-emulsification was observed for the systems composing of Cremophor EL and Labrasol. The surfactant to cosurfactant ratio greatly affected the droplet size of the nanoemulsion. Based on the outcomes in dissolution profiles, stability data, and particle size profiles, three optimized formulations were selected: Sefsol 218/Cremophor EL/Labrasol (50:25:25, w/w), Sefsol 218/Cremophor EL/Labrasol/Transcutol P (50:20:20:10, w/w), and Sefsol 218/Cremophor EL/Labrasol/Transcutol P (50:17.5:17.5:15, w/w). Based on the conventional dissolution method, a remarkable increase in dissolution was observed for the SNEDDS when compared with the commercial tablet. The oral absorption of OA from SNEDDS showed a 2.4-fold increase in relative bioavailability compared with that of the tablet (p < 0.05), and an increased mean retention time of OA in rat plasma was also observed compared with that of the tablet (p < 0.01). These results suggest the potential use of SNEDDS to improve dissolution and oral bioavailability for poorly water-soluble triterpenoids such as OA.

Accelerometer-Based Gait Recognition by Sparse Representation of Signature Points With Clusters

Abstract: Gait, as a promising biometric for recognizing human identities, can be nonintrusively captured as a series of acceleration signals using wearable or portable smart devices. It can be used for access control. Most existing methods on accelerometer-based gait recognition require explicit step-cycle detection, suffering from cycle detection failures and intercycle phase misalignment. We propose a novel algorithm that avoids both the above two problems. It makes use of a type of salient points termed signature points (SPs), and has three components: 1) a multiscale SP extraction method, including the localization and SP descriptors; 2) a sparse representation scheme for encoding newly emerged SPs with known ones in terms of their descriptors, where the phase propinquity of the SPs in a cluster is leveraged to ensure the physical meaningfulness of the codes; and 3) a classifier for the sparse-code collections associated with the SPs of a series. Experimental results on our publicly available dataset of 175 subjects showed that our algorithm outperformed existing methods, even if the step cycles were perfectly detected for them. When the accelerometers at five different body locations were used together, it achieved the rank-1 accuracy of 95.8% for identification, and the equal error rate of 2.2% for verification.