Spatiotemporal Mobility Based Trajectory Privacy-Preserving Algorithm in Location-Based Services.

Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user's trajectory privacy while providing high quality services. Trajectory k-anonymity is one of the most important technologies to protect the user's trajectory privacy. However, the user's attributes are rarely considered when constructing the k-anonymity set. It results in that the user's trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user's attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user's attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal k-anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided.

Ecology and Biodiversity Ontology Alignment for Smart Environment via Adaptive Compact Evolutionary Algorithm.

Smart Environment (SE) focuses on the initiatives for healthy living, where ecological issues and biodiversity play a vital role in the environment and sustainability. To manage the knowledge on ecology and biodiversity and preserve the ecosystem and biodiversity simultaneously, it is necessary to align the data entities in different ecology and biodiversity ontologies. Since the problem of Ecology and Biodiversity Ontology Alignment (EBOA) is a large-scale optimization problem with sparse solutions, finding high-quality EBOA is an open challenge. Evolutionary Algorithm (EA) is a state-of-the-art technique in the ontology aligning domain, and this study further proposes an Adaptive Compact EA (ACEA) to address the problem of EBOA, which uses semantic reasoning to reduce searching space and adaptively guides searching direction to improve the algorithm's performance. In addition, we formally model the problem of EBOA as a discrete optimization problem, which maximizes the alignment's completeness and correctness through determining an optimal entity corresponding set. After that, a hybrid entity similarity measure is presented to distinguish the heterogeneous data entities, and an ACEA-based aligning technique is proposed. The experiment uses the famous Biodiversity and Ecology track to test ACEA's performance, and the experimental results show that ACEA-based aligning technique statistically outperforms other EA-based and state-of-the-art aligning techniques.

TVGG Dental Implant Identification System.

Identifying the right accessories for installing the dental implant is a vital element that impacts the sustainability and the reliability of the dental prosthesis when the medical case of a patient is not comprehensive. Dentists need to identify the implant manufacturer from the x-ray image to determine further treatment procedures. Identifying the manufacturer is a high-pressure task under the scaling volume of patients pending in the queue for treatment. To reduce the burden on the doctors, a dental implant identification system is built based on a new proposed thinner VGG model with an on-demand client-server structure. We propose a thinner version of VGG16 called TVGG by reducing the number of neurons in the dense layers to improve the system's performance and gain advantages from the limited texture and patterns in the dental radiography images. The outcome of the proposed system is compared with the original pre-trained VGG16 to verify the usability of the proposed system.

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data.

Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 min into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25-100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm.

To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment's quality, which calculate the alignment's statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm's convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)'s biomedical tracks to test aMMOEA's performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI's participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

A Comprehensive Data Gathering Network Architecture in Large-Scale Visual Sensor Networks.

The fundamental utility of the Large-Scale Visual Sensor Networks (LVSNs) is to monitor specified events and to transmit the detected information back to the sink for achieving the data aggregation purpose. However, the events of interest are usually not uniformly distributed but frequently detected in certain regions in real-world applications. It implies that when the events frequently picked up by the sensors in the same region, the transmission load of LVSNs is unbalanced and potentially cause the energy hole problem. To overcome this kind of problem for network lifetime, a Comprehensive Visual Data Gathering Network Architecture (CDNA), which is the first comparatively integrated architecture for LVSNs is designed in this paper. In CDNA, a novel α-hull based event location algorithm, which is oriented from the geometric model of α-hull, is designed for accurately and efficiently detect the location of the event. In addition, the Chi-Square distribution event-driven gradient deployment method is proposed to reduce the unbalanced energy consumption for alleviating energy hole problem. Moreover, an energy hole repairing method containing an efficient data gathering tree and a movement algorithm is proposed to ensure the efficiency of transmitting and solving the energy hole problem. Simulations are made for examining the performance of the proposed architecture. The simulation results indicate that the performance of CDNA is better than the previous algorithms in the realistic LVSN environment, such as the significant improvement of the network lifetime.

Directional virtual backbone based data aggregation scheme for Wireless Visual Sensor Networks.

Data gathering is a fundamental task in Wireless Visual Sensor Networks (WVSNs). Features of directional antennas and the visual data make WVSNs more complex than the conventional Wireless Sensor Network (WSN). The virtual backbone is a technique, which is capable of constructing clusters. The version associating with the aggregation operation is also referred to as the virtual backbone tree. In most of the existing literature, the main focus is on the efficiency brought by the construction of clusters that the existing methods neglect local-balance problems in general. To fill up this gap, Directional Virtual Backbone based Data Aggregation Scheme (DVBDAS) for the WVSNs is proposed in this paper. In addition, a measurement called the energy consumption density is proposed for evaluating the adequacy of results in the cluster-based construction problems. Moreover, the directional virtual backbone construction scheme is proposed by considering the local-balanced factor. Furthermore, the associated network coding mechanism is utilized to construct DVBDAS. Finally, both the theoretical analysis of the proposed DVBDAS and the simulations are given for evaluating the performance. The experimental results prove that the proposed DVBDAS achieves higher performance in terms of both the energy preservation and the network lifetime extension than the existing methods.