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Wireless body area networks (WBAN) consist of resource constrained sensing devices just like other wireless sensor networks (WSN). However, they differ from WSN in topology, scale and security requirements. Due to these differences, key management schemes designed for WSN are inefficient and unnecessarily complex when applied to WBAN. Considering the key management issue, WBAN are also different from WPAN because WBAN can use random biometric measurements as keys. We highlight the differences between WSN and WBAN and propose an efficient key management scheme, which makes use of biometrics and is specifically designed for WBAN domain.

Background: Verifying the exact location of talocalcaneal (TC) coalition is important for surgery, but the complicated anatomy of the subtalar joint makes it difficult to visualize on radiographs. No study has used computed tomography (CT) or magnetic resonance imaging (MRI) to verify the radiological characteristics of TC coalition or those of different facet coalitions. Therefore, this study verified the radiological findings used to identify TC coalitions and those of different facet coalitions using CT and MRI. Materials and Methods: Plain with/without weight bearing anteroposterior and lateral radiographs, CT, and MRI of 43 feet in 39 patients with TC coalitions were reviewed retrospectively. CT or MRI was used to verify the location of the TC coalition. Secondary signs for the presence of a coalition in the anteroposterior and lateral plain radiographs, including talar beak, humpback sign, duck-face sign, and typical or deformed C-sign, were evaluated. Three independent observers evaluated the radiographs twice at 6-week intervals to determine intraobserver reliability. They examined the radiographs for the secondary signs, listed above, and coalition involved facets. Results: The average rates from both assessments were as follows: Middle facet 5%, middle and posterior facets 27%, and posterior facet 68%. The deformed C-sign is more prevalent in posterior facet coalitions. The posterior facet has the highest prevalence of involvement in TC coalitions, and the deformed C-sign and duck-face sign have high correlations with TC coalitions in the posterior subtalar facet. Conclusion: A posterior facet is the most prevalent for TC coalition, and the C-sign is useful for determining all types of TC coalition. [ABSTRACT FROM AUTHOR]

Normally, individuals use smartphones for a variety of purposes like photography, schedule planning, playing games, and so on, apart from benefiting from the core tasks of call-making and short messaging. These services are sources of personal data generation. Therefore, any application that utilises personal data of a user from his/her smartphone is truly a great witness of his/her interests and this information can be used for various personalised services. In this paper, we present Lifestyle Pattern MIning (LPaMI), which is a personalised application for mining the lifestyle patterns of a smartphone user. LPaMI uses the personal photograph collections of a user, which reflect the day-to-day photos taken by a smartphone, to recognise scenes (called objects of interest in our work). These are then mined to discover lifestyle patterns. The uniqueness of LPaMI lies in our graph-based approach to mining the patterns of interest. Modelling of data in the form of graphs is effective in preserving the lifestyle behaviour maintained over the passage of time. Graph-modelled lifestyle data enables us to apply variety of graph mining techniques for pattern discovery. To demonstrate the effectiveness of our proposal, we have developed a prototype system for LPaMI to implement its end-to-end pipeline. We have also conducted an extensive evaluation for various phases of LPaMI using different real-world datasets. We understand that the output of LPaMI can be utilised for variety of pattern discovery application areas like trip and food recommendations, shopping, and so on.

Sensor-based activity recognition is attracting growing attention in many applications. Several studies have been performed to analyze activity patterns from an activity database gathered by activity recognition. Activity pattern classification is a technique that predicts class labels of people such as individual identification, nationalities, and jobs. For this classification problem, it is important to mine discriminative features reflecting the intrinsic patterns of each individual. In this paper, we propose a framework that can classify activity patterns effectively. We extensively analyze activity models from a classification viewpoint. Based on the analysis, we represent activities as activity graphs by combining every combination of daily activity sequences in meaningful periods. Frequent patterns over these activity graphs can be used as discriminative features, since they reflect people's intrinsic lifestyles. Experiments show that the proposed method achieves high classification accuracy compared with existing graph classification techniques.

Graph classification is an important topic in graph mining research since it has many applications, such as, social web mining, function prediction of molecules for drug design, XML document classification and anomaly detection in program flows. The key difficulty in graph classification lies in selecting a subset of optimal features from a huge number of structural features. The features need to be highly discriminative and small in numbers for better classification accuracy and running time. In this paper, we propose a novel feature selection framework that selects an optimal feature subset by removing redundant subgraphs. Topologically similar subgraphs have similar discriminative powers and coverage. We cluster these subgraphs and select one subgraph as a feature. We also propose an efficient topological similarity-based clustering method that guarantees the efficiency of our framework. Empirical results showthat the proposed framework achieves significantly improved classification accuracy and running time in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. In this paper, we propose three novel tree structures to efficiently perform incremental and interactive HUP mining. The first tree structure, Incremental HUP Lexicographic Tree (IHUPL-Tree), is arranged according to an item's lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP Transaction Frequency Tree (IHUPTF-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-Transaction-Weighted Utilization Tree (IHUPTWU-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining. [ABSTRACT FROM AUTHOR]