Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.

Users in online social networking sites unknowingly disclose their sensitive information that aggravate the social and financial risks. Hence, to prevent the information loss and privacy exposure, users need to find ways to quantify their privacy level based on their online social network data. Current studies that focus on measuring the privacy risk and disclosure consider only a single source of data, neglecting the fact that users, in general, can have multiple social network accounts disclosing different sensitive information. In this paper, we investigate an approach that can help social media users to measure their privacy disclosure score (PDS) based on the information shared across multiple social networking sites. In particular, we identify the main factors that have impact on users privacy, namely, sensitivity and visibility, to obtain the final disclosure score for each user. By applying the statistical and fuzzy systems, we can specify the potential information loss for a user by using obtained PDS. Our evaluation results with real social media data show that our method can provide a better estimation of privacy disclosure score for users having presence in multiple online social networks.

A key parameter affecting the operation of differential evolution (DE) is the crossover rate Cr ϵ [0, 1]. While very low values are recommended for and used with separable problems, on non-separable problems, which include most real-world problems, Cr = 0.9 has become the de facto standard, working well across a large range of problem domains. Recent work on separable and non-separable problems has shown that lower-dimensional searches can play an important role in the performance of search techniques in higher-dimensional search spaces. However, the standard value of Cr = 0.9 implies a very high-dimensional search, which is not effective for other search techniques. An analysis of Cr across its range [0, 1] provides insight into how its value affects the performance of DE and suggests how low values may be used to improve the performance of DE. This new understanding of the operation of DE at high and low crossover rates is useful for analysing how adaptive parameters affect DE performance and leads to new suggestions for how adaptive DE techniques might be developed.

With the increasing number of Internet of Things (IoT) devices, the volume and variety of data being generated by these devices are increasing rapidly. Cloud computing cannot process this data due to its high latency and scalability. In order to process this data in less time, fog computing has evolved as an extension to Cloud computing. In a fog computing environment, a resource monitoring service plays a vital role in providing advanced services such as scheduling, scaling and migration. Most of the research in fog computing has assumed that a resource monitoring service is already available. Conventional methods proposed for other distributed systems may not be suitable due to the unique features of a fog environment. To improve the overall performance of fog computing and to optimise resource usage, effective resource monitoring techniques are required. Hence, we propose a Support and Confidence based (SCB) technique which optimises the resource usage in the resource monitoring service. The performance of our proposed system is evaluated by examining a real-time traffic use case in a fog emulator with synthetic data. The experimental results obtained from the fog emulator show that the proposed technique consumes 19% lesser resources compared with the existing technique.