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This paper evaluates a recently developed hybrid method for the embodied energy analysis of the Australian construction industry. It was found that the truncation associated with process analysis can be up to 80%, whilst the use of input-output analysis alone does not always provide a perfect model for replacing process data. There is also a considerable lack in the quantity and possibly quality of process data currently available. These findings suggest that current best-practice methods are sufficiently accurate for most typical applications, but this is heavily dependant upon data quality and availability. The hybrid method evaluated can be used for the optimisation of embodied energy and for identifying opportunities for improvements in energy efficiency.

With the gradual popularity of Android apps, smartphones have become an important source of privacy. While malicious apps are becoming increasingly rampant, even some seemingly ordinary apps may leak your private data at any time, so identifying and detecting malware plays an important role in mobile security.

However, existing deep learning-based malware approaches suffer from poor scalability and high experimental costs. This is due to the diverse and complex detection steps, especially in the software analysis and feature extraction phases.To solve the above problems, we propose a highly scalable full-process automation platform-ExpandDetector-which simplifies the analysis process of the original program by a custom repackaged framework, generating good feature forms to facilitate later construction of datasets and analysis work.

Finally, we tested on the malware dataset CIC-AAGM2017, and ExpandDetector does not exceed 5% of the original in size after repackaging. With only static features extracted, ExpandDetector analyzes a larger (up to 60MB in size) and a smaller (In the case of extracting only static features, ExpandDetector takes about 15 seconds and 3 seconds to perform a complete analysis of a larger (up to 60MB in size) and a smaller (up to 30MB in size) individually. In cases where both static and dynamic features need to be extracted, ExpandDetector outperforms existing methods by 5% to 15% in terms of the completeness of the extracted features.

In this paper we report our experience in solving min cost flow problems approximately by transforming them to network analysis problems. In the process we solve large (of the order of a million nodes) resistive networks. The preconditioned conjugate gradient (PCG) method appears the most suitable for this problem but runs into convergence difficulties if the conductance values have the high range of 1 – 10⁸. We solve this problem by developing a variation of the PCG (which is described in the paper) and using it to solve hybrid analysis equations of the network. This suggests a relook at commonly used algorithms in computational linear algebra by associating an electrical network with the linear equations in question. In order to make the paper self contained we give a formal description of commonly used network analysis procedures such as nodal, loop and hybrid analysis.