This paper presents a data acquisition process for solar energy generation and then analyzes the dynamics of its data stream, mainly employing open software solutions such as Python, MySQL, and R. For the sequence of hourly power generations during the period from January 2016 to March 2017, a variety of queries are issued to obtain the number of valid reports as well as the average, maximum, and total amount of electricity generation in 7 solar panels. The query result on all-time, monthly, and daily basis has found that the panel-by panel difference is not so significant in a university-scale microgrid, the maximum gap being 7.1% even in the exceptional case. In addition, for the time series of daily energy generations, we develop a neural network-based trace and prediction model. Due to the time lagging effect in forecasting, the average prediction error for the next hours or days reaches 27.6%. The data stream is still being accumulated and the accuracy will be enhanced by more intensive machine learning.

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By measuring similarity of programs, we can determine whether someone illegally copies a program from another program or not. If the similarity is significantly high, it means that a program is a copy of the other. This paper proposes three techniques to measure similarity of the Dalvik executable codes (DEXs) in the Android application Packages (APKs). Firstly, we decompile the DEXs of candidate applications into Java sources and compute the similarity between the decompiled sources. Secondly, candidate DEXs are disassembled and the similarities between disassembled codes are measured. Finally, we extract k-gram based software birthmark form the dissembled codes and calculate the similarity of sample DEXs by comparing the extracted birthmarks. We perform several experiments to identify effects of the three techniques. With the analysis of the experimental results, the advantages and disadvantages of each technique are discussed.

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