Energy Complexity of Software in Embedded Systems

The importance of low power consumption is widely acknowledged due to the increasing use of portable devices, which require minimizing the consumption of energy. The energy in a computational system depends heavily on the software being executed, since it determines the activity in the underlying circuitry. In this paper we introduce the notion of energy complexity of an algorithm for estimating the required energy consumption. As test vehicle we employ matrix multiplication algorithms and from the results it can be observed that energy complexity in combination with computational complexity, provides an accurate estimation for the energy consumed in the system

Entropy as a measure of objectoriented design quality

Abstract. In this paper, object-oriented designs are approached from an information theoretic point of view and entropy is proposed as a design quality metric. One of the primary aims of object-orientation is the flexibility and the ease in extending a system’s functionality, with limited alterations to existing modules. This feature is evaluated defining an appropriate probability space according to the number of unary associations enabling the definition of an entropy metric. The entropy of the next generation of an object-oriented system with enhanced functionality remains close to its previous level in case the added functionality affects a limited number of existing classes; on the other hand, a poorly designed system increases entropy drastically. In this way, not only a given system is evaluated but it is also possible to assess the degradation of a system and its “distance ” from the original design.

Evaluating the style-based risk model for equity mutual funds investing in Europe

American equity mutual funds of varying investment styles investing in Europe is examined, using Value at Risk (VaR) and expected tail loss (ETL) models developed through three techniques (parametric, nonparametric and style-based approach). Alternative VaR and ETL implementations might impact the market risk forecast. It is necessary to avoid biasing fund risk estimates. Particular attention is given to the style-based risk approach by comparing it to the other methods. A performance evaluation of the models is approached from two directions: statisical model selection and model selection based on a loss function. The empirical results show that the particular investment style of a mutual fund must guide and determine which VaR and ETL model may be applied in order to extract accurate risk estimates. For the least diversified funds that overweight growth and underweight value stocks, the style-based risk model produce significantly lower VaR and ETL estimates than do the other models. The results for the well-diversified fund show an opposite significance pattern. Through 'backtesting' procedures, additional evidence is provided for the significance of testing frequency and size of tail losses in order to rank risk models.