Automatic portfolio optimization (APO) is the process of automatically optimizing the allocation of assets in an investment portfolio through algorithms and models. Empirical evidence suggests that stock prices are likely to follow the mean reversion theory. Although existing mean reversion strategies for APO have been shown to achieve good empirical performance across many real-world datasets, they tend to construct single-stock portfolios, which results in high risk. In this paper, we propose a risk diversification version of Online Moving Average Reversion (OLMAR) [16], one of the major mean reversion strategies. The parameter k of our proposed method OLMAR-k controls the search space for optimization to guarantee the existence of solutions and prevent from solutions close to uniform. According to experimental results in resent datasets, OLMAR-k outperforms other state-of-the-art APO methods in terms of popular metrics for return and risk.

In data-parallel computing that uses a graphic processing unit (GPU), processing of large data requires that multiple GPUs be used in the computer to increase its execution performance. Increasing processing performance by using multiple computing resources has been enabled by the development of computing frameworks based on a directed acyclic graph (DAG). However, their performance degrades in out-of-core processing, which often occurs in processing of large data on GPUs with limited memory capacity. The GPU data input/output (I/O) for data swapping between host memory and GPU memory during the execution of a user DAG is usually a performance bottleneck. A computing framework called "Victream" is proposed to overcome this drawback. It uses a novel scheduler that involves two methods to minimize the total amount of GPU data I/O of data swapping. First, it performs locality-aware scheduling. When it schedules a task, it selects one that requires the minimum amount of data swapping and reuses as much of the data residing in GPU memory as possible. Second, it extends the locality-aware scheduling so that GPUs can execute data prefetching. Prefetching data that are swapped out from a GPU enables efficient use of bottleneck GPU I/O resources. To prefetch the input data of future tasks, it is required to determine the schedule of future tasks. Victream's scheduler (hereafter, the Victream scheduler) extends the locality-aware scheduling so that it can schedule future tasks to enable data prefetching that is executed in the way that minimizes the amount of data I/O of data swapping. Evaluation of a Victream prototype showed that the performance of Victream is better than that of conventional frameworks by up to 117%.

We have collected several size of data during experiments of the online shopping game. Where we could collect various information about the participants' behaviour. For instance, how they moved in the supermarket and which things (products) they checked, returned or bought. We have an intention to use those data to determine how human beings think during shopping. In addition, we think we can estimate the intelligence level (cognitive function) from the data. In order to do so, it will be necessary to deal with the collected data correctly. In this paper, from the viewpoint of granular computing, we will discuss how to collect data, what data should be collected, and how to store data in the data base. In addition, we will discuss how to estimate the cognitive function.