Solar power is the most widely used renewable energy source, which reduces pollution consequences from using conventional fossil fuels. However, supplying stable power from solar power generation remains challenging because it is difficult to forecast power generation. Accurate prediction of solar power generation would allow effective control of the amount of electricity stored in batteries, leading in a stable supply of electricity. Although the number of power plants is increasing, building a solar power prediction model for a newly constructed power plant usually requires collecting a new training dataset for the new power plant, which takes time to collect a sufficient amount of data. This paper aims to develop a highly accurate solar power prediction model for multiple power plants available for both new and existing power plants. The proposed method trains the model on existing multiple power plants to generate a general prediction model, and then uses it for a new power plant while waiting for the data to be collected. In addition, the proposed method tunes the general prediction model on the newly collected dataset and improves the accuracy for the new power plant. We evaluated the proposed method on 55 power plants in Japan with the dataset collected for two and a half years. As a result, the pre-trained models of our proposed method significantly reduces the average RMSE of the baseline method by 73.19%. This indicates that the model can generalize over multiple power plants, and training using datasets from other power plants is effective in reducing the RMSE. Fine-tuning the pre-trained model further reduces the RMSE by 8.12%.

Software peer review is regarded as one of the most important approaches to preserving software quality. Due to the distributed collaborations in Open Source Software (OSS) development, the review techniques and processes conducted in OSS environment differ from the traditional review method that based on formal face-to-face meetings. Unlike other related works, this study investigates peer review processes of OSS projects from the social perspective: communication and interaction in peer review by using social network analysis (SNA). Moreover, the relationship between peer review contributors and their activities is studied. We propose an approach to evaluating contributors' activeness and social relationship using SNA named Peer Review Social Network (PeRSoN). We evaluate our approach by empirical case study, 326,286 review comments and 1,745 contributors from three representative industrial OSS projects have been extracted and analyzed. The results indicate that the social network structure influences the realistic activeness of contributors significantly. Based on the results, we suggest our approach can support project leaders in assigning review tasks, appointing reviewers and other activities to improve current software processes.

OpenFlow is a widely adopted implementation of the Software-Defined Networking (SDN) architecture. Since conventional network monitoring systems are unable to cope with OpenFlow networks, researchers have developed various monitoring systems tailored for OpenFlow networks. However, these existing systems either rely on a specific controller framework or an API, both of which are not part of the OpenFlow specification, and thus limit their applicability. This article proposes a transparent and low-overhead monitoring system for OpenFlow networks, referred to as Opimon. Opimon monitors the network topology, switch statistics, and flow tables in an OpenFlow network and visualizes the result through a web interface in real-time. Opimon monitors a network by interposing a proxy between the controller and switches and intercepting every OpenFlow message exchanged. This design allows Opimon to be compatible with any OpenFlow switch or controller. We tested the functionalities of Opimon on a virtual network built using Mininet and a large-scale international OpenFlow testbed (PRAGMA-ENT). Furthermore, we measured the performance overhead incurred by Opimon and demonstrated that the overhead in terms of latency and throughput was less than 3% and 5%, respectively.

Marc Kellner proposed an example problem intending to compare modeling and describing techniques of software process. In this paper, we will describe our approach to understanding and describing the problem, from a process/product relation view, and synchronization/concurrent view. Also, we will show that a description of the problem is translated for execution and its correctness is validated.