System logs are useful to understand the status of and detect faults in large scale networks. However, due to their diversity and volume of these logs, log analysis requires much time and effort. In this paper, we propose a log event anomaly detection method for large-scale networks without pre-processing and feature extraction. The key idea is to embed a large amount of diverse data into hidden states by using latent variables. We evaluate our method with 12 months of system logs obtained from a nation-wide academic network in Japan. Through comparisons with Kleinberg's univariate burst detection and a traditional multivariate analysis (i.e., PCA), we demonstrate that our proposed method achieves 14.5% higher recall and 3% higher precision than PCA. A case study shows detected anomalies are effective information for troubleshooting of network system faults.

In order to operate an ambulance efficiently, we developed a Smart Ambulance Approach Alarm System using smartphone, by notifying the approach of an ambulance to other vehicles on public roads. The position information of ambulances has not been opened in view of development costs and privacy protection. Therefore, our study opens the position information inexpensively by loading commodity smartphones, not special devices, into ambulances. The position information is made to be open as minimum necessary information by our developed cloud server application, considering dynamic state of other vehicles on public roads and privacy of ambulance service users. We tested the functional efficiency of this system by the demonstration experiment on public roads.

Smart education environment, that is a learning environment utilizing the Information Communication Technology (ICT), has attracted a great deal of attention. In order to expand this environment, we need a system that can establish the learning environment armed cloud systems to reduce a significant strain on teaching staff. The important issue for such system is extensibility because the system should be adapted to many kinds of original digital learning material with minimum modification. Therefore, this paper proposes “An Application Framework for Smart Education System: SES Framework”. In this Smart Education System, multi-aspect information concerning to a technical term embedded in the original digital learning material can be retrieved from different social media automatically. They can be also displayed on multi-screen devices according to user's operation. It is implemented based on “Transforming Model” which enables the migration of the original digital learning material to the smart education environment. It also has an easy operation flow for trainees named “three-step selection flow”. SES Framework derived from Model-View-Controller (MVC) pattern is based on the system architecture that enables triple mashup against the original digital learning material, external social media, and screen devices in front of users. All these functionalities have been implemented on cloud systems. We show SES Framework through the implementation example. We also demonstrate the effectiveness of SES Framework by indicating the system modification case study.

With the growing global demand for seafood, sustainable aquaculture is attracting more attention than conventional natural fishing, which causes overfishing and damage to the marine environment. However, a major problem facing the aquaculture industry is the cost of feeding, which accounts for about 60% of a fishing expenditure. Excessive feeding increases costs, and the accumulation of residual feed on the seabed negatively impacts the quality of water environments (e.g., causing red tides). Therefore, the importance of raising fishes efficiently with less food by optimizing the timing and quantity of feeding becomes more evident. Thus, we developed a system to quantitate the amount of fish activity for the optimal feeding time and feed quantity based on the images taken. For quantitation, optical flow that is a method for tracking individual objects was used. However, it is difficult to track individual fish and quantitate their activity in the presence of many fishes. Therefore, all fish in the filmed screen were considered as a single school and the amount of change in an entire screen was used as the amount of the school activity. We divided specifically the entire image into fixed regions and quantitated by vectorizing the amount of change in each region using optical flow. A vector represents the moving distance and direction. We used the numerical data of a histogram as the indicator for the amount of fish activity by dividing them into classes and recording the number of occurrences in each class. We verified the effectiveness of the indicator by quantitating the eating and not eating movements during feeding. We evaluated the performance of the quantified indicators by the support vector classification, which is a form of machine learning. We confirmed that the two activities can be correctly classified.

Electromagnetic scattering by four perfectly conducting rectangular cylinders is analyzed by Point Matching Method. The numerical results are in full agreement with the results for the well known integral solution method. And this letter give not only the field calculated around four cylinders but also model experiments using a line source at 9.6 GHz. The numerical results are in nearly good agreement with the experimental results.