Seiya Miyazaki

Computer-Aided Privacy Requirements Elicitation Technique

The legislative penalties and economic penalties for privacy violations are more serious for a service provider these days. In spite of demonstrating that it is willing and able to protect the privacy of information, a service provider developing a privacy-compliant system faces two challenges; technical complexities and legal complexities. In this paper, we propose a computer-aided privacy requirements elicitation technique (PRET) that helps software developers elicit privacy requirements more efficiently in the early stages of software development. The goal of the PRET tool is to accelerate the elicitation process and prevent privacy requirements leaks by using a general privacy requirements database derived from privacy laws and empirical privacy requirements. We also show the results of integrating the PRET tool with the security quality requirements engineering (SQUARE) methodology and provide evidence of the efficacy of the resultant tool.

Consistency measures for feature selection: a formal definition, relative sensitivity comparison and a fast algorithm

Consistency-based feature selection is an important category of feature selection research yet is defined only intuitively in the literature. First, we formally define a consistency measure, and then using this definition, evaluate 19 feature selection measures from the literature. While only 5 of these were labeled as consistency measures by their original authors, by our definition, an additional 9 measures should be classified as consistency measures. To compare these 14 consistency measures in terms of sensitivity, we introduce the concept of quasi-linear compatibility order, and partially determine the order among the measures. Next, we propose a new fast algorithm for consistency-based feature selection. We ran experiments using eleven large datasets to compare the performance of our algorithm against INTERACT and LCC, the only two instances of consistency-based algorithms with potential real world application. Our algorithm shows vast improvement in time efficiency, while its performance in accuracy is comparable with that of INTERACT and LCC.

A Fast and Accurate Feature Selection Algorithm Based on Binary Consistency Measure

Consistency‐based feature selection is an important category of feature selection research, and its advantage over other categories is due to consistency measures used to include the effect of interaction among features into evaluation of relevance of features. Even if features individually appear irrelevant to class labels, they can collectively show strong relevance. In such cases, we say that the features interact with each other. Consistency measures, in this regard, evaluate the collective relevance of a set of features and has been intuitively understood as a metric to measure a distance of an arbitrary feature set from the state of being consistent: A set of features is said to be consistent if, and only if, they as a whole determine class labels. In history, the binary consistency measure, which returns the value 1 if the feature set is consistent and 0 otherwise, was the first consistency measure introduced, and many advanced measures followed. The problem of the binary measure consists in the fact that it always returns 1 if a data set includes no consistent feature set. The measures that followed have solved this problem but sacrificed time efficiency of evaluation. Therefore, feature selection leveraging these measures are not fast enough to apply to large data sets. In this article, we aim to improve time efficiency of consistency‐based feature selection. To achieve the goal, we propose a new idea, which we call data set denoising: We eliminate examples which are viewed as noises from a data set until the data set becomes to include consistent feature sets and then apply the binary measure to find an appropriate feature set that is consistent. In our evaluation through intensive experiments, CWC, a new algorithm that implements data set denoising outperformed in both time efficiency and accuracy the benchmark consistency‐based algorithms. Specifically, CWC was about 31 times faster than the LCC that had been known as the fastest in the literature. Furthermore, in a comparison including feature selection algorithms that are not consistency‐based, CWC has turned out to be one of the fastest and the most accurate feature selection algorithms.

H ∞ control of microgrids involving gas turbine engines and batteries

This paper proposes a new power management control method for microgrids based on H∞ control theory. Microgrid systems consist of distributed power sources such as cogeneration systems and photovoltaic systems with batteries. In general, power generation by photovoltaic systems and power consumption of various loads cause large fluctuations depending on weather conditions and varied lifestyles. Moreover, efficient management of battery capacity is a critical issue to prolong the battery life. Therefore, in power management control of microgrids, it is necessary to take account of a number of aspects such as power balancing performance, maintaining battery capacity, and robustness against power fluctuations. For such multiobjective control problems, we apply H∞ control theory which offers a unified robust control method with desirable power balancing performance and efficient battery management. The experimental results illustrate the effectiveness of the proposed control method.

Integrating privacy requirements considerations into a security requirements engineering method and tool

In this paper we examine a method for identifying privacy requirements within the context of a security requirements engineering method. We briefly describe the security quality requirements engineering (SQUARE) methodology. Next we discuss our definition of privacy and the associated privacy concerns. We discuss the challenges of privacy requirements engineering and the need for incorporating privacy considerations into security requirements engineering approaches. We describe a novel modification to the SQUARE method and tool to incorporate privacy considerations, and identify future work that will lead to a more integrated method for security and privacy requirements engineering.

Creating mobile animation messages without authoring

Mobile phone e-mail messaging is increasingly being chosen by consumers as their primary communication tool. Messages exchanged among mobile phone users frequently contain “emojis (pictograms)”. Some consumers have started to use services which add a relevant animated image to a message. Consumers will start to exchange animated messages if they are able. This paper proposes a method of generating animations from text messages without an authoring process. This method utilizes information available in the consumer’s ubiquitous computing environment to supply the information needed to make animations which are not included in the original e-mail message.