I would like to draw the attention of the editorial board of IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences and its readers to a recent paper, Tianruo Yang, "The integrated scheduling and allocation of high-level test synthesis," vol. E82-A, no. 1, January 1999, pp. 145-158. (Here we call this paper the Yang's paper. ) Yang did not give the correct information about the originality of the paper. I will point out that the writings (and the idea accordingly) of section 6 of Yang's paper came from papers [1] and [2].

Web service users are overwhelmed by the amount of information presented to them and have difficulties in finding the information that they need. Therefore, a recommendation system that predicts users' taste is an essential factor for the success of businesses. However, recommendation systems require users' personal information and can thus lead to serious privacy violations. To solve this problem, many research has been conducted about protecting personal information in recommendation systems and implementing differential privacy, a privacy protection technique that inserts noise into the original data. However, previous studies did not examine the following factors in applying differential privacy to recommendation systems. First, they did not consider the sparsity of user rating information. The total number of items is much more than the number of user-rated items. Therefore, a rating matrix created for users and items will be very sparse. This characteristic renders the identification of user patterns in rating matrixes difficult. Therefore, the sparsity issue should be considered in the application of differential privacy to recommendation systems. Second, previous studies focused on protecting user rating information but did not aim to protect the lists of user-rated items. Recommendation systems should protect these item lists because they also disclose user preferences. In this study, we propose a differentially private recommendation scheme that bases on a grouping method to solve the sparsity issue and to protect user-rated item lists and user rating information. The proposed technique shows better performance and privacy protection on actual movie rating data in comparison with an existing technique.

This paper presents a new data path synthesis algorithm which takes into account simultaneously three important design criteria: testability, design area, and total execution time. We define a goodness measure on the testability of a circuit based on three rules of thumb introduced in prior work on synthesis for testability. We then develop a stepwise refinement synthesis algorithm which carries out the scheduling and allocation tasks in an integrated fashion. Experimental results for benchmark and other circuit examples show that we were able to enhance the testability of circuits significantly with very little overheads on design area and execution time.