担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

<p>Inspired by quantum annealing, digital annealing computers specified for annealing computations have been realized on a large scale, such as the Digital Annealer (DA) developed by Fujitsu and the CMOS Annealing Machine developed by Hitachi. With the progress achieved using these computers, it has become necessary to estimate the computational hardness of cryptographic problems. This paper focuses on lattice problems, such as the closest vector problem (CVP) and shortest vector problem (SVP), which are a class of optimization problems. These problems form the basis of the security of lattice-based cryptography, which is a prime candidate for the NIST post-quantum cryptography standardization. For these lattice problems, we propose methods for generating an Ising model and solving the Ising model on annealing computers with a bit representation as the input, which represents encodings to map each integer variable in the SVP into binary variables. We propose two methods for SVPs, a basic method and a variant incorporating approximately the concept of the classical lattice enumeration. In our experimental results obtained using the second-generation DA, we succeeded in finding a shortest nonzero lattice vector in 40- and 45-dimensional lattices in the Darmstadt SVP Challenge. The basic method with a hybrid bit representation was the fastest among our methods with a bit representation, and the expected running time was estimated as 664 and 13,750 seconds for the 40- and 45-dimensional lattices, respectively. These results provide a benchmark for solving the SVP with annealing computers.</p>

DOI： 10.15807/jorsj.65.121

CiNii Research

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

The NIS-Apriori algorithm, which is extended from the Apriori algorithm, was proposed for rule generation from non-deterministic information systems and implemented in SQL. The realized system handles the concept of certainty, possibility, and three-way decisions. This paper newly focuses on such a characteristic of table data sets that there is usually a fixed decision attribute. Therefore, it is enough for us to handle itemsets with one decision attribute, and we can see that one frequent itemset defines one implication. We make use of these characteristics and reduce the unnecessary itemsets for improving the performance of execution. Some experiments by the implemented software tool in Python clarify the improved performance.

DOI： 10.1007/978-3-030-52705-1_7

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85088571315&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

In the field of recommendation, there have been many efforts to help users interact with recommender systems in ways that appropriately elucidate user preferences. To let users interact with recommender systems, it is desirable that recommender systems are as transparent as possible. However, it is difficult to achieve complete transparency even with a simple method for interactive recommender systems because the relationship between the item features and the user preference is not intuitive when there is a utility function to generate recommendations. We focus on multi-attribute utility theory (MAUT) as one of the simplest methods for recommender systems and clarify the difficulties with its usage in interactive environments. Then, to overcome the difficulties, we propose an algorithm to generate natural language hints to let users understand ways of operation and see more items that match their preferences. The results of an offline simulation demonstrate that our method can effectively recommend a diverse range of items to users. As future work, we will conduct empirical experiments to evaluate the performance of our method in online situations.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072879340&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

How can we classify multi-way data such as network traffic logs with multi-way relations between source IPs, destination IPs, and ports? Multi-way data can be represented as a tensor, and there have been several studies on classification of tensors to date. One critical issue in the classification of multi-way relations is how to extract important features for classification when objects in different multi-way data, i.e., in different tensors, are not necessarily in correspondence. In such situations, we aim to extract features that do not depend on how we allocate indices to an object such as a specific source IP; we are interested in only the structures of the multi-way relations. However, this issue has not been considered in previous studies on classification of multi-way data. We propose a novel method which can learn and classify multi-way data using neural networks. Our method leverages a novel type of tensor decomposition that utilizes a target core tensor expressing the important features whose indices are independent of those of the multi-way data. The target core tensor guides the tensor decomposition into more effective results and is optimized in a supervised manner. Our experiments on three different domains show that our method is highly accurate, especially on higher order data. It also enables us to interpret the classification results along with the matrices calculated with the novel tensor decomposition.

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85053216047&origin=inward

対象： 高校生

種別：出前授業