Optimizing mkbTT

We describe performance enhancements that have been added to mkbTT, a modern completion tool combining multi-completion with the use of termination tools

Semi-supervised Learning from Crowds Using Deep Generative Models

Although supervised learning requires a labeled dataset, ob- taining labels from experts is generally expensive. For this reason, crowdsourcing services are attracting attention in the field of machine learning as a way to collect labels at rela- tively low cost. However, the labels obtained by crowdsourc- ing, i.e., from non-expert workers, are often noisy. A num- ber of methods have thus been devised for inferring true la- bels, and several methods have been proposed for learning classifiers directly from crowdsourced labels, referred to as learning from crowds. A more practical problem is learn- ing from crowdsourced labeled data and unlabeled data, i.e., semi-supervised learning from crowds. This paper presents a novel generative model of the labeling process in crowdsourc- ing. It leverages unlabeled data effectively by introducing latent featuresand a data distribution. Because the data distri- bution can be complicated, we use a deep neural network for the data distribution. Therefore, our model can be regarded as a kind of deep generative model. The problems caused by the intractability of latent variable posteriors is solved by intro- ducing an inference model. The experiments show that it out- performs four existing models, including a baseline model, on the MNIST dataset with simulated workers and the Rot- ten Tomatoes movie review dataset with Amazon Mechanical Turk workers

Multi-Context Rewriting Induction with Termination Checkers

Inductive theorem proving plays an important role in the field of formal verification of systems. The rewriting induction (RI) is a method for inductive theorem proving proposed by Reddy. In order to obtain successful proofs, it is very important to choose appropriate contexts (such as in which direction each equation should be oriented) when applying RI inference rules. If the choice is not appropriate, the procedure may diverge or the users have to come up with several lemmas to prove together with the main theorem. Therefore we have a good reason to consider parallel execution of several instances of the rewriting induction procedure, each in charge of a distinguished single context in search of a successful proof. In this paper, we propose a new procedure, called multi-context rewriting induction, which efficiently simulates parallel execution of rewriting induction procedures in a single process, based on the idea of the multi-completion procedure. By the experiments with a well-known problem set, we discuss the effectiveness of the proposed procedure when searching along various contexts for a successful inductive proof

Hardness measures for gridworld benchmarks and performance analysis of real-time heuristic search algorithms

Gridworlds are one of the most popular settings used in benchmark problems for real-time heuristic search algorithms. However, no comprehensive studies have existed so far on how the difference in the density of randomly positioned obstacles affects the hardness of the problems. This paper presents two measures for characterizing the hardness of gridworld problems parameterized by obstacle ratio, and relates them to the performance of the algorithms. We empirically show that the peak locations of those measures and actual performance degradation of the basic algorithms (RTA* and LRTA*) almost coincide with each other for a wide variety of problem settings. Thus the measures uncover some interesting aspects of the gridworlds

A Method for Describing Structure of System Security Based on Trust and Authentication

In this paper, we propose a method by which frontline engineers in system development fields can readily describe the structure of the security of systems. This method, based on the assumption of the use of standard encryption technologies and existing cryptographic protocols, reveals hidden security threats and vulnerabilities of systems. It extracts only security elements that constitute the trust relationship of system components, describing the relation between the elements, and analyzing the relation. This method provides a valuable assistance tool to build secure systems, because it works as an efficient communication paradigm between stakeholders of a system to help them in understanding the security of the system and confirming that their security requirements are fulfilled

Adaptive Rotation Forests : Decision Tree Ensembles for Sequential Learning

We have developed an ensemble-based approach for online machine learning: adaptive rotation forest and AD-WIN adaptive rotation forest. We focused on rotation forest, an offline supervised ensemble algorithm with a particularly high prediction accuracy while all the features are continuous. Our objective was to develop a high-performance online ensemble method that uses a process similar to that of rotation forest in an online environment. Our experiments demonstrated that the proposed approach simplifies the tree structure used for the base learners, reduces memory consumption, and improves prediction accuracy for some data streams