A single deletion error correcting code (SDECC) over binary alphabet is a set of fixed-length sequences consisting of two types of symbols, 0 and 1, such that the original sequence can be recovered for at most one deletion error. There is a conjecture “the upper bound for the size of SDECC is equal to the size of Varshamov- Tenengolts (VT) code.” This conjecture had been shown to be true when the code length is ten or less. In this paper, we discuss a method for calculating this upper bound by providing an integer linear programming solver with several linear constraints. As a new result, we obtained that the tight upper bound for the size of a single deletion error correcting code of length 11 is 172. In other words, we could prove that the conjecture is true for the case where the length is 11.

In this paper, we explain the development of a new Mizar tokenizer and parser program as a component of a search system that works on the Mizar Mathematical Library.The existing Mizar tokenizer and parser can handle only an article as a whole written in the Mizar language, however, the newly developed program can deal with a snippet of a Mizar article.In particular, since it is possible to handle a snippet of an article without specifying a vocabulary section of an environment part, it is expected that user input efforts will be greatly reduced. I. MOTIVATION T HE AUTHOR is developing a new information retrievalsystem that works on the Mizar Mathematical Library (MML) [1].In this paper, we explain a developed tokenizer and parser program of the Mizar language as a component of our search system.

This study was conducted to create driving episodes using machine-learning-based algorithms that address long-term memory (LTM) and topological mapping. This paper presents a novel episodic memory model for driving safety according to traffic scenes. The model incorporates three important features: adaptive resonance theory (ART), which learns time-series features incrementally while maintaining stability and plasticity for time-series data; self-organizing maps (SOMs), which represent input data as a map with topological relations using self-mapping characteristics; and counter propagation networks (CPNs), which label category maps using input features and counter signals. Category maps represent driving episode information that includes driving contexts and facial expressions. The bursting states of respective maps produce LTM, which is created on ART as episodic memory. Evaluation of the experimentally obtained results show the possibility of using recorded driving episodes with image datasets obtained using an event data recorder (EDR) with two cameras. Using category maps, we visualize driving features according to driving scenes on a public road and an expressway.

This paper presents a novel method using accelerated KAZE (AKAZE) and Gist for a context-based semantic classification and recognition of indoor scenes used for a vision-based mobile robot. Our method represents spatial relations among categories for mapping neighborhood units on category maps using counter propagation networks (CPNs) while maintaining sequential information of labels generated from adaptive resonance theory 2 (ART-2) networks. We evaluated the performance and accuracy of semantic categories using KTH-IDOL benchmark datasets. Compared with the earlier described method using scale-invariant feature transform (SIFT), accuracies of classification, recognition, and F-measure were improved to 2.9%, 3.4%, and 3.3% of our method using AKAZE. For analyzing results, confusion matrixes show that incorrect images between corridors and rooms are decreased in our method compared with the former method. We consider that our proposed feature representation method based on context and category formation, combined with ART-2 and CPNs, is useful for indoor scene classification and recognition for robot vision.