IADIS-INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS

13

Authors: Tsuyoshi Miyashita, Ryota Imai, Masaki Kondo and Tadasuke Furuya

Now, in Japan, the coastal shipping industry has problems reducing the seafarers and aging. The closed sea areas in central bays and ports in Japan are navigated by many ships. In these waters, an inexperienced ship operator may make a mistake in judgment due to extreme tension, which may cause a marine accident. The authors made a prototype to provide ship operators on board with maneuvering assistance from shore to solve the problems. We developed a prototype by using wireless and mobile communication, VPN, and web browser. We conducted an actual ship experiment in the natural sea as a verification, discussing its effectiveness. It was shown that maneuvering about the same level as onboard as possible. As a result, it was confirmed that this method could be effective by future improvements.

14

Authors: Damiano Oriti, Andrea Sanna, Francesco De Pace, Federico Manuri, Francesco Tamburello and Fabrizio Ronzino

Augmented reality (AR) and virtual reality (VR) applications can take advantage of efficient digitalization of real objects as reconstructed elements can allow users a better connection between real and virtual worlds than using pre-set 3D CAD models. Technology advances contribute to the spread of AR and VR technologies, which are always more diffuse and popular. On the other hand, the design and implementation of virtual and extended worlds is still an open problem; affordable and robust solutions to support 3D object digitalization is still missing. This work proposes a reconstruction system that allows users to receive a 3D CAD model starting from a single image of the object to be digitalized and reconstructed. A smartphone can be used to take a photo of the object under analysis and a remote server performs the reconstruction process by exploiting a pipeline of three Deep Learning methods. Accuracy and robustness of the system have been assessed by several experiments and the main outcomes show how the proposed solution has a comparable accuracy (chamfer distance) with the state-of-the-art methods for 3D object reconstruction.

16

Authors: Evan Bryer, Theppatorn Rhujittawiwat, John R. Rose and Colin F. Wilder

The goal of this paper is to modify an existing clustering algorithm with the use of the Hunspell spell checker to specialize it for the use of cleaning early modern European book title data. Duplicate and corrupted data is a constant concern for data analysis, and clustering has been identified to be a robust tool for normalizing and cleaning data such as ours. In particular, our data comprises over 5 million books published in European languages between 1500 and 1800 in the Machine-Readable Cataloging (MARC) data format from 17,983 libraries in 123 countries. However, as each library individually catalogued their records, many duplicative and inaccurate records exist in the data set. Additionally, each language evolved over the 300-year period we are studying, and as such many of the words had their spellings altered. Without cleaning and normalizing this data, it would be difficult to find coherent trends, as much of the data may be missed in the query. In previous research, we have identified the use of Prediction by Partial Matching to provide the most increase in base accuracy when applied to dirty data of similar construct to our data set. However, there are many cases in which the correct book title may not be the most common, either when only two values exist in a cluster, or the dirty title exists in more records. In these cases, a language agnostic clustering algorithm would normalize the incorrect title and lower the overall accuracy of the data set. By implementing the Hunspell spell checker into the clustering algorithm, using it to rank clusters by the number of words not found in their dictionary, we can drastically lower the cases of this occurring. Indeed, this ranking algorithm proved to increase the overall accuracy of the clustered data by as much as 25% over the unmodified Prediction by Partial Matching algorithm.