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.

14

Authors: Marie-E. Godefroid

While digitalization remains high on the agenda of most private sector organizations, many non-governmental organizations (NGOs) still struggle, due to budget constraints (Merkel et al. 2007). A concept that might be helpful in this context is lightweight IT. It describes "a socio-technical knowledge regime, driven by competent users’ need for solutions, enabled by the consumerization of digital technology, and realized through innovation processes" (Bygstad 2017). By building on existing solutions and focusing on the user, NGOs might circumvent high IT spending and sub-optimal IT solutions that do not fit users' needs. A use case analysis along the NGO value chain shows that ~1/3 of the use cases identified from literature already represent lightweight IT examples. Nonetheless, certain lightweight IT solutions successfully deployed in the private sector like RPA do not appear at all, leaving room for future exploration and improvement.

15

Authors: Ghassan (Gus) Jabbour and Jason J. Jabbour

The insider threat is a problem that organizations have to deal with. Regardless of its size, mission, or location, any company that uses information systems is potentially vulnerable to insider attacks. Federal agencies, non-governmental organizations, as well as data centers face the same risk of being attacked by an insider. Countering the insider threat is a difficult and daunting task. Organizations concerned with the problem usually train their employees on security-related matters, rules of behavior policies, and the consequences of committing criminal activities. More technically-oriented solutions include enhanced credentialing and access control, and the use of monitoring tools that provide insight into the health and status of systems. This paper addresses the deficiency of widely-used monitoring tools and strategies. It discusses the difference between traditional security approaches and autonomic-based self-protection. The paper then proposes a solution that equips a system with innate self-defense mechanisms that relieve the system from having to rely on human intervention. The paper introduces the Insider Threat Minimization and Mitigation Framework. This framework equips systems with self-defense mechanisms such that a system can instantaneously respond to potential threats and defend itself against users who have unfettered access to it. The framework employs the autonomous demotion of power users’ access privileges based on analysis and evaluation of the user’s risk level. The paper presents the details of the proposed framework and simulates its effectiveness within a data center environment of mission-critical systems.

17

Authors: Frank Grave, Rogier van de Wetering and Rob J. Kusters

The exploitation of new value propositions enabled by disruptive digital technologies—also known as digital transformations—influences the configuration of people, processes, and technology in a firm and must be considered in the enterprise’s strategy. Strategy planning, i.e., the process that determines strategy, is facilitated by enterprise architecture (EA). Hence, strategic plans are reflected in EA documents called artifacts. Our research identified fifteen EA artifacts that facilitate digital transformations’ strategy planning process through a systematic literature review. Furthermore, a multiple case study in the financial services sector was carried out to verify our theoretical findings. The case organizations show a fragmented implementation of EA artifacts, with each organization having established its own, almost unique, way of using them. Only four of the theoretically conceptualized artifacts were found in all organizations. Moreover, two EA artifacts used in all case organizations were not included in our theoretical conceptualization. In combination with the empirical research, the systematic literature review leads to an improved understanding of EA artifacts and their use in the strategy planning process for digital transformations.