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Digital, Volume 2, Issue 4 (December 2022) – 8 articles

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14 pages, 6124 KiB  
Article
When BERT Started Traveling: TourBERT—A Natural Language Processing Model for the Travel Industry
by Veronika Arefeva and Roman Egger
Digital 2022, 2(4), 546-559; https://doi.org/10.3390/digital2040030 - 11 Nov 2022
Cited by 1 | Viewed by 2314
Abstract
In recent years, Natural Language Processing (NLP) has become increasingly important for extracting new insights from unstructured text data, and pre-trained language models now have the ability to perform state-of-the-art tasks like topic modeling, text classification, or sentiment analysis. Currently, BERT is the [...] Read more.
In recent years, Natural Language Processing (NLP) has become increasingly important for extracting new insights from unstructured text data, and pre-trained language models now have the ability to perform state-of-the-art tasks like topic modeling, text classification, or sentiment analysis. Currently, BERT is the most widespread and widely used model, but it has been shown that a potential to optimize BERT can be applied to domain-specific contexts. While a number of BERT models that improve downstream tasks’ performance for other domains already exist, an optimized BERT model for tourism has yet to be revealed. This study thus aimed to develop and evaluate TourBERT, a pre-trained BERT model for the tourism industry. It was trained from scratch and outperforms BERT-Base in all tourism-specific evaluations. Therefore, this study makes an essential contribution to the growing importance of NLP in tourism by providing an open-source BERT model adapted to tourism requirements and particularities. Full article
(This article belongs to the Collection Digital Systems for Tourism)
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8 pages, 254 KiB  
Editorial
Bridging Digital Approaches and Legacy in Archaeology
by Markos Katsianis, Tuna Kalayci and Apostolos Sarris
Digital 2022, 2(4), 538-545; https://doi.org/10.3390/digital2040029 - 09 Nov 2022
Cited by 5 | Viewed by 2317
Abstract
The emergence of the ubiquitous digital ecosystem has provided new momentum for research in archaeology and the cultural heritage domain [...] Full article
(This article belongs to the Special Issue Bridging Digital Approaches and Legacy in Archaeology)
18 pages, 789 KiB  
Article
Decision-Making Approach for an IoRT-Aware Business Process Outsourcing
by Najla Fattouch, Imen Ben Lahmar, Mouna Rekik and Khouloud Boukadi
Digital 2022, 2(4), 520-537; https://doi.org/10.3390/digital2040028 - 04 Nov 2022
Cited by 1 | Viewed by 1860
Abstract
In the context of Industry 4.0, IoRT-aware BPs represent an attractive paradigm that aims to automate the classic business process (BP) using the internet of robotics things (IoRT). Nonetheless, the execution of these processes within the enterprises may be costly due to the [...] Read more.
In the context of Industry 4.0, IoRT-aware BPs represent an attractive paradigm that aims to automate the classic business process (BP) using the internet of robotics things (IoRT). Nonetheless, the execution of these processes within the enterprises may be costly due to the consumed resources, recruitment cost, etc. To bridge these gaps, the business process outsourcing (BPO) strategy can be applied to outsource partially or totally a process to external service suppliers. Despite the various advantages of BPO, it is not a trivial task for enterprises to determine which part of the process should be outsourced and which environment would be selected to deploy it. This paper deals with the decision-making outsourcing of an IoRT-aware BP to the fog and/or cloud environments. The fog environment includes devices at the edge of the network which will ensure the latency requirements of some latency-sensitive applications. However, relying on cloud, the availability and computational requirements of applications can be met. Toward these objectives, we realized an in-depth analysis of the enterprise requirements, where we identified a set of relevant criteria that may impact the outsourcing decision. Then, we applied the method based on the removal effects of criteria (MEREC) to automatically generate the weights of the identified criteria. Using these weights, we performed the selection of the suitable execution environment by using the ELECTRE IS method. As an approach evaluation, we sought help from an expert to estimate the precision, recall, and F-score of our approach. The obtained results show that our approach is the most similar to the expert result, and it has acceptable values. Full article
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19 pages, 4033 KiB  
Article
Significance of Machine Learning for Detection of Malicious Websites on an Unbalanced Dataset
by Ietezaz Ul Hassan, Raja Hashim Ali, Zain Ul Abideen, Talha Ali Khan and Rand Kouatly
Digital 2022, 2(4), 501-519; https://doi.org/10.3390/digital2040027 - 31 Oct 2022
Cited by 28 | Viewed by 3582
Abstract
It is hard to trust any data entry on online websites as some websites may be malicious, and gather data for illegal or unintended use. For example, bank login and credit card information can be misused for financial theft. To make users aware [...] Read more.
It is hard to trust any data entry on online websites as some websites may be malicious, and gather data for illegal or unintended use. For example, bank login and credit card information can be misused for financial theft. To make users aware of the digital safety of websites, we have tried to identify and learn the pattern on a dataset consisting of features of malicious and benign websites. We treated the problem of differentiation between malicious and benign websites as a classification problem and applied several machine learning techniques, for example, random forest, decision tree, logistic regression, and support vector machines to this data. Several evaluation metrics such as accuracy, precision, recall, F1 score, and false positive rate, were used to evaluate the performance of each classification technique. Since the dataset was imbalanced, the machine learning models developed a bias during training toward a specific class of websites. Multiple data balancing techniques, for example, undersampling, oversampling, and SMOTE, were applied for balancing the dataset and removing the bias. Our experiments showed that after balancing the data, the random forest algorithm using the oversampling technique showed the best results in all evaluation metrics for the benign and malicious website feature dataset. Full article
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17 pages, 919 KiB  
Article
Checking Consistency and Comparing Multi-Criteria Decision Making Methods in the Evaluation of Museums’ Websites
by Katerina Kabassi, Stelios Bekatoros and Athanasios Botonis
Digital 2022, 2(4), 484-500; https://doi.org/10.3390/digital2040026 - 24 Oct 2022
Cited by 2 | Viewed by 1638
Abstract
The need to evaluate museum websites is an issue that has been highlighted by several researchers. In this paper, we focus on museums’ website evaluation and use as a case study the evaluation of natural history museums’ websites. For this evaluation experiment, MCDM [...] Read more.
The need to evaluate museum websites is an issue that has been highlighted by several researchers. In this paper, we focus on museums’ website evaluation and use as a case study the evaluation of natural history museums’ websites. For this evaluation experiment, MCDM methods are combined and compared. The focus of this paper is twofold: (1) checking the consistency of AHP for calculating the weights of criteria and (2) comparing Fuzzy TOPSIS and Fuzzy VIKOR with each other and with a usability evaluation questionnaire. Full article
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21 pages, 1179 KiB  
Article
Managerial Practices for the Digital Transformation of Manufacturers
by Lukas Budde, Christoph Benninghaus, Roman Hänggi and Thomas Friedli
Digital 2022, 2(4), 463-483; https://doi.org/10.3390/digital2040025 - 30 Sep 2022
Cited by 3 | Viewed by 3023
Abstract
The digital transformation is a complex and multi-faceted phenomenon, which companies hamper to manage effectively. One particular facet of this phenomenon is the role of managers, which is still underrepresented in research. This study aims to identify and explain why and what managerial [...] Read more.
The digital transformation is a complex and multi-faceted phenomenon, which companies hamper to manage effectively. One particular facet of this phenomenon is the role of managers, which is still underrepresented in research. This study aims to identify and explain why and what managerial practices and competencies are particularly needed to effectively govern through this transformation. We choose the case study methodology as the research design with eight manufacturing companies in Western Europe, where we applied within- and cross-case analyses. Specific barriers for digital transformation and four aggregated managerial practices, such as strategy/organization, collaboration, cross-functionality and data-driven use cases, were identified. These were supported by 13 competencies to facilitate digitalization. We explicate these practices based on the change management theory and provide a model describing the impact of these practices on profitability. This study contributes to the emergent change theory by analyzing practices and competencies that managers should be equipped with to foster digitalization. Full article
(This article belongs to the Topic Digital Transformation and E-Government)
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19 pages, 350 KiB  
Article
Explicit and Implicit Trust Modeling for Recommendation
by Utku Demirci and Pinar Karagoz
Digital 2022, 2(4), 444-462; https://doi.org/10.3390/digital2040024 - 29 Sep 2022
Viewed by 1875
Abstract
Recommendation has become an inseparable component of many software applications, such as e-commerce, social media and gaming platforms. Particularly in collaborative filtering-based recommendation solutions, the preferences of other users are considered heavily. At this point, trust among the users comes into the scene [...] Read more.
Recommendation has become an inseparable component of many software applications, such as e-commerce, social media and gaming platforms. Particularly in collaborative filtering-based recommendation solutions, the preferences of other users are considered heavily. At this point, trust among the users comes into the scene as an important concept to improve the recommendation performance. Trust describes the nature and the strength of ties between individuals and hence provides useful information to improve the recommendation accuracy, particularly against data sparsity and cold start problems. The Trust notion helps alleviate the effect of these problems by providing additional reliable relationships between the users. However, trust information, specifically explicit trust, is not straightforward to collect and is only scarcely available. Therefore, implicit trust models have been proposed to fill in the gap. The literature includes a variety of studies proposing the use of trust for recommendation. In this work, two specific sub-problems are elaborated on: the relationship between explicit and implicit trust scores, and the construction of a machine learning model for explicit trust. For the first sub-problem, an implicit trust model is devised and the compatibility of implicit trust scores with explicit scores is analyzed. For the second sub-problem, two different explicit trust models are proposed: Explicit trust modeling through users’ rating behavior and explicit trust modeling as a link prediction problem. The performances of the prediction models are analyzed on a set of benchmark data sets. It is observed that explicit and implicit trust models have different natures, and are to be used in a complementary way for recommendation. Another important result is that the accuracy of the machine learning models for explicit trust is promising and depends on the availability of data. Full article
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22 pages, 2147 KiB  
Article
Digitising Legacy Field Survey Data: A Methodological Approach Based on Student Internships
by Anita Casarotto
Digital 2022, 2(4), 422-443; https://doi.org/10.3390/digital2040023 - 22 Sep 2022
Cited by 1 | Viewed by 3940
Abstract
In the Mediterranean, field survey has been the most widely used method to detect archaeological sites in arable fields since the 1970s. Through survey, data about the state of preservation of ancient settlements have been extensively mapped by archaeologists over large rural landscapes [...] Read more.
In the Mediterranean, field survey has been the most widely used method to detect archaeological sites in arable fields since the 1970s. Through survey, data about the state of preservation of ancient settlements have been extensively mapped by archaeologists over large rural landscapes using paper media (e.g., topographical maps) or GPS and GIS technologies. These legacy data are unique and irreplaceable for heritage management in landscape planning, territorial monitoring of cultural resources, and spatial data analysis to study past settlement patterns in academic research (especially in landscape archaeology). However, legacy data are at risk due to often improper digital curation and the dramatic land transformation that is affecting several regions. To access this vast knowledge production and allow for its dissemination, this paper presents a method based on student internships in data digitisation to review, digitise, and integrate archaeological primary survey data. A pilot study for Central–Southern Italy and the Iberian Peninsula exemplifies how the method works in practice. It is concluded that there are clear benefits for cultural resource management, academic research, and the students themselves. This method can thus help us to achieve large-scale collection, digitisation, integration, accessibility, and reuse of field survey datasets, as well as compare survey data on a supranational scale. Full article
(This article belongs to the Special Issue Bridging Digital Approaches and Legacy in Archaeology)
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