Research

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21 pages, 1524 KiB

Open AccessArticle

A Survey on Compression Domain Image and Video Data Processing and Analysis Techniques

by Yuhang Dong and W. David Pan

Information 2023, 14(3), 184; https://doi.org/10.3390/info14030184 - 15 Mar 2023

Cited by 3 | Viewed by 2513

A tremendous amount of image and video data are being generated and shared in our daily lives. Image and video data are typically stored and transmitted in compressed form in order to reduce storage space and transmission time. The processing and analysis of [...] Read more.

A tremendous amount of image and video data are being generated and shared in our daily lives. Image and video data are typically stored and transmitted in compressed form in order to reduce storage space and transmission time. The processing and analysis of compressed image and video data can greatly reduce input data size and eliminate the need for decompression and recompression, thereby achieving significant savings in memory and computation time. There exists a body of research on compression domain data processing and analysis. This survey focuses on the work related to image and video data. The papers cited are categorized based on their target applications, including image and video resizing and retrieval, information hiding and watermark embedding, image and video enhancement and segmentation, object and motion detection, as well as pattern classification, among several other applications. Key methods used for these applications are explained and discussed. Comparisons are drawn among similar approaches. We then point out possible directions of further research. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Humanities)

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20 pages, 3343 KiB

Open AccessArticle

The LACRIMALit Ontology of Crisis: An Event-Centric Model for Digital History

by Maria Papadopoulou, Christophe Roche and Eleni-Melina Tamiolaki

Information 2022, 13(8), 398; https://doi.org/10.3390/info13080398 - 22 Aug 2022

Viewed by 1816

Abstract

The article presents the building of an event-centric model for the computational representation of crisis events using an ontology encoded in the Web Ontology Language (OWL). The work presented here is done in collaboration with the Leaders and Crisis Management in Ancient Literature. [...] Read more.

The article presents the building of an event-centric model for the computational representation of crisis events using an ontology encoded in the Web Ontology Language (OWL). The work presented here is done in collaboration with the Leaders and Crisis Management in Ancient Literature. A Comparative Approach (LACRIMALit) project, (2022–2025) hosted at the Institute for Mediterranean Studies/Foundation for Research and Technology (IMS-FORTH). A key outcome of the project is the LACRIMALit ontology that aims principally at the semantic annotation of ancient Greek historiographical texts in open access via Perseus Digital Library. The ontology will facilitate reasoning on and across these documents and enable their semantic querying. The tagset of annotations, concepts, relations, and terms of the ontology will be both human and machine readable, extensible and reusable. The annotated corpus of texts to be produced will be available for sophisticated queries based on the concepts and relations, defined by the ontologies. This will considerably improve the string-based querying of the texts in their present digital format. This article presents the principles of conceptualization of the domain in the three dimensions: domain knowledge (mainly classes illustrated with some individuals), linguistic dimension (terms, proper names, definite descriptions), and references. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Humanities)

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15 pages, 1209 KiB

Open AccessArticle

Computationally Efficient Context-Free Named Entity Disambiguation with Wikipedia

by Michael Angelos Simos and Christos Makris

Information 2022, 13(8), 367; https://doi.org/10.3390/info13080367 - 02 Aug 2022

Cited by 2 | Viewed by 1874

Abstract

The induction of the semantics of unstructured text corpora is a crucial task for modern natural language processing and artificial intelligence applications. The Named Entity Disambiguation task comprises the extraction of Named Entities and their linking to an appropriate representation from a concept [...] Read more.

The induction of the semantics of unstructured text corpora is a crucial task for modern natural language processing and artificial intelligence applications. The Named Entity Disambiguation task comprises the extraction of Named Entities and their linking to an appropriate representation from a concept ontology based on the available information. This work introduces novel methodologies, leveraging domain knowledge extraction from Wikipedia in a simple yet highly effective approach. In addition, we introduce a fuzzy logic model with a strong focus on computational efficiency. We also present a new measure, decisive in both methods for the entity linking selection and the quantification of the confidence of the produced entity links, namely the relative commonness measure. The experimental results of our approach on established datasets revealed state-of-the-art accuracy and run-time performance in the domain of fast, context-free Wikification, by relying on an offline pre-processing stage on the corpus of Wikipedia. The methods introduced can be leveraged as stand-alone NED methodologies, propitious for applications on mobile devices, or in the context of vastly reducing the complexity of deep neural network approaches as a first context-free layer. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Humanities)

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19 pages, 501 KiB

Open AccessArticle

Influencer Marketing on Instagram: A Sequential Mediation Model of Storytelling Content and Audience Engagement via Relatability and Trust

by Madiha Atiq, Ghulam Abid, Aizza Anwar and Muhammad Fazal Ijaz

Information 2022, 13(7), 345; https://doi.org/10.3390/info13070345 - 16 Jul 2022

Cited by 8 | Viewed by 11949

Abstract

Storytelling content is where the facts are conveyed by emotion and that make people more engaged and want to take action or change their surroundings. Stories fascinate people and can easily be remembered compared to the facts alone. The much-hyped feature “stories” of [...] Read more.

Storytelling content is where the facts are conveyed by emotion and that make people more engaged and want to take action or change their surroundings. Stories fascinate people and can easily be remembered compared to the facts alone. The much-hyped feature “stories” of Instagram, a trendy social media platform, has become a game-changer for influencer marketing. The present study extends reactance theory in the context of Instagram’s millennial users. Previous researchers have tested the effectiveness of the stories feature of this particular social media platform. Therefore, in line with the earlier studies, we propose a sequential mediation model that investigates the effect of storytelling content (made by Instagram Influencers) on audience engagement using two sequential mediation mechanisms of relatability and trust. Data were obtained using a cross-sectional study design from 273 millennial users of Instagram. Our results justify the direct and indirect hypothesized relationship through Process Macros. We found that relatability and trust play a significant role in building a strong relationship between storytelling content and audience engagement. Ultimately, the research findings suggest that professionals should be more creative while making the content on Instagram to engage the millennial market. Moreover, this research has tried to fill the gap in the literature on Instagram “stories” as an advertising platform. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Humanities)

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14 pages, 559 KiB

Open AccessArticle

Digital Transformation Strategy in Post-COVID Era: Innovation Performance Determinants and Digital Capabilities in Driving Schools

by Evangelia Nousopoulou, Maria Kamariotou and Fotis Kitsios

Information 2022, 13(7), 323; https://doi.org/10.3390/info13070323 - 04 Jul 2022

Cited by 13 | Viewed by 4846

Abstract

Businesses affected by the pandemic have realized the importance of incorporating digital transformation into their operations. However, as a result of the market lockdown, they realized that they needed to digitalize their firms immediately and make greater attempts to enhance their economic situation [...] Read more.

Businesses affected by the pandemic have realized the importance of incorporating digital transformation into their operations. However, as a result of the market lockdown, they realized that they needed to digitalize their firms immediately and make greater attempts to enhance their economic situation by integrating a greater number of technological components. While there have been numerous studies conducted on the adoption of digital transformation in small–medium enterprises, there has been no research carried out on the implementation of digital transformation in the specific industry of driving schools. This paper investigates the significance of digital transformation, as well as the potential for its application in this industry’s business setting and the ways in which it can be utilized to improve innovation capabilities and performance. The data for this study came from 300 driving instructors in Greece and Cyprus. Multivariate regression analysis was used to analyze the data. The outcomes suggest that driving schools have a generally positive reaction to and acknowledgement of the increasing speed of digital transformation. The results also give driving school owners useful information that helps them show how important digital transformation is to their businesses. Using the findings of this study, driving schools will be able to improve their operational capabilities and accelerate their development in the post-COVID era. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Humanities)

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12 pages, 2064 KiB

Open AccessArticle

A Non-Iterative Constrained Measure of Research Impact

by Jakub Swacha

Information 2022, 13(7), 319; https://doi.org/10.3390/info13070319 - 29 Jun 2022

Cited by 1 | Viewed by 1215

Abstract

The number of received citations and more complex bibliographic measures calculated based on them, such as the h-index, remain the most widely used indicators for measuring research impact in an objective and easy-to-compute way. However, using the number of received citations as a [...] Read more.

The number of received citations and more complex bibliographic measures calculated based on them, such as the h-index, remain the most widely used indicators for measuring research impact in an objective and easy-to-compute way. However, using the number of received citations as a research impact measure has its shortcomings, some intrinsic (stemming from the doubt whether a citation is actually a confirmation of the cited paper’s impact), some extrinsic (stemming from the ease of manipulating this measure by deliberately inserting multiple unmerited references). While the first can only be addressed by a careful interpretation of the measure with consideration of its limitations, the latter can be reduced to much extent by replacing simple citation counting with a more sophisticated procedure constraining the impact that a single publication may have on the others. One such solution is ArticleRank, which has, however, several disadvantages limiting its practical use. In this paper, we propose another solution to this problem, the Transitive Research Impact Score (TRIS), which is free of these disadvantages, and validate it on a sample dataset. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Humanities)

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14 pages, 3142 KiB

Open AccessArticle

A GIS-Based Fuzzy Multiclassification Framework Applied for Spatiotemporal Analysis of Phenomena in Urban Contexts

by Barbara Cardone and Ferdinando Di Martino

Information 2022, 13(5), 248; https://doi.org/10.3390/info13050248 - 12 May 2022

Cited by 2 | Viewed by 1631

Abstract

In this research, we propose a GIS-based framework implementing a fuzzy-based document classification method aimed at classifying urban areas by the type of criticality inherent or specific problems highlighted by citizens. The urban study area is divided into subzones; for each subzone, the [...] Read more.

In this research, we propose a GIS-based framework implementing a fuzzy-based document classification method aimed at classifying urban areas by the type of criticality inherent or specific problems highlighted by citizens. The urban study area is divided into subzones; for each subzone, the reports of citizens relating to specific criticalities are analyzed and documents are created, and collected by topic and by temporal extension. The framework implements a model applied to the multiclassification of the documents in which the topic to be analyzed is divided into categories and a dictionary of terms connected to each category is built to measure the relevance of the category in the document. The framework produces, for each time frame, thematic maps of the relevance of a category in a time frame in which a subzone of the study area is classified based on the classification of the corresponding document. The framework was experimented on to analyze and monitor over time the relevance of disruptions detected by users in entities that make up urban areas, such as: roads, private buildings, public buildings and transport infrastructures, lighting networks, and public green areas. The study area is the city of Naples (Italy), partitioned in ten municipalities. The results of the tests show that the proposed framework can be a support for decision makers in analyzing the relevance of categories into which a topic is partitioned and their evolution over time. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Humanities)

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Review

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12 pages, 2876 KiB

Open AccessEditor’s ChoiceReview

Fourth Industrial Revolution between Knowledge Management and Digital Humanities

by Muhammad Anshari, Muhammad Syafrudin and Norma Latif Fitriyani

Information 2022, 13(6), 292; https://doi.org/10.3390/info13060292 - 08 Jun 2022

Cited by 20 | Viewed by 9004

Abstract

The Fourth Industrial Revolution (4IR) offers optimum productivity and efficiency via automation, expert systems, and artificial intelligence. The Fourth Industrial Revolution deploys smart sensors, Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Services (IoS), big data and analytics, Augmented Reality (AR), autonomous [...] Read more.

The Fourth Industrial Revolution (4IR) offers optimum productivity and efficiency via automation, expert systems, and artificial intelligence. The Fourth Industrial Revolution deploys smart sensors, Cyber-Physical Systems (CPS), Internet of Things (IoT), Internet of Services (IoS), big data and analytics, Augmented Reality (AR), autonomous robots, additive manufacturing (3D Printing), and cloud computing for optimization purposes. However, the impact of 4IR has brought various changes to digital humanities, mainly in the occupations of people, but also in ethical compliance. It still requires the redefining of the roles of knowledge management (KM) as one of the tools to assist in organization growth, especially in negotiating tasks between machines and people in an organization. Knowledge management is crucial in the development of new digital skills that are governed by the ethical obligations that are necessary in the Fourth Industrial Revolution. The purpose of the study is to examine the role of KM strategies in responding to the emergence of 4IR, its impact on and challenges to the labor market, and employment. This paper also analyzes and further discusses how 4IR and employment issues are being viewed in the context of ethical dilemmas. Full article

(This article belongs to the Special Issue Knowledge Management and Digital Humanities)

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