Semantic Web Research Articles

Enhanced Semantic Visual Cryptography with AI-driven error reduction for improved two-dimensional image quality and security

Abstract Visual Cryptography (VC) has emerged as a vital technique in the information security domain, with the fundamental purpose of securing 2-Dimensional (2D) image content through encryption and facilitating secure communication. While traditional VC has been instrumental in safeguarding data, it often falls short in maintaining image quality and semantic accuracy upon reconstruction. To address these limitations, this research encompasses the development of an Enhanced Semantic VC (ESVC) model, which aims to refine the encryption process while ensuring the semantic integrity of the images. The ESVC model introduces a new approach that merges VC with artificial intelligence (AI) to enhance 2D image encryption and decryption. The novel aspect of this research lies in the integration of AI-driven reinforcement learning (RL) to increase the quality of the 2D image by measuring the errors between the original secret image and the reconstructed image. This innovative framework is tailored for the secure transmission of 2D grayscale images, ensuring the preservation of semantic integrity while measuring and minimizing image quality loss. By integrating RL algorithms with a measurement of error reduction protocol, the model promises robust encryption capabilities with enhanced resilience against a plethora of cyber threats, thereby elevating the standard for secure image communication. Empirical evaluation of the ESVC model yields promising results, with the Peak Signal-to-Noise Ratio (PSNR) of reconstructed images achieving impressive values between +39 and +42 decibels (dB). These findings underscore the ESVC model’s capability to produce high-fidelity decrypted images, significantly surpassing traditional VC methods in both security and image quality. The research findings illuminate the potential of merging AI with VC to achieve a harmonious balance between computational efficiency and encryption strength, marking a significant advancement in the domain of visual data protection.

The Role of Early Intact Auditory Experience on the Perception of Spoken Emotions, Comparing Prelingual to Postlingual Cochlear Implant Users.

Cochlear implants (CI) are remarkably effective, but have limitations regarding the transformation of the spectro-temporal fine structures of speech. This may impair processing of spoken emotions, which involves the identification and integration of semantic and prosodic cues. Our previous study found spoken-emotions-processing differences between CI users with postlingual deafness (postlingual CI) and normal hearing (NH) matched controls (age range, 19 to 65 years). Postlingual CI users over-relied on semantic information in incongruent trials (prosody and semantics present different emotions), but rated congruent trials (same emotion) similarly to controls. Postlingual CI's intact early auditory experience may explain this pattern of results. The present study examined whether CI users without intact early auditory experience (prelingual CI) would generally perform worse on spoken emotion processing than NH and postlingual CI users, and whether CI use would affect prosodic processing in both CI groups. First, we compared prelingual CI users with their NH controls. Second, we compared the results of the present study to our previous study (Taitlebaum-Swead et al. 2022; postlingual CI). Fifteen prelingual CI users and 15 NH controls (age range, 18 to 31 years) listened to spoken sentences composed of different combinations (congruent and incongruent) of three discrete emotions (anger, happiness, sadness) and neutrality (performance baseline), presented in prosodic and semantic channels (Test for Rating of Emotions in Speech paradigm). Listeners were asked to rate (six-point scale) the extent to which each of the predefined emotions was conveyed by the sentence as a whole (integration of prosody and semantics), or to focus only on one channel (rating the target emotion [RTE]) and ignore the other (selective attention). In addition, all participants performed standard tests of speech perception. Performance on the Test for Rating of Emotions in Speech was compared with the previous study (postlingual CI). When asked to focus on one channel, semantics or prosody, both CI groups showed a decrease in prosodic RTE (compared with controls), but only the prelingual CI group showed a decrease in semantic RTE. When the task called for channel integration, both groups of CI users used semantic emotional information to a greater extent than their NH controls. Both groups of CI users rated sentences that did not present the target emotion higher than their NH controls, indicating some degree of confusion. However, only the prelingual CI group rated congruent sentences lower than their NH controls, suggesting reduced accumulation of information across channels. For prelingual CI users, individual differences in identification of monosyllabic words were significantly related to semantic identification and semantic-prosodic integration. Taken together with our previous study, we found that the degradation of acoustic information by the CI impairs the processing of prosodic emotions, in both CI user groups. This distortion appears to lead CI users to over-rely on the semantic information when asked to integrate across channels. Early intact auditory exposure among CI users was found to be necessary for the effective identification of semantic emotions, as well as the accumulation of emotional information across the two channels. Results suggest that interventions for spoken-emotion processing should not ignore the onset of hearing loss.

NEW MEANINGS OF PERMANENT MEDIA ONYMS WITH COMPONENT INVINCIBILITY

The article is devoted to the analysis of the semantic load of media onyms updated in the infospace of the new time. Today, phrases such as invincible city, invincible people, invincible country, etc. have become dominant in mass communication, which acquire the status of semantically condensed media onyms, and their features are precedent, stable reproducibility, semantic integrity. Due to the fact that these units are expressively and emotionally colored, their semantic scope also changes. The purpose of the article is the analysis of new semantically condensed media onyms, which represent in the mass consciousness conceptualized linguistic phenomena with the component unbreakable, which can be endlessly formed in accordance with the background knowledge of the recipients and the semantic landmarks of the modern infospace: the invincible city (Kyiv, Odesa, Mariupol, Kharkiv, etc.), an invincible mayor (Vitaliy Klitschko, Igor Terekhov, Vitaliy Kim, etc.), an invincible country (Ukraine, Israel, Georgia, etc.). It was noticed that these units are close in their structure and semantic-stylistic potential to media phraseology, which verbalize objective reality and are used in communication as communicative "glues" for the cumulative and manipulative purpose of the message. It has been proven that new constructions become a kind of semantic triggers of media messages in new informational realities: invincible warriors, invincible Irpians, invincible prisoners, invincible armed forces, etc. The article draws attention to the universalization of onyms in the media space: it is about the use of the meaning-making concept INVINCIBILITY, which is used in relation to Ukraine and Ukrainians in the foreign press. Therefore, the formation of new cognitive meanings by media onyms was observed, which was generated by the Russian-Ukrainian war and is a segment of the semiotic system of the globalized information space of the world.

Autocleandeepfood: auto-cleaning and data balancing transfer learning for regional gastronomy food computing

AbstractFood computing has emerged as a promising research field, employing artificial intelligence, deep learning, and data science methodologies to enhance various stages of food production pipelines. To this end, the food computing community has compiled a variety of data sets and developed various deep-learning architectures to perform automatic classification. However, automated food classification presents a significant challenge, particularly when it comes to local and regional cuisines, which are often underrepresented in available public-domain data sets. Nevertheless, obtaining high-quality, well-labeled, and well-balanced real-world labeled images is challenging since manual data curation requires significant human effort and is time-consuming. In contrast, the web has a potentially unlimited source of food data but tapping into this resource has a good chance of corrupted and wrongly labeled images. In addition, the uneven distribution among food categories may lead to data imbalance problems. All these issues make it challenging to create clean data sets for food from web data. To address this issue, we present AutoCleanDeepFood, a novel end-to-end food computing framework for regional gastronomy that contains the following components: (i) a fully automated pre-processing pipeline for custom data sets creation related to specific regional gastronomy, (ii) a transfer learning-based training paradigm to filter out noisy labels through loss ranking, incorporating a Russian Roulette probabilistic approach to mitigate data imbalance problems, and (iii) a method for deploying the resulting model on smartphones for real-time inferences. We assess the performance of our framework on a real-world noisy public domain data set, ETH Food-101, and two novel web-collected datasets, MENA-150 and Pizza-Styles. We demonstrate the filtering capabilities of our proposed method through embedding visualization of the feature space using the t-SNE dimension reduction scheme. Our filtering scheme is efficient and effectively improves accuracy in all cases, boosting performance by 0.96, 0.71, and 1.29% on MENA-150, ETH Food-101, and Pizza-Styles, respectively.

Dissociating Semantic Integration and Inhibitory Control in the Remote Associates Test: A tDCS-EEG Study

ABSTRACT Neuromodulation was utilized here to investigate the distinct involvement of two recognized cortical hubs for semantic integration (the left anterior temporal lobe, lATL) and inhibitory control (the right dorsolateral prefrontal cortex, rDLPFC) in creative problem-solving. Participants were presented with a list of category-exemplar words, selectively recalled some of them, and then solved a set of RAT problems. Selective retrieval was introduced to trigger inhibitory control over competitors. Critically, some RAT problems could be solved with words from the previous phases of the experiment, including words that might be less accessible due to inhibition. Other problems, however, could only be solved with unpresented words. Experiment 1 showed that anodal tDCS over the lATL had a negative effect on the production of correct responses to baseline RAT problems, but not on those that required inhibited solutions. Experiment 2 produced the reverse pattern with cathodal tDCS over the rDLPFC. Resting-state EEG recordings were obtained before and after delivering tDCS, which also revealed specific tDCS-induced changes in frequency bands depending on the site of stimulation. Overall, these findings provide support for the involvement of semantic and control processes in creative problem solving that are linked to different brain networks.

Early parafoveal semantic integration in natural reading.

Humans can read and comprehend text rapidly, implying that readers might process multiple words per fixation. However, the extent to which parafoveal words are previewed and integrated into the evolving sentence context remains disputed. We investigated parafoveal processing during natural reading by recording brain activity and eye movements using MEG and an eye tracker while participants silently read one-line sentences. The sentences contained an unpredictable target word that was either congruent or incongruent with the sentence context. To measure parafoveal processing, we flickered the target words at 60 Hz and measured the resulting brain responses (i.e. Rapid Invisible Frequency Tagging, RIFT) during fixations on the pre-target words. Our results revealed a significantly weaker tagging response for target words that were incongruent with the previous context compared to congruent ones, even within 100ms of fixating the word immediately preceding the target. This reduction in the RIFT response was also found to be predictive of individual reading speed. We conclude that semantic information is not only extracted from the parafovea but can also be integrated with the previous context before the word is fixated. This early and extensive parafoveal processing supports the rapid word processing required for natural reading. Our study suggests that theoretical frameworks of natural reading should incorporate the concept of deep parafoveal processing.

Early parafoveal semantic integration in natural reading

Early parafoveal semantic integration in natural reading

The analysis of firm web data for predicting company innovativeness: a comparison across different types of innovation

ABSTRACT This paper investigates which of the core types of innovation can be best predicted based on ’firms’ website data. In particular, we focus on four distinct key standard types of innovations in firms: product, process, organisational, and marketing innovation. Web mining of textual data on the websites of firms from Estonia, combined with the application of artificial intelligence (AI) methods, turned out to be a suitable approach to predict firm-level innovation indicators. The key novel addition to the existing literature is the finding that web mining is more applicable to predicting marketing innovation than the other three core types of innovation. As AI-based models are often black-box in nature, for transparency, we use an explainable AI approach (SHAP - SHapley Additive exPlanations), where we look at the most important words predicting a particular type of innovation. Our models confirm that the marketing innovation indicator from survey data was clearly related to marketing-related terms on the ’firms’ websites. In contrast, the results on the relevant words on websites for other innovation indicators were much less clear. Our analysis concludes that the effectiveness of web-scraping and web-text-based AI approaches in predicting cost-effective, granular and timely firm-level innovation indicators varies according to the type of innovation considered.

Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology

Background Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. Methods To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates: 1. An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications. 2. The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets. 3. The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. Results EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. Conclusions The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers

Ontological description method for building service-oriented distributed learning systems

The subject of the research is the analysis and justification of the use of software engineering processes based on ontologies of data access and representation of access to knowledge bases and knowledge reuse in the language of algebra of finite predicates, which is a class of descriptive logics. This approach allows you to use the advantages of ontologies and logic programming in the refactoring of distributed distance learning systems. The purpose of the work is to create a formalism for describing the interaction of services and algorithms for building web service interfaces for the implementation of an effective SOA system using the paradigm of access to data based on ontologies. To achieve the goal, the following tasks were solved: an analysis of the main types of interaction structures of SOA services, an overview of implementations of systems working with a family of descriptive logic languages, namely, algebra of finite predicates, and a description of the structure of the ontology necessary for the operation of service-oriented systems, which has the markup of the Semantic Web standard using finite predicate algebra equations. The task is an urgent task of ensuring the organization of interaction of Internet resources in the development of distributed virtual learning systems, and such interaction is enabled by the service-oriented architecture of software development. The article examines modern technologies of the Semantic Web and their role in ensuring the creation of applied software systems of distributed learning resources built using distributed interactive services. The considered methods and materials include methods of algebra of finite predicates, theory of algorithms, object-oriented design, theory of unification. The results. The semantics of the SOA system is obtained, which makes it possible to describe SOA systems in the languages of descriptive logic. The effectiveness of the mathematical formalism of the algebra of finite predicates for the tasks of logical analysis of applied ontologies, the use of knowledge reuse methods, and the description of service-oriented systems is shown. The necessity of using algorithms for automatic construction of web interfaces is shown. The conclusions of the work highlight the advantages of the researched solution, namely, algorithms for the automated construction of web service interfaces for SOA architecture, which make it possible to automatically build web service interfaces. Algorithms differ in a more general model and speed of work and require an assessment of complexity.

Exploring the Synergy of Web Usage Data and Content Mining for Personalized Effectiveness

In light of the exponential growth of web data and user volume, individuals are increasingly overwhelmed by information overload on the internet. Addressing this challenge, our study focuses on enhancing web information retrieval and presentation by leveraging web data mining techniques to uncover intrinsic relationships within textual, linkage, and usability data. Specifically, we aim to improve the performance of web information retrieval and presentation by analysing web data features. Our approach centres on web usage mining to identify usage patterns and integrate this knowledge with user profiles for personalized content delivery. Personalization, tailored to user’s characteristics and behaviours, serves to enhance engagement, conversion, and long-term commitment to websites. The objective of our research is to develop a web personalization system that enables users to access relevant website content without the need for explicit queries. This paper presents an extensive survey of various approaches proposed by researchers in the field of web personalization. It highlights the diverse methodologies and techniques employed to enhance user experience and engagement on the web. The paper identifies key challenges that require urgent attention to advance the field of web personalization.

Leveraging Linked Data for Knowledge Management: A Proposal for the Aerospace Industry

Within a given subculture or industry, knowledge proliferates insofar as those outside the sphere of knowledge lack an understanding of the terms, concepts, and ideas specific to the domain. Moreover, collaborative work within large industries suffers from a lack of understanding between collaborators and from the use of nonstructured legacy knowledge artifacts. The aerospace and defense industries are no exception; indeed, they present examples of some of the largest industries with extensive projects and a diverse array of applications. This paper presents a state-of-the-art literature review of knowledge management and knowledge collaboration systems in the aerospace and defense industries. Based on the review findings, a proposal for an extensible and queryable knowledge management system for aerospace industry workspaces using the Web Ontology Language of the Semantic Web is presented.

THE DIGITAL ENGINEERING FACTORY: CONSIDERATIONS, CURRENT STATUS, AND LESSONS LEARNED

AbstractIn industry, the advancement of digital engineering and the digital thread aims to reduce the impact of knowledge ‘siloes’ by providing a way to integrate data across the entire system lifecycle and across multiple domains. In a typical engineering curriculum, however, courses are still treated as ‘siloes’, and students often do not have the opportunity to experience this industrially relevant approach to engineering. The Digital Engineering Factory (DEF) is a digital engineering environment under development at the University of Arizona to support engineering students. The DEF supports students by providing access to multiple engineering tools and is structured using a ‘hub‐and‐spoke’ approach to consolidate data from these tools. Through this connected architecture, students can transfer data generated in a particular course to tools for use in other courses. Connecting course activities in this way enables students to experience a complete end‐to‐end system lifecycle. At its ‘hub’, the DEF uses Violet to integrate data from multiple sources, create a digital thread, and generate a graph representation of the dataset. This knowledge graph, written in the Ontological Modeling Language (OML), can be viewed in OML Rosetta and is structured according to the University of Arizona Ontology Stack (UAOS). The use of the UAOS and OML Rosetta allows instructors to leverage semantic web technologies to support teaching activities such as grading. In this paper, the authors review the objectives of the DEF, discuss the status of the project, and highlight current limitations and lessons learned with regards to its deployment. These may be useful to inform similar developments in industrial settings.

Ontology-based Food Menu Recommender System for Pregnant Women Using SWRL Rules

Pregnancy is a crucial period in a woman's life because her body must prepare and support the growth and development of the fetus. During pregnancy nutritional needs will increase. Lack of nutritional intake during pregnancy can cause serious health problems, one of which is anemia. However, excess nutrition during pregnancy also has a negative impact on pregnant women. Therefore, a recommender system is required to provide food menu recommendations according to the daily nutritional needs of pregnant women. Currently, there has been a lot of research on ontology-based food recommender systems that can provide food recommendations to users, but there is no research that specifically provides food menu recommendations that suit the needs of pregnant women. Therefore, in this research, we propose an ontology-based food menu recommender system using SWRL (Semantic Web Rule Language) rules for pregnant women. In this food menu recommender system, ontology is used to represent food knowledge and its nutritional content, and SWRL rules are used to reason logical rules in the ontology to determine the appropriate food menu for pregnant women. This recommender system also considers diseases and allergies that pregnant women have so that it can provide food menu recommendations that are more suitable for users. From 15 data samples from pregnant women, the system provides 75 food menu recommendations for pregnant women. Based on the validation results that have been carried out, the precision value is 0.986, the recall is 1, and the F1-score is 0.992.

Global bioethics in european context

The article analyses the foundations and principles of global bioethics in the European ethical, legal and philosophical contexts. An analysis of European bioethics shows that there are at least three modern models of European bioethics: bioethics as metaethics; bioethics as biolaw; bioethics as applied biomedical ethics. European bioethics originates in the global bioethics of V.R. Potter and F. Jahr, and encompasses not only moral issues of medicine and public health, but also global environmental and social problems. In this context, the convergence of the natural and the human is seen as a rehabilitation of practical philosophy, a further study and development of the principles of practical reason, understood by analogy with the objective laws of nature. The study of constitutions, legislative and legal regulations of European countries has revealed a variety of bioethical principles in the field of biomedicine. However, at the level of legislation in biomedicine, there is a certain influence of the American model of bioethics, biomedical ethics, which is manifested in the principlism methodology and the weak connection between medical and environmental issues. Global bioethics in the European context is characterized by an intensification of bioethical reflection, social relationships and legislative activity in biomedicine within pan-European structures, such as the European Council and the European Union. The defining feature of global bioethics as a phenomenon of European culture and ethos is that its principles can be “activated” only as a semantic and logical integrity. Bioethical principles serve as a kind of harmonising factor of European biopolitics.

Enhancing Semantic Interoperability in Bird Classification through XML/RDF and SPARQL

Classification of birds is the area that is quite complex and involved such a lot of varieties that demands the correct as well as the professional organization of the information. This study addresses the problem of semantic interoperability in the bird categorization process by means of constructing an ontology which covers all the words used in taxonomic descriptions. The paper exploits XML/RDF standards for semantic web compatibility and Open Link Virtuoso SPARQL Query Editor that enables simple interaction with its tools and visualization in the process of querying and presentation of results. Attempt to develop a specific ontology for bird identification for the purpose of optimizing the accessibility and retrieval of heterogeneous bird-oriented data that ultimately help in building effective knowledge management system in this field. The effectiveness of an ontology is evaluated by its ability to make possible classification of diverse information. This research methodology is proposed for implementation in a broad range of researches, education, and conservation programs that can targeted to enhance their output and increase accuracy.

Integrated representation of technical systems with BIM and linked data: TUBES system ontology

Technical systems in the Architecture, Engineering, Construction, and Operations (AECO) industry are complex interconnected structures. To cope with this high complexity, large amounts of data must be processed and exchanged throughout the whole life cycle of these systems. Building Information Modeling (BIM) and Semantic Web Technologies (SWT) offer the possibility to structure and link this data as well as to define common terminology. Semantic Web ontologies describing HVAC systems in the AECO industry have been developed, but an ontology for an integrated representation based on general system theory is missing. Therefore, this study proposes the TUBES System Ontology (TSO) as a knowledge representation for an integrated, explicit representation of linked technical systems, their hierarchical subdivision, structural and functional interconnections, and relationships to spatial entities. The contribution is validated on two example models, and the results are critically discussed, pointing out corresponding limitations as well as the need for further research.

Guest editorial: Recent trends in semantic web and knowledge graphs

Guest editorial: Recent trends in semantic web and knowledge graphs

The temporal dynamics of conscious and unconscious audio-visual semantic integration

We compared the time course of cross-modal semantic effects induced by both naturalistic sounds and spoken words on the processing of visual stimuli, whether visible or suppressed form awareness through continuous flash suppression. We found that, under visible conditions, spoken words elicited audio-visual semantic effects over longer time (−1000, −500, −250 ms SOAs) than naturalistic sounds (−500, −250 ms SOAs). Performance was generally better with auditory primes, but more so with congruent stimuli. Spoken words presented in advance (−1000, −500 ms) outperformed naturalistic sounds; the opposite was true for (near-)simultaneous presentations. Congruent spoken words demonstrated superior categorization performance compared to congruent naturalistic sounds. The audio-visual semantic congruency effect still occurred with suppressed visual stimuli, although without significant variations in the temporal patterns between auditory types. These findings indicate that: 1. Semantically congruent auditory input can enhance visual processing performance, even when the visual stimulus is imperceptible to conscious awareness. 2. The temporal dynamics is contingent on the auditory types only when the visual stimulus is visible. 3. Audiovisual semantic integration requires sufficient time for processing auditory information.

How the reviewer’s historical ratings influence the review helpfulness?: focus on the extremity and negativity

PurposeAs online reviews have become a potent marketing tool, the underlying motivation has come into question. Focus has shifted towards assessing reviewer credibility before appraising online review credibility. Guided by source credibility theory, this study investigates the effect of reviewers’ historical ratings on review helpfulness to gain insight into the role of reviewer credibility.Design/methodology/approachWe explore readers’ underlying psychological processes using web data analysis (Study 1) and experiments (Study 2). Study 1 empirically examines the effect of reviewers’ historical ratings on review helpfulness using 100,621 reviews authored by 890 TripAdvisor reviewers. Study 2 involves two experiments with 328 participants to scrutinize the readers’ underlying mechanisms in establishing reviewer credibility, with a specific focus on the effect of reviewers’ historical ratings.FindingsWhen a reviewer’s historical ratings are predominantly extreme, readers perceive the reviewer as less credible, leading to decreased helpfulness in reviews authored by that reviewer. Interestingly, high negativity in historical ratings does not have a significant effect on either reviewer credibility or review helpfulness.Originality/valueThis study offers two significant contributions to the existing literature. First, it extends previous research on review helpfulness by incorporating reviewers’ historical rating behavior. This provides a more comprehensive understanding of the factors that influence the perceived usefulness of reviews. Second, it integrates two distinct research methods: TripAdvisor web data analysis and experiments. This methodological synthesis enhances the robustness of the study by offering a more nuanced and well-rounded perspective on the dynamics between reviewers’ historical ratings and perceived helpfulness of reviews.