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Touch-based fingerprints are widely used in today's world;even with all the success, the touch-based nature of these is a threat, especially in this COVID-19 period. A solution to the same is the introduction of Touchless Fingerprint Technology. The workflow of a touchless system varies vastly from its touch-based counterpart in terms of acquisition, pre-processing, image enhancement, and fingerprint verification. One significant difference is the methods used to segment desired fingerprint regions. This literature focuses on pixel-level classification or semantic segmentation using U-Net, a key yet challenging task. A plethora of semantic segmentation methods have been applied in this field. In this literature, a spectrum of efforts in the field of semantic segmentation using U-Net is investigated along with the components that are integral while training and testing a model, like optimizers, loss functions, and metrics used for evaluation and enumeration of results obtained. © 2022 IEEE.
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The CT images of lungs of COVID-19 patients have distinct pathological features, segmenting the lesion area accurately by the method of deep learning, which is of great significance for the diagnosis and treatment of COVID-19 patients. Instance segmentation has higher sensitivity and can output the Bounding Boxes of the lesion region, however, the traditional instance segmentation method is weak in the segmentation of small lesions, and there is still room for improvement in the segmentation accuracy. We propose a instance segmentation network which is called as Semantic R-CNN. Firstly, a semantic segmentation branch is added on the basis of Mask-RCNN, and utilizing the image processing tool Skimage in Python to label the connected domain for the result of semantic segmentation, extracting the rectangular boundaries of connected domain and using them as Proposals, which will replace the Regional Proposal Network in the instance segmentation. Secondly, the Atrous Spatial Pyramid Pooling is introduced into the Feature Pyramid Network, then improving the feature fusion method in FPN. Finally, the cascade method is introduced into the detection branch of the network to optimize the Proposals. Segmentation experiments were carried out on the pathological lesion segmentation data set of CC-CCII, the average accuracy of the semantic segmentation is 40.56mAP, and compared with the Mask-RCNN, it has improved by 9.98mAP. After fusing the results of semantic segmentation and instance segmentation, the Dice coefficient is 80.7%, the sensitivity is 85.8%, and compared with the Inf-Net, it has increased by 1.6% and 8.06% respectively. The proposed network has improved the segmentation accuracy and reduced the false-negatives. © 2022 ACM.
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Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.
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In recent years, COVID-19 has impacted all aspects of human life. As a result, numerous publications relating to this disease have been issued. Due to the massive volume of publications, some retrieval systems have been developed to provide researchers with useful information. In these systems, lexical searching methods are widely used, which raises many issues related to acronyms, synonyms, and rare keywrds. In this paper, we present a hybrid relation retrieval system, CovRelex-SE, based on embeddings to provide high-quality search results. Our system can be accessed through the following URL: https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/. © 2023 Association for Computational Linguistics.
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In research and policies, the identification of trends as well as emerging topics and topics in decline is an important source of information for both academic and innovation management. Since at present policy analysis mostly employs qualitative research methods, the following article presents and assesses different approaches – trend analysis based on questionnaires, quantitative bibliometric surveys, the use of computer-linguistic approaches and machine learning and qualitative investigations. Against this backdrop, this article examines digital applications in cultural heritage and, in particular, built heritage via various investigative frameworks to identify topics of relevance and trendlines, mainly for European Union (EU)-based research and policies. Furthermore, this article exemplifies and assesses the specific opportunities and limitations of the different methodical approaches against the backdrop of data-driven vs. data-guided analytical frameworks. As its major findings, our study shows that both research and policies related to digital applications for cultural heritage are mainly driven by the availability of new technologies. Since policies focus on meta-topics such as digitisation, openness or automation, the research descriptors are more granular. In general, data-driven approaches are promising for identifying topics and trendlines and even predicting the development of near future trends. Conversely, qualitative approaches are able to answer "why” questions with regard to whether topics are emerging due to disruptive innovations or due to new terminologies or whether topics are becoming obsolete because they are common knowledge, as is the case for the term "internet”.
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Public interest in where food comes from and how it is produced, processed, and distributed has increased over the last few decades, with even greater focus emerging during the COVID-19 pandemic. Mounting evidence and experience point to disturbing weaknesses in our food systems' abilities to support human livelihoods and wellbeing, and alarming long-term trends regarding both the environmental footprint of food systems and mounting vulnerabilities to shocks and stressors. How can we tackle the "wicked problems” embedded in a food system? More specifically, how can convergent research programs be designed and resulting knowledge implemented to increase inclusion, sustainability, and resilience within these complex systems, support widespread contributions to and acceptance of solutions to these challenges, and provide concrete benchmarks to measure progress and understand tradeoffs among strategies along multiple dimensions? This article introduces and defines food systems informatics (FSI) as a tool to enhance equity, sustainability, and resilience of food systems through collaborative, user-driven interaction, negotiation, experimentation, and innovation within food systems. Specific benefits we foresee in further development of FSI platforms include the creation of capacity-enabling verifiable claims of sustainability, food safety, and human health benefits relevant to particular locations and products;the creation of better incentives for the adoption of more sustainable land use practices and for the creation of more diverse agro-ecosystems;the wide-spread use of improved and verifiable metrics of sustainability, resilience, and health benefits;and improved human health through better diets.
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This paper presents the Coronavirus Disease Ontology (CovidO), a superset of the available Coronavirus (COVID-19) ontologies, including all the possible dimensions. CovidO consists of an ontological network of thriving distinct dimensions for storing coronavirus information. CovidO has 175 classes, 169 properties, 4141 triples, 645 individuals with 264 nodes and 308 edges. CovidO is based on standard input of coronavirus disease data sources, activities, and related sources, which collects and validates records for decision-making used to set guidelines and recommend resources. We present CovidO to a growing community of artificial intelligence project developers as pure metadata and illustrate its importance, quality, and impact. The ontology developed in this work addresses grouping the existing ontologies to build a global data model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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PurposeResearchers and standardisation bodies are key to accelerating societal transition and contributing to society's demands for sustainability, resilience and digitalisation. Standards are the agreed-upon best practices published by national or international bodies and are key enablers of transformation. Scholars have not yet identified a relationship between standards and facilities management (FM) research. The purpose of this paper is to investigate the role of formal standards in FM research.Design/methodology/approachA literature review was conducted to identify journal articles addressing standards and FM. A total of 198 journal articles published from 2010 to 2021 were identified. After screening these articles, 27 journal articles were considered the most relevant for data analysis.FindingsThe findings show that the role of standards in research can be analysed thematically, categorically, textually, methodologically and directionally. Standards are relevant to research by defining terms, creating backgrounds, guiding research, supporting the development of new standards and encouraging more collaboration between research and standardisation. Some studies have shown how standards influence research, but only a few have explored how research influences standards.Research limitations/implicationsThis research provides examples that inspire stronger collaboration between people and processes in research and standardisation.Originality/valueThe articles collected and analysed in this study comprise original research. A limited preliminary study of ten core articles was presented at the International Council for Research and Innovation in Building and Construction World Congress 2022. This presentation of this work provides an expanded framework for analysing the roles of standards in research. This framework includes (1) categorical analysis of research and standardisation streams;(2) thematic analysis of the topic of interest;(3) textual analysis of the use of the term "standard”;(4) methodological analysis of the influence of standards on the research method;and (5) directional analysis of the intended audience.
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During the last decade, ontology engineering has undoubtedly participated in a lot of beneficial applications in different domains. Nevertheless, ontology development still faces several significant challenges that need to be addressed. This study proposes an enhanced architecture for the ontology development lifecycle. With the help of this architecture, users can complete ontology development tasks since it provides guidance for all key activities, from requirement specification to ontology evaluation. Ontology-driven conceptual modeling (ODCM) and ontology matching serve as the foundation of this architecture. ODCM is defined as the application of ontological ideas from various fields to build engineering objects that improve conceptual modeling. Ontology matching is a promising approach to overcome the semantic heterogeneity challenge between different ontologies. The proposed architecture is applied to e-governance domain, which is one of the online services that gains a great attention worldwide, especially during the coronavirus pandemic. However, residents of Arab countries face numerous obstacles and do not receive the full benefits of e-governance. For these reasons, Egyptian e-government is selected as the suggested case study. The results are encouraging when the produced ontology is compared with 20 existing ontologies from the same domain. On the basis of OntoMetrics, the average values of metrics correlated to accuracy, understandability, cohesion and conciseness lie in the 95th, 95th, 95th and 57th percentiles respectively. The results can be further enhanced by defining more non-inheritance relations and distributing the instances across all classes. © 2022. ISCA
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COVID-19 is a very dangerous pandemic attacking the respiratory organs of humans. It is characterized by its contagious speed, especially with its last versions. Effectiveness of confrontation resides in a strategy based on the speed of intervention, early detection, and appropriate and quick treatment. However, this strategy requires more effort and enormous human, material, and financial resources. The latter situation requires a much more efficient solution based on using new technologies. Through the integration of the internet of things in healthcare, the quality of the latter will be improved. This integration requires suitable architecture represent the foundation for the system that handles the data generated by this technology. To concretize this strategy, we propose an approach based on IoT architecture inspired by the organization of the Algerian health structure for fighting COVID-19. The architecture allows the organization to manage resources and ensure adequate resources. The approach also consists of a semantic web-based system to handle the heterogeneity of data sources and exchange it with different applications. The system is based on a proposed fuzzy ontology that helps to treat vague and imprecise data that characterize the medical domain. The fuzzy ontology is developed by reusing a standard IoT ontology for allowing the sharing and reuse of IoT data. It also uses the NEWS2 score system for defining membership functions. We conducted an experimental study, and the results of the proposed approach were compared with those obtained by physician assessment. The results show that the approach is effective, and even if COVID-19 disappears soon according to the World Health Organization indications, the proposed approach will still be valid for any other epidemic that may occur in the future or for any other disease. © 2023 Iraqi Journal for Computer Science and Mathematics. All rights reserved.
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Fighting against misinformation and computational propaganda requires integrated efforts from various domains like law or education, but there is also a need for computational tools. I investigate here how reasoning in Description Logics (DLs) can detect inconsistencies between trusted knowledge and not trusted sources. The proposed method is exemplified on fake news for the new coronavirus. Indeed, in the context of the Covid-19 pandemic, many were quick to spread deceptive information. Since, the not-trusted information comes in natural language (e.g. "Covid-19 affects only the elderly”), the natural language text is automatically converted into DLs using the FRED tool. The resulted knowledge graph formalised in Description Logics is merged with the trusted ontologies on Covid-10. Reasoning in Description Logics is then performed with the Racer reasoner, which is responsable to detect inconsistencies within the ontology. When detecting inconsistencies, a "red flag” is raised to signal possible fake news. The reasoner can provide justifications for the detected inconsistency. This availability of justifications is the main advantage compared to approaches based on machine learning, since the system is able to explain its reasoning steps to a human agent. Hence, the approach is a step towards human-centric AI systems. The main challenge remains to improve the technology which automatically translates text into some formal representation. © 2022, Springer Nature Switzerland AG.
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Semantic relation prediction aims to mine the implicit relationships between objects in heterogeneous graphs, which consist of different types of objects and different types of links. In real-world scenarios, new semantic relations constantly emerge and they typically appear with only a few labeled data. Since a variety of semantic relations exist in multiple heterogeneous graphs, the transferable knowledge can be mined from some existing semantic relations to help predict the new semantic relations with few labeled data. This inspires a novel problem of few-shot semantic relation prediction across heterogeneous graphs. However, the existing methods cannot solve this problem because they not only require a large number of labeled samples as input, but also focus on a single graph with a fixed heterogeneity. Targeting this novel and challenging problem, in this paper, we propose a Meta-learning based Graph neural network for Semantic relation prediction, named MetaGS. Firstly, MetaGS decomposes the graph structure between objects into multiple normalized subgraphs, then adopts a two-view graph neural network to capture local heterogeneous information and global structure information of these subgraphs. Secondly, MetaGS aggregates the information of these subgraphs with a hyper-prototypical network, which can learn from existing semantic relations and adapt to new semantic relations. Thirdly, using the well-initialized two-view graph neural network and hyper-prototypical network, MetaGS can effectively learn new semantic relations from different graphs while overcoming the limitation of few labeled data. Extensive experiments on three real-world datasets have demonstrated the superior performance of MetaGS over the state-of-the-art methods. IEEE
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For the purpose of capturing the semantic information accurately and clarifying the user's questioning intention, this paper proposes a novel, ensemble deep architecture BERT-MSBiLSTM-Attentions (BMA) which uses the Bidirectional Encoder Representations from Transformers (BERT), Multi-layer Siamese Bi-directional Long Short Term Memory (MSBiLSTM) and dual attention mechanism (Attentions) in order to solve the current question semantic similarity matching problem in medical automatic question answering system. In the preprocessing part, we first obtain token-level and sentence-level embedding vectors that contain rich semantic representations of complete sentences. The fusion of more accurate and adequate semantic features obtained through Siamese recurrent network and dual attention network can effectively eliminate the effect of poor matching results due to the presence of certain non-canonical texts or the diversity of their expression ambiguities. To evaluate our model, we splice the dataset of Ping An Healthkonnect disease QA transfer learning competition and "public AI star” challenge - COVID-19 similar sentence judgment competition. Experimental results with CC19 dataset show that BMA network achieves significant performance improvements compared to existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Introduction: One of the ways of coping with COVID-19 concerns aspects related to the production and dissemination of reliable, clear and quickly understood information. There are many communicational and informational actions and initiatives in favor of dissemination and of the means that guarantee the acceptability, adherence and compliance with the prevention and control measures of COVID-19. This research aims to develop a digital environment, understood here as a panel with topics related to COVID-19, based on SPARQL Protocol and RDF Query Language (SPARQL) queries and on the Wikidata dataset. Method: To do so, a theoretical and applied methodology is used, based on the Systematic Literature Review to support the construction of the conceptual corpus underlying the computational technologies from the Semantic Web and Linked Data and its application in the structuring and modeling of the environment, for making scientific data available and sharing. Results: The data collected in the Systematic Literature Review reveal little scientific production available at the international level, however, interesting initiatives are already concerned with the openness and availability of scientific data on the Web. In addition, the information panel on COVID-19 developed is categorized into six main axes, such as Map COVID-19, Symptoms of COVID-19, Possible treatments, Taxonomy, Related works and Related images. Conclusion: Thus, the information panel about COVID-19 presents itself as a digital environment that enhances the visualization, access and sharing of data and information for heterogeneous users, contributing to the transfer of consistent, structured and reliable information, as well as the promotion of public guidelines for controlling the spread of the disease. © 2020 Arakaki, Castro & Arakaki.
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In the last years, Learning Management systems (LMSs) are acquiring great importance in online education, since they offer flexible integration platforms for organising a vast amount of learning resources, as well as for establishing effective communication channels between teachers and learners, at any direction. These online platforms are then attracting an increasing number of users that continuously access, download/upload resources and interact each other during their teaching/learning processes, which is even accelerating by the breakout of COVID-19. In this context, academic institutions are generating large volumes of learning-related data that can be analysed for supporting teachers in lesson, course or faculty degree planning, as well as administrations in university strategic level. However, managing such amount of data, usually coming from multiple heterogeneous sources and with attributes sometimes reflecting semantic inconsistencies, constitutes an emerging challenge, so they require common definition and integration schemes to easily fuse them, with the aim of efficiently feeding machine learning models. In this regard, semantic web technologies arise as a useful framework for the semantic integration of multi-source e-learning data, allowing the consolidation, linkage and advanced querying in a systematic way. With this motivation, the e-LION (e-Learning Integration ONtology) semantic model is proposed for the first time in this work to operate as data consolidation approach of different e-learning knowledge-bases, hence leading to enrich on-top analysis. For demonstration purposes, the proposed ontological model is populated with real-world private and public data sources from different LMSs referring university courses of the Software Engineering degree of the University of Malaga (Spain) and the Open University Learning. In this regard, a set of four case studies are worked for validation, which comprise advance semantic querying of data for feeding predictive modelling and time-series forecasting of students' interactions according to their final grades, as well as the generation of SWRL reasoning rules for student's behaviour classification. The results are promising and lead to the possible use of e-LION as ontological mediator scheme for the integration of new future semantic models in the domain of e-learning. • e-LION semantic approach is proposed for e-learning data source integration. • An OWL Ontology is designed for e-learning, including SWRL reasoning rules. • The proposal is validated with four real-world (Moodle) and academic cases study. • Obtained semantised data successfully feed predictive machine learning models. • We provide actual e-learning users with a model to enhance their analytics. [ FROM AUTHOR]
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The effective receptive field of a fully convolutional neural network is an important consideration when designing an architecture, as it defines the portion of the input visible to each convolutional kernel. We propose a neural network module, extending traditional skip connections, called the translated skip connection. Translated skip connections geometrically increase the receptive field of an architecture with negligible impact on both the size of the parameter space and computational complexity. By embedding translated skip connections into a benchmark architecture, we demonstrate that our module matches or outperforms four other approaches to expanding the effective receptive fields of fully convolutional neural networks. We confirm this result across five contemporary image segmentation datasets from disparate domains, including the detection of COVID-19 infection, segmentation of aerial imagery, common object segmentation, and segmentation for self-driving cars. © 2022 IEEE.
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Last years witnessed a shift from the potential utility in digitisation to a crucial need to enjoy activities virtually. In fact, before 2019, data curators recognised the utility of performing data digitisation, while during the lockdown caused by the COVID-19, investing in virtual and remote activities to make culture survive became crucial as no one could enjoy Cultural Heritage in person. The Cultural Heritage community heavily invested in digitisation campaigns, mainly modelling data as Knowledge Graphs by becoming one of the most successful Semantic Web technologies application domains. Despite the vast investment in Cultural Heritage Knowledge Graphs, the syntactic complexity of RDF query languages, e.g., SPARQL, negatively affects and threatens data exploitation, risking leaving this enormous potential untapped. Thus, we aim to support the Cultural Heritage community (and everyone interested in Cultural Heritage) in querying Knowledge Graphs without requiring technical competencies in Semantic Web technologies. We propose an engaging exploitation tool accessible to all without losing sight of developers' technological challenges. Engagement is achieved by letting the Cultural Heritage community leave the passive position of the visitor and actively create their Virtual Assistant extensions to exploit proprietary or public Knowledge Graphs in question-answering. By accessible to all, we mean that the proposed software framework is freely available on GitHub and Zenodo with an open-source license. We do not lose sight of developers' technical challenges, which are carefully considered in the design and evaluation phases. This article first analyses the effort invested in publishing Cultural Heritage Knowledge Graphs to quantify data developers can rely on in designing and implementing data exploitation tools in this domain. Moreover, we point out challenges developers may face in exploiting them in automatic approaches. Second, it presents a domain-agnostic Knowledge Graph exploitation approach based on virtual assistants as they naturally enable question-answering features where users formulate questions in natural language directly by their smartphones. Then, we discuss the design and implementation of this approach within an automatic community-shared software framework (a.k.a. generator) of virtual assistant extensions and its evaluation in terms of performance and perceived utility according to end-users. Finally, according to a taxonomy of the Cultural Heritage field, we present a use case for each category to show the applicability of the proposed approach in the Cultural Heritage domain. In overviewing our analysis and the proposed approach, we point out challenges that a developer may face in designing virtual assistant extensions to query Knowledge Graphs, and we show the effect of these challenges in practice.
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BACKGROUND: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. OBJECTIVE: The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America reports. METHODS: The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing. RESULTS: The current version of the DAO comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreased the false alarm rate by adding external knowledge to the machine learning process. The ontology is recurrently updated to capture evolving concepts in different contexts and applied to analyze data related to social media and dark web marketplaces. CONCLUSIONS: The DAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research.
Subject(s)
COVID-19 , Social Media , Substance-Related Disorders , Humans , United States/epidemiology , Artificial Intelligence , Pandemics , COVID-19/epidemiology , Substance-Related Disorders/epidemiology , Analgesics, OpioidABSTRACT
During the last decade, communities at local and national level are implementing actions geared towards improving disaster resilience. In this context, the importance of ICT in disaster risk management is rapidly increasing globally, especially nowadays amidst the climate crisis and the covid-19 pandemic. However, disaster risk management operations require contributions and collaboration of different type of actors and infrastructures with different functions, rules, protocols and datasets, forming complex contexts in decision making and event coordination. Hence, semantic interoperability between the various stakeholders is one of the challenges to be confronted. In this paper, we present the RES-Q (RESCUE) approach that proposes an information technology solution concerning the real-time recommendation and orchestration of post-disaster response plans. The implemented RES-Q prototype comprises an expert system and a workflow execution engine based on an ontological infrastructure for modeling the response actions for each type of disaster. The ontological model is designed using a multi-layer approach encapsulating the required knowledge streams and a semantic rule repository. During the execution of a post-disaster plan, the system reasons over the rules and composes the next steps of the corresponding response processes. The rule repository is able to infer new knowledge as each plan progresses, which can update the RES-Q ontology accordingly. © 2022, National Technical University of Athens. All rights reserved.
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Ontology is the kernel technique of the Semantic Web (SW), which models the domain knowledge in a formal and machine-understandable way. To ensure different ontologies’ communications, the cutting-edge technology is to determine the heterogeneous entity mappings through the ontology matching process. During this procedure, it is of utmost importance to integrate different similarity measures to distinguish heterogeneous entity correspondence. The way to find the most appropriate aggregating weights to enhance the ontology alignment’s quality is called ontology meta-matching problem, and recently, Evolutionary Algorithm (EA) has become a great methodology of addressing it. Classic EA-based meta-matching technique evaluates each individual through traversing the reference alignment, which increases the computational complexity and the algorithm’s running time. For overcoming this drawback, an Interpolation Model assisted EA (EA-IM) is proposed, which introduces the IM to predict the fitness value of each newly generated individual. In particular, we first divide the feasible region into several uniform sub-regions using lattice design method, and then precisely evaluate the Interpolating Individuals (INIDs). On this basis, an IM is constructed for each new individual to forecast its fitness value, with the help of its neighborhood. For testing EA-IM’s performance, we use the Ontology Alignment Evaluation Initiative (OAEI) Benchmark in the experiment and the final results show that EA-IM is capable of improving EA’s searching efficiency without sacrificing the solution’s quality, and the alignment’s f-measure values of EA-IM are better than OAEI’s participants.