4,153 results on '"Semantic data model"'
Search Results
2. Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems.
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tom Wörden, Henrik, Spreckelsen, Florian, Luther, Stefan, Parlitz, Ulrich, and Schlemmer, Alexander
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INFORMATION technology ,DATA modeling ,DATA management ,DATA integration ,DATA visualization - Abstract
Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous use of the file system and RDMS. This simultaneous use can have many benefits, but keeping data in RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow for keeping file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS LinkAhead (previously named "CaosDB"). [ABSTRACT FROM AUTHOR]
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- 2024
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3. Data-driven and LCA-based Framework for environmental and circular assessment of Modular Curtain Walls
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Luca Morganti, Peru Elguezabal Esnarrizaga, Alessandro Pracucci, Theo Zaffagnini, Veronica Garcia Cortes, Andreas Rudenå, Birgit Brunklaus, and Julen Astudillo Larraz
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Custom prefabricated Modules for Curtain Walls ,Life Cycle Assessment ,Digital Product Passport ,Semantic Data Model ,Eco-design tool ,Production Management and Innovation ,Architecture ,NA1-9428 ,Building construction ,TH1-9745 - Abstract
To assist the sustainable development of the building sector, designers require tools illustrating the most viable design options. This paper, starting by presenting the opportunities and limitations of the Life Cycle Assessment (LCA) methodology and Digital Product Passport (DPP) instrument when applied to Custom Modules for Curtain Walls, proposes a Semantic Data-driven Framework to facilitate the design of low-carbon and circular façade modules. Based on literature and the practical outcome of the H2020 project Basajaun, this framework integrates computer-aided technologies that manufacturing companies commonly employ to automate an efficient sustainability assessment process using primary data. This solution innovates industrial process management and architectural design and supports the creation of greener products. It also facilitates the output of documents supporting end-of-life scenarios. The development methodology involves investigating required quantitative project data, environmental factors, and circularity information, as well as the definition of flowcharts for the Life Cycle Inventory, extending a best practice for the façade module’s DPP. Furthermore, the methodology implicates data collection and IT implementation and organisation. This is through the definition of an ontology conceived for interconnection between digital systems. The findings shall contribute to implementing the LCA and DPP practices for custom prefabricated façade modules and suggest areas for further development. Challenges include obtaining and sharing data on environmental impacts and circularity, but involving stakeholders and addressing technical limitations can improve sustainability.
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- 2024
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4. Mapping Hierarchical File Structures to Semantic Data Models for Efficient Data Integration into Research Data Management Systems
- Author
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Henrik tom Wörden, Florian Spreckelsen, Stefan Luther, Ulrich Parlitz, and Alexander Schlemmer
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research data management ,FAIR ,file structure ,file crawler ,semantic data model ,Bibliography. Library science. Information resources - Abstract
Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous use of the file system and RDMS. This simultaneous use can have many benefits, but keeping data in RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow for keeping file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS LinkAhead (previously named “CaosDB”).
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- 2024
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- View/download PDF
5. Clinical Trials Data Management in the Big Data Era
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Perez-Arriaga, Martha O., Poddar, Krishna Ashok, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nepal, Surya, editor, Cao, Wenqi, editor, Nasridinov, Aziz, editor, Bhuiyan, MD Zakirul Alam, editor, Guo, Xuan, editor, and Zhang, Liang-Jie, editor
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- 2020
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6. Information-Driven Monitoring of Production Process: A Semantic Data Model
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Oksana Petrina, Sergei Marchenkov, and Dmitry Korzun
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monitoring ,industrial internet ,semantic data model ,information-driven ,Telecommunication ,TK5101-6720 - Abstract
Recent technologies of industrial monitoring provide highly fragmented information. Large amounts of multiparameter sensed data on production process and equipment operation are stored in disparate structures (databases). Effective use of the collected information requires data fusion within an information-driven monitoring system. In this short paper, we design a semantic data model to create a unified information space that fuses events derived from real-time sensed data streams. Our semantic data model considers a hierarchy of production equipment nodes and supports rules for identifying and composing events in the production process under monitoring.
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- 2021
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7. A forensic-driven data model for automatic vehicles events analysis
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Aymen Akremi
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Vehicle detection ,Forensics requirements ,Semantic data model ,Clustered Cameras network ,Events analysis ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Digital vision technologies emerged exponentially in all living areas to watch, play, control, or track events. Security checkpoints have benefited also from those technologies by integrating dedicated cameras in studied locations. The aim is to manage the vehicles accessing the inspection security point and fetching for any suspected ones. However, the gathered data volume continuously increases each day, making their analysis very hard and time-consuming. This paper uses semantic-based techniques to model the data flow between the cameras, checkpoints, and administrators. It uses ontologies to deal with the increased data size and its automatic analysis. It considers forensics requirements throughout the creation of the ontology modules to ensure the records’ admissibility for any possible investigation purposes. Ontology-based data modeling will help in the automatic events search and correlation to track suspicious vehicles efficiently.
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- 2022
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8. Semantic Data Model
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Schintler, Laurie A., editor and McNeely, Connie L., editor
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- 2022
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9. Construction of Semantic Data Models
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Perez-Arriaga, Martha O., Estrada, Trilce, Abad-Mota, Soraya, Barbosa, Simone Diniz Junqueira, Series Editor, Chen, Phoebe, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Bernardino, Jorge, editor, and Quix, Christoph, editor
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- 2018
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10. Towards a semantics representation framework for narrative images
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Li, Xuhui, Wu, Yanqiu, Wang, Xiaoguang, Qian, Tieyun, and Hong, Liang
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- 2019
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11. Architecture of a Virtual Reality and Semantics-Based Framework for the Return to Work of Wheelchair Users
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Arlati, Sara, Spoladore, Daniele, Mottura, Stefano, Zangiacomi, Andrea, Ferrigno, Giancarlo, Sacchetti, Rinaldo, Sacco, Marco, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, De Paolis, Lucio Tommaso, editor, Bourdot, Patrick, editor, and Mongelli, Antonio, editor
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- 2017
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12. Semantic Data Model
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Embley, David W., Liu, Ling, editor, and Özsu, M. Tamer, editor
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- 2018
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13. DeepSIM: Deep Semantic Information-Based Automatic Mandelbug Classification
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Guanping Xiao, Zheng Zheng, Kishor S. Trivedi, Xiaoting Du, and Zenghui Zhou
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Word embedding ,business.industry ,Computer science ,Deep learning ,computer.software_genre ,Semantic data model ,Convolutional neural network ,Software ,Heisenbug ,Classifier (linguistics) ,Artificial intelligence ,Electrical and Electronic Engineering ,Safety, Risk, Reliability and Quality ,business ,computer ,Word (computer architecture) ,Natural language processing - Abstract
Understanding and predicting types of bugs are of practical importance for developers to improve the testing efficiency and take appropriate steps to address bugs in software releases. However, due to the complex conditions under which faults manifest and the complexity of the classification rules, the automatic classification of Mandelbugs is a difficult task. In this article, we present a deep semantic information-based Mandelbug classification method that combines a semantic model with a deep learning classifier and makes use of both labeled and unlabeled bug reports. By training the bug report semantic model on millions of bug reports, each word in the text of a bug report is represented as a word embedding that preserves the semantic relationship among the words. Then, a convolutional neural network model is designed to capture the high-level features of bug reports to obtain a more accurate classification. Moreover, the effects of the semantic model size and domain on the classification results are investigated, and the quality of word embeddings is evaluated by analyzing several important parameters.
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- 2022
14. Big Data in the Health Sector
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Zillner, Sonja, Neururer, Sabrina, Cavanillas, José María, editor, Curry, Edward, editor, and Wahlster, Wolfgang, editor
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- 2016
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15. K-Culture Time Machine: Development of Creation and Provision Technology for Time-Space-Connected Cultural Contents
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Ha, Taejin, Kim, Younsung, Kim, Eunseok, Kim, Kihong, Lim, Sangmin, Hong, Seungmo, Kim, Jeain, Kim, Sunhyuck, Kim, Junghwa, Woo, Woontack, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Yamamoto, Sakae, editor
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- 2015
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16. Supporting the Design of AAL through a SW Integration Framework: The D4All Project
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Sacco, Marco, Caldarola, Enrico G., Modoni, Gianfranco, Terkaj, Walter, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Stephanidis, Constantine, editor, and Antona, Margherita, editor
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- 2014
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17. COSEM: Collaborative Semantic Map Matching Framework for Autonomous Robots
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Yufeng Yue, Yuanzhe Wang, Chunyang Zhao, Mingxing Wen, and Danwei Wang
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Matching (statistics) ,Computer science ,Association (object-oriented programming) ,computer.software_genre ,Semantic data model ,Transformation (function) ,Control and Systems Engineering ,Robustness (computer science) ,Robot ,Data mining ,Minification ,Electrical and Electronic Engineering ,computer ,Rigid transformation - Abstract
Relative localization is a fundamental requirement for the coordination of multiple robots. To date, existing research in relative localization mainly depends on the extraction of low-level geometry features such as planes, lines, and points, which may fail in challenging cases when initial error is large and overlapping area is low. In this paper, a novel approach named collaborative semantic map matching (COSEM) is proposed to estimate the relative transformation between robots. COSEM jointly performs multimodal information fusion, semantic data association, and optimization in a unified framework. Firstly, each robot applies a multimodal information fusion model to generate local semantic maps. Since the correspondences between local maps are latent variables, a flexible semantic data association strategy is proposed using Expectation-Maximization. Instead of assigning hard geometry data association, semantic association and geometry association are jointly estimated. Then, the minimization of the expected cost results in a rigid transformation matrix between two semantic maps. Evaluations on Semantic KITTI benchmarks and real world experiments show the improved accuracy, convergence, and robustness.
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- 2022
18. MyFishCheck: A Model to Assess Fish Welfare in Aquaculture
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Linda Tschirren, David Bachmann, Ali Cem Güler, Oliver Blaser, Nicola Rhyner, Andreas Seitz, Erich Zbinden, Thomas Wahli, Helmut Segner, and Dominik Refardt
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aquaculture ,fish welfare ,ontology ,semantic data model ,animal welfare assessment ,Veterinary medicine ,SF600-1100 ,Zoology ,QL1-991 - Abstract
Welfare in animal husbandry includes considerations of biology, ethics, ecology, law and economics. These diverse aspects must be translated into common quantifiable parameters and applicable methods to objectively assess welfare in animals. To assist this process in the field of aquaculture, where such methods are largely missing, we developed a model to assess fish welfare. A network of information was created to link needs, i.e., fundamental requirements for welfare, with parameters, i.e., quantifiable aspects of welfare. From this ontology, 80 parameters that are relevant for welfare, have practicable assessment methods and deliver reliable results were selected and incorporated into a model. The model, named MyFishCheck, allows the evaluation of welfare in five distinct modules: farm management, water quality, fish group behaviour, fish external and fish internal appearance, thereby yielding five individual grades categorising welfare ranging from critical, to poor, to acceptable, and good. To facilitate the use of the model, a software application was written. With its adaptability to different fish species, farming systems, regulations and purposes as well as its user-friendly digital version, MyFishCheck is a next step towards improved fish welfare assessment and provides a basis for ongoing positive developments for the industry, the farmers and the fish.
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- 2021
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19. Kajian Semantik atas Konsep Hablun dalam Al-Quran
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Farhan Ahsan Anshari
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Poetry ,Semantics (computer science) ,Interpretation (philosophy) ,media_common.quotation_subject ,Islam ,Meaning (non-linguistic) ,Semantic data model ,Agreement ,Linguistics ,Rope ,media_common - Abstract
In general, people interpret a rope as a tool for binding. In the extensive Indonesian dictionary, rope means goods with long threads, made of various materials (coconut fiber, palm fiber, plastic, etc.) used for binding, pulling. Whereas in Arabic and the Quran, rope mean hablun, and it does not always mean rope. In the interpretation of hablun it is known as al-jama'ah, agreement, the Quran, Islam, necklaces of commands and prohibitions, neck veins. Whereas the word rope is only a tool for binding, pulling, pulling, tightening, etc. The theory used in this research is semantics. Semantics is a science that is used to explore or study a meaning in words. The semantics used is the encyclopedic model introduced by Dadang Darmawan and Irma Riyani in their journal article, which refutes the Izutsu semantic model with several shortcomings. The word hablun has a primary meaning in the form of a long tool used to bind. In contrast, in relational meaning, it means abstract in jahiliyah poetry and mass media, while in interpretation, it means concrete in Makkiyah letters and abstract meaning in madaniyah surahs. The concept of the word hablun is fundamental to learn because it is a rope and has important lessons for the safety of the world and the hereafter. Among them is that the concept of hablun commands to hold on to the Prophet Muhammad's two inheritances, avoid division, and understand and practice the concept of Islamic sociology.
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- 2021
20. Dedication to a Theory of Modelling : Bernhard Thalheim’s Scientific Journey
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Düsterhöft, Antje, Klettke, Meike, Schewe, Klaus-Dieter, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Düsterhöft, Antje, editor, Klettke, Meike, editor, and Schewe, Klaus-Dieter, editor
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- 2012
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21. Linked Data Processing for Human-in-the-Loop in Cyber–Physical Systems
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Zhigao Zheng, Shahid Mumtaz, Varun G. Menon, and Mohammad Reza Khosravi
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Computer science ,Distributed computing ,Smart device ,Graph partition ,Cyber-physical system ,Linked data ,Semantic data model ,law.invention ,Data modeling ,Human-Computer Interaction ,law ,Modeling and Simulation ,Programming paradigm ,Human-in-the-loop ,Social Sciences (miscellaneous) - Abstract
There are several kinds of smart devices, such as smartphones, sensors, and smart wearable devices, included in the Human-in-the-Loop (HITL) system, but different devices have their own data processing and programming paradigm. Programmers usually need to design the same data processing logic for different devices by using a different programming model. How to mapping the same code to different devices without any change is an emerging topic in the HITL system. Furthermore, the intelligent data processing for the smart CPS sector is experiencing significant growth in data volume, driven by a large number of smart devices that are anticipated in the near further. All these smart devices are expected to improve the overall HITL system performance marvelously. A large number of devices can also outstandingly increase the data volume, which needs to be processed in real time. How to process large-scale data on a smart device in real time is another challenge. Focused on these challenges, this article proposed a computing device-aware HITL CPS data processing framework, named Barge, aiming to map the regular code to the different hardware without any change. In Barge, a semantic model, an architecture-driven programming model, and a graph partition scheme are included. The semantic model is used to express the user-defined graph algorithms by using the domain-specific language. The architecture-driven programming model will execute the graph algorithms on a different device in parallel. Furthermore, the graph partition scheme will partition the large-scale graphs into suitable partitions by aware of the topology to make the partitioned data suitable for kinds of smart devices. We believe that our work would open a wide range of opportunities to improve the performance of large-scale graph processing for HITL systems.
- Published
- 2021
22. Improving Ocean Data Services with Semantics and Quick Index
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Kaijun Ren, Zichen Xu, Kefeng Deng, Aolong Zhou, Xiaoyong Li, Junqiang Song, and Ren Xiaoli
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Information retrieval ,Computer science ,business.industry ,Ontology-based data integration ,Data management ,Data discovery ,Ontology (information science) ,Semantic data model ,Data structure ,Computer Science Applications ,Theoretical Computer Science ,Metadata ,Data access ,Computational Theory and Mathematics ,Hardware and Architecture ,business ,Software - Abstract
Massive ocean data acquired by various observing platforms and sensors poses new challenges to data management and utilization. Typically, it is difficult to find the desired data from the large amount of datasets efficiently and effectively. Most of existing methods for data discovery are based on the keyword retrieval or direct semantic reasoning, and they are either limited in data access rate or do not take the time cost into account. In this paper, we creatively design and implement a novel system to alleviate the problem by introducing semantics with ontologies, which is referred to as Data Ontology and List-Based Publishing (DOLP). Specifically, we mainly improve the ocean data services in the following three aspects. First, we propose a unified semantic model called OEDO (Ocean Environmental Data Ontology) to represent heterogeneous ocean data by metadata and to be published as data services. Second, we propose an optimized quick service query list (QSQL) data structure for storing the pre-inferred semantically related services, and reducing the service querying time. Third, we propose two algorithms for optimizing QSQL hierarchically and horizontally, respectively, which aim to extend the semantics relationships of the data service and improve the data access rate. Experimental results prove that DOLP outperforms the benchmark methods. First, our QSQL-based data discovery methods obtain a higher recall rate than the keyword-based method, and are faster than the traditional semantic method based on direct reasoning. Second, DOLP can handle more complex semantic relationships than the existing methods.
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- 2021
23. Modular, compositional, and executable formal semantics for LLVM IR
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Vadim Zaliva, Calvin Beck, Irene Yoon, Yannick Zakowski, Steve Zdancewic, Ilia Zaichuk, CASH - Compilation and Analysis, Software and Hardware (CASH), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania, and Department of Computer and Information Science [Pennsylvania] (CIS)
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Monads ,Verified Compilation ,Correctness ,Semantics (computer science) ,Computer science ,Formal semantics (linguistics) ,0102 computer and information sciences ,02 engineering and technology ,computer.software_genre ,Semantic data model ,01 natural sciences ,Operational semantics ,Software and its engineering ,0202 electrical engineering, electronic engineering, information engineering ,Coq ,Safety, Risk, Reliability and Quality ,Theory of computation ,Denotational semantics ,Bisimulation ,[INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL] ,Interpretation (logic) ,Programming language ,020207 software engineering ,computer.file_format ,Semantics ,Compilers ,010201 computation theory & mathematics ,Program verification ,LLVM ,TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS ,Executable ,computer ,Software - Abstract
International audience; This paper presents a novel formal semantics, mechanized in Coq, for a large, sequential subset of the LLVM IR. In contrast to previous approaches, which use relationally-specified operational semantics, this new semantics is based on monadic interpretation of interaction trees, a structure that provides a more compositional approach to defining language semantics while retaining the ability to extract an executable interpreter. Our semantics handles many of the LLVM IR's non-trivial language features and is constructed modularly in terms of event handlers, including those that deal with nondeterminism in the specification. We show how this semantics admits compositional reasoning principles derived from the interaction trees equational theory of weak bisimulation, which we extend here to better deal with nondeterminism, and we use them to prove that the extracted reference interpreter faithfully refines the semantic model. We validate the correctness of the semantics by evaluating it on unit tests and LLVM IR programs generated by HELIX.
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- 2021
24. An Ontological and Terminological Resource for n-ary Relation Annotation in Web Data Tables
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Touhami, Rim, Buche, Patrice, Dibie-Barthélemy, Juliette, Ibănescu, Liliana, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Meersman, Robert, editor, Dillon, Tharam, editor, Herrero, Pilar, editor, Kumar, Akhil, editor, Reichert, Manfred, editor, Qing, Li, editor, Ooi, Beng-Chin, editor, Damiani, Ernesto, editor, Schmidt, Douglas C., editor, White, Jules, editor, Hauswirth, Manfred, editor, Hitzler, Pascal, editor, and Mohania, Mukesh, editor
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- 2011
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25. Geofit Project Creating the Opportunity of Geographical – BIM (GEOBIM) Platform to Manage Geothermal Systems
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Ramon, Juan and Velásquez, Sergio
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product data models ,GeoSciML ,IfcExplorer ,semantic data model ,OGC ,object type libraries ,geothermal performance modelling ,DTwin ,GEOFIT ,CityGML ,IFC ,interoperability ,web service - Abstract
Within the GEOFIT project (Smart GeothermalSystems1), BIM environment has been defined asGeoBIM platform. This term refers to those specificgeothermal applications which are included in a tailormadeBIM platform to manage the geothermal systems, building, site and assets information from models,sensors installed and simulations. In GEOFIT project,the demo-sites location is enriched up to the holisticview of the retrofitted buildings with all the geothermalfacilities designed, simulated, installed, commissioned,and monitored, from inception onward, during thelifecycle of a facility and includes all stakeholders who need facility information – from the designers to theoccupants with the building in operation. This holisticview includes the execution control and the permanentgeographical reference because the simulation,monitoring and design processes happen in a specificgeographical context. The definition andimplementation of a GEOBIM platform is paramountfor the project and it is one of the main outcomes ofGEOFIT project. While BIM implementation isubiquitous in the architectural issues of the project, relying mostly on CAD designs, geographicalinformation has a limited role particularly inconstruction projects, it is often restricted to somespecific tasks or seen as a potential redundancy to BIM. Considering the geographical dependent tasks inGEOFIT, GIS can bring a valuable complementarycontribution to the BIM process by providing spatialinput and geospatial visualization, adding informationon the retrofitting demo-site’s surroundingenvironment and underground thermal information thatis essential for design decisions and the approvalprocesses regarding building integrity and geothermalenergy availability. In this paper, an interdisciplinarycooperation, data exchange, and data transfer occurs among the different professionals and disciplinesinvolved for the successful retrofitting project planningand energy efficiency demonstration throughout theGEOBIM platform. This is implemented to assemblethis set of powerful assessment, inspection and groundresearch, testing, and real time monitoring tools. Raw datais available for download here.
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- 2022
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26. Ontology Engineering, Universal Algebra, and Category Theory
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Johnson, Michael, Rosebrugh, Robert, Poli, Roberto, editor, Healy, Michael, editor, and Kameas, Achilles, editor
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- 2010
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27. Information Networking Model
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Liu, Mengchi, Hu, Jie, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Laender, Alberto H. F., editor, Castano, Silvana, editor, Dayal, Umeshwar, editor, Casati, Fabio, editor, and de Oliveira, José Palazzo M., editor
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- 2009
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28. Implementing a Categorical Information System
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Johnson, Michael, Rosebrugh, Robert, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meseguer, José, editor, and Roşu, Grigore, editor
- Published
- 2008
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29. Constant Complements, Reversibility and Universal View Updates
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Johnson, Michael, Rosebrugh, Robert, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meseguer, José, editor, and Roşu, Grigore, editor
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- 2008
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30. Ontology-based approach to data exchanges for robot navigation on construction sites
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David St-Onge, Ivanka Iordanova, and Sina Karimi
- Subjects
Knowledge representation and reasoning ,Computer science ,business.industry ,0211 other engineering and technologies ,Navigation system ,Mobile robot ,02 engineering and technology ,Building and Construction ,Ontology (information science) ,Semantic data model ,Computer Science Applications ,Building information modeling ,Human–computer interaction ,Data exchange ,021105 building & construction ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,business ,Semantic Web ,Civil and Structural Engineering - Abstract
As the use of autonomous Unmanned Ground Vehicles (UGV) for automated data collection from construction projects increases, construction stakeholders have become aware of a problem with inter-disciplinary semantic data sharing and exchanges between construction and robotic. Cross-domain data translation requires detailed specifications especially when it comes to semantic data translation. Building Information Modeling (BIM) and Geographic Information System (GIS) are the two digital building technologies used to capture and store semantic information for indoor structures and outdoor environments respectively. In the absence of a standard format for data exchanges between the construction and robotic domains, the tools of both industries have yet to be integrated into a coherent deployment infrastructure. In other words, the semantics of BIM-GIS cannot be automatically integrated by the robotic platforms currently being used. To enable semantic data transfer across domains, semantic web technology has been widely used in multi-disciplinary areas for interoperability. This paves the way to smarter, quicker and more precise robot navigation on construction sites. This paper develops a semantic web ontology integrating robot navigation and data collection to convey the meanings from BIM-GIS to the robot. The proposed Building Information Robotic System (BIRS) provides construction data that are semantically transferred to the robotic platform and can be used by the robot navigation software stack on construction sites. To meet this objective, first, knowledge representation between construction and robotic domains is bridged. Then, a semantic database integrated with the Robot Operating System (ROS) is developed, which can communicate with the robot and the navigation system to provide the robot with semantic building data at each step of data collection. Finally, the BIRS proposed system is validated through four case studies.
- Published
- 2021
31. A semantic common model for product data in the water industry
- Author
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Yasir Alani, Nashwan Dawood, Huda Dawood, Joao Patacas, and Sergio Rodriguez
- Subjects
Knowledge management ,Computer science ,business.industry ,Interoperability ,0211 other engineering and technologies ,02 engineering and technology ,Building and Construction ,Semantic interoperability ,Ontology (information science) ,Semantic data model ,Computer Science Applications ,Data exchange ,021105 building & construction ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Product (category theory) ,business ,Semantic Web ,Information exchange ,Civil and Structural Engineering - Abstract
The issue of interoperability in the Architecture, Engineering, and Construction (AEC) industry represents a challenge on a scale that spans across the project life cycle. This is predominant in the infrastructure sector that usually comprises a more versatile Operations and Maintenance (O&M) phase in comparison with the buildings sector. To this end, an important stage in the information life cycle is the asset information capture and validation during product procurement at the O&M phase. The water industry in the United Kingdom relies on Product Data Templates (PDTs) to fulfil such task, which is usually an error prone manual process. This paper presents an ongoing research, which investigates the application of Semantic Web Technologies (SWT) for improving product data exchange during product procurement at the O&M phase for the water industry in the United Kingdom (UK). Therefore, focus group sessions with industry experts were held to discuss current inefficiencies and solution requirements. Based on these results, a semantic common model named Asset Specification Ontology (ASO) was developed to capture and validate asset information during product procurement at the O&M phase. The common model (ontology) is based on available technologies, namely Web Ontology Language (OWL) and Shapes Constraint Language (SHACL). This gives the advantage of semantically rich data which can be linked and queried in a meaningful way to facilitate the exchange and validation of water industry assets’ data. The uniqueness of this paper is manifested in the issue it tackles, as efficient product procurement, and hence, data exchange in the water industry is an industrial challenge that is seldom researched. Results from the focus group sessions showed that information exchange within the UK water industry is impeded due to the lack of structured and semantic data. However, for a robust semantic interoperability, there needs to be a robust semantic data infrastructure, which would require semantic mappings from standards to product properties, from standards to other standards, and from standards to dictionaries. These conclusions were further supported by the common model, which was created from existing schemas, standards, and dictionaries. Generally, this paper recommends a common model/product library for phase-specific product data exchange in the water industry.
- Published
- 2021
32. Towards a Digital Diatom: Image Processing and Deep Learning Analysis ofBacillaria paradoxaDynamic Morphology
- Author
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Richard Gordon, Bradly Alicea, Thomas Harbich, Vinay Varma, Ujjwal Singh, and Asmit Kumar Singh
- Subjects
biology ,Artificial neural network ,Computer science ,business.industry ,Deep learning ,Image processing ,Morphology (biology) ,Pattern recognition ,biology.organism_classification ,Semantic data model ,Diatom ,Artificial intelligence ,business ,Bacillaria ,Organism - Abstract
Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allow for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.
- Published
- 2021
33. Completeness based classification algorithm: a novel approach for educational semantic data completeness assessment
- Author
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Nabil Hmina, Ouidad Akhrif, Youness El Bouzekri El Idrissi, Chaymae Benfaress, and Mostapha El Jai
- Subjects
Computer science ,business.industry ,05 social sciences ,050301 education ,02 engineering and technology ,Semantic data model ,computer.software_genre ,Education ,Completeness (order theory) ,0202 electrical engineering, electronic engineering, information engineering ,Computer Science (miscellaneous) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,0503 education ,computer ,Natural language processing - Abstract
Purpose The purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment. Design/methodology/approach A random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier. Findings The smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives. Originality/value The present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.
- Published
- 2021
34. Towards evolutionary knowledge representation under the big data circumstance
- Author
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Tieyun Qian, Xuhui Li, Qingfeng Wu, Yiwen Li, Liuyan Liu, and Xiaoguang Wang
- Subjects
Knowledge representation and reasoning ,Computer science ,business.industry ,05 social sciences ,Big data ,02 engineering and technology ,Library and Information Sciences ,Semantic data model ,Semantics ,Data science ,Computer Science Applications ,Knowledge base ,Specialization (logic) ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,0509 other social sciences ,050904 information & library sciences ,Representation (mathematics) ,business - Abstract
Purpose The purpose of this paper is to propose a graph-based representation approach for evolutionary knowledge under the big data circumstance, aiming to gradually build conceptual models from data. Design/methodology/approach A semantic data model named meaning graph (MGraph) is introduced to represent knowledge concepts to organize the knowledge instances in a graph-based knowledge base. MGraph uses directed acyclic graph–like types as concept schemas to specify the structural features of knowledge with intention variety. It also proposes several specialization mechanisms to enable knowledge evolution. Based on MGraph, a paradigm is introduced to model the evolutionary concept schemas, and a scenario on video semantics modeling is introduced in detail. Findings MGraph is fit for the evolution features of representing knowledge from big data and lays the foundation for building a knowledge base under the big data circumstance. Originality/value The representation approach based on MGraph can effectively and coherently address the major issues of evolutionary knowledge from big data. The new approach is promising in building a big knowledge base.
- Published
- 2021
35. Semantic Querying of News Articles With Natural Language Questions
- Author
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Tuan-Dung Cao and Quang-Minh Nguyen
- Subjects
Information retrieval ,General Computer Science ,Computer science ,business.industry ,Semantic search ,computer.file_format ,Ontology (information science) ,computer.software_genre ,Semantic data model ,News aggregator ,Metadata ,SPARQL ,business ,Semantic Web ,computer ,Natural language - Abstract
The heterogeneity and the increasing amount of the news published on the web create challenges in accessing them. In the authors' previous studies, they introduced a semantic web-based sports news aggregation system called BKSport, which manages to generate metadata for every news item. Providing an intuitive and expressive way to retrieve information and exploiting the advantages of semantic search technique is within their consideration. In this paper, they propose a method to transform natural language questions into SPARQL queries, which could be applied to existing semantic data. This method is mainly based on the following tasks: the construction of a semantic model representing a question, detection of ontology vocabularies and knowledge base elements in question, and their mapping to generate a query. Experiments are performed on a set of questions belonging to various categories, and the results show that the proposed method provides high precision.
- Published
- 2021
36. An Intelligent Video Analysis Method for Abnormal Event Detection in Intelligent Transportation Systems
- Author
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Zonghua Gu, Shaohua Wan, Tian Wang, and Xiaolong Xu
- Subjects
Matching (statistics) ,business.industry ,Event (computing) ,Computer science ,Mechanical Engineering ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image segmentation ,Semantic data model ,Semantics ,Computer Science Applications ,Automotive Engineering ,Computer vision ,Segmentation ,Superframe ,Artificial intelligence ,business ,Intelligent transportation system - Abstract
Intelligent transportation systems pervasively deploy thousands of video cameras. Analyzing live video streams from these cameras is of significant importance to public safety. As streaming video is increasing, it becomes infeasible to have human operators sitting in front of hundreds of screens to catch suspicious activities or detect objects of interests in real-time. Actually, with millions of traffic surveillance cameras installed, video retrieval is more vital than ever. To that end, this article proposes a long video event retrieval algorithm based on superframe segmentation. By detecting the motion amplitude of the long video, a large number of redundant frames can be effectively removed from the long video, thereby reducing the number of frames that need to be calculated subsequently. Then, by using a superframe segmentation algorithm based on feature fusion, the remaining long video is divided into several Segments of Interest (SOIs) which include the video events. Finally, the trained semantic model is used to match the answer generated by the text question, and the result with the highest matching value is considered as the video segment corresponding to the question. Experimental results demonstrate that our proposed long video event retrieval and description method which significantly improves the efficiency and accuracy of semantic description, and significantly reduces the retrieval time.
- Published
- 2021
37. DIGITAL TRANSFORMATION AT THE RECRUITMENT AND SELECTION PROCESS: A STUDY OF SEMANTIC ANALYSIS
- Author
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Renata Martins Correa and Flavia Frate
- Subjects
Knowledge management ,business.industry ,Computer science ,Process (engineering) ,Technological change ,Exploratory research ,Selection (linguistics) ,Digital transformation ,Semantic analysis (knowledge representation) ,business ,Semantic data model ,Human resources - Abstract
An era of exponential technological changes marks the current century. The convergence of different processes has changed the way that companies use technology. Thus, the objective of this study is to reveal how companies can achieve digital transformation at the recruitment and selection process through semantic data analysis. The methodological procedures were elaborated in a descriptive exploratory research, through the qualitative method, which consisted of the literature review and documentary analysis. As a result, it was found that technology, by using artificial intelligence, can assist in the analysis of the candidates' responses. Therefore, if a person uses more the pronoun “we”, it may indicate that the person is more sociable, according to the analyzed case. In this way, it is concluded that technology can corroborate for a more assertive hiring in the management of Human Resources, and then increase efficiency in recruitment and selection activities. Therefore, the future of work will be marked by advanced technologies.
- Published
- 2021
38. HBIM MODELLING FOR AN HISTORICAL URBAN CENTRE
- Author
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M. Avena, E. Colucci, G. Sammartano, and A. Spanò
- Subjects
Technology ,Geospatial analysis ,Computer science ,Interoperability ,Semantic data model ,computer.software_genre ,HBIM, Parametric modelling, multi-sensor 3D survey, UAV clouds, clouds segmentation, urban heritage, GIS-BIM visualisation ,GIS-BIM visualisation ,HBIM ,Applied optics. Photonics ,CityGML ,multi-sensor 3D survey ,Spatial database ,clouds segmentation ,Engineering (General). Civil engineering (General) ,Data science ,TA1501-1820 ,Architect's scale ,Workflow ,Photogrammetry ,urban heritage ,TA1-2040 ,Parametric modelling ,UAV clouds ,computer - Abstract
The research in the geospatial data structuring and formats interoperability direction is the crucial task for creating a 3D Geodatabase at the urban scale. Both geometric and semantic data structuring should be considered, mainly regarding the interoperability of objects and formats generated outside the geographical space. Current reflections on 3D database generation, based on geospatial data, are mostly related to visualisation issues and context-related application. The purposes and scale of representation according to LoDs require some reflections, particularly for the transmission of semantic information.This contribution adopts and develops the integration of some tools to derive object-oriented modelling in the HBIM environment, both at the urban and architectural scale, from point clouds obtained by UAV (Unmanned Aerial Vehicle) photogrammetry.One of the paper’s objectives is retracing the analysis phases of the point clouds acquired by UAV photogrammetry technique and their suitability for multiscale modelling. Starting from UAV clouds, through the optimisation and segmentation, the proposed workflow tries to trigger the modelling of the objects according to the LODs, comparing the one coming from CityGML and the one in use in the BIM community. The experimentation proposed is focused on the case study of the city of Norcia, which like many other historic centres spread over the territory of central Italy, was deeply damaged by the 2016-17 earthquake.
- Published
- 2021
39. End-to-end dilated convolution network for document image semantic segmentation
- Author
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Can-hui Xu, Cao Shi, and Yinong Chen
- Subjects
Network architecture ,Pixel ,Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Metals and Alloys ,General Engineering ,Pattern recognition ,Context (language use) ,Semantic data model ,Convolution ,Feature (computer vision) ,Segmentation ,Deconvolution ,Artificial intelligence ,business - Abstract
Semantic segmentation is a crucial step for document understanding. In this paper, an NVIDIA Jetson Nano-based platform is applied for implementing semantic segmentation for teaching artificial intelligence concepts and programming. To extract semantic structures from document images, we present an end-to-end dilated convolution network architecture. Dilated convolutions have well-known advantages for extracting multi-scale context information without losing spatial resolution. Our model utilizes dilated convolutions with residual network to represent the image features and predicting pixel labels. The convolution part works as feature extractor to obtain multidimensional and hierarchical image features. The consecutive deconvolution is used for producing full resolution segmentation prediction. The probability of each pixel decides its predefined semantic class label. To understand segmentation granularity, we compare performances at three different levels. From fine grained class to coarse class levels, the proposed dilated convolution network architecture is evaluated on three document datasets. The experimental results have shown that both semantic data distribution imbalance and network depth are import factors that influence the document’s semantic segmentation performances. The research is aimed at offering an education resource for teaching artificial intelligence concepts and techniques.
- Published
- 2021
40. Social Signal-Driven Knowledge Automation: A Focus on Social Transportation
- Author
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Kaize Shi, Yifan Zhu, Weichao Gong, Hao Lu, Yisheng Lv, Juanjuan Li, Zhendong Niu, Yong Yuan, and Fei-Yue Wang
- Subjects
Decision support system ,Exploit ,business.industry ,Computer science ,Crowdsourcing ,Semantic data model ,Data science ,Automation ,Human-Computer Interaction ,Modeling and Simulation ,Task analysis ,business ,Intelligent transportation system ,Social Sciences (miscellaneous) ,Social behavior - Abstract
Urban transportation systems are shaped by factors that include people, vehicles, roads, and the environment, forming a complex and giant system with dynamics, diversity, and uncertainty. Physical signal-driven intelligent transportation systems (ITSs) typically lack the ability to capture social behaviors or crowd willingness, and they achieve only information automation for transportation decision support. The crowdsourcing social signals consist of timely, extensive, comprehensive, and rich intelligence that concern urban dynamics, social behaviors, and traffic environments. Such social signals provide a new paradigm for operating ITS with unstructured semantic data, making knowledge automation for decision intelligence a possibility. This article reviews the knowledge automation paradigms for cyber–physical–social systems (CPSSs) compared with traditional information automation paradigms for cyber–physical systems (CPSs) in ITS, from the perspective of data-driven, modeling space, analytical methodologies, and decision support services. To investigate the key methodology in social spaces that enhance information automation into knowledge automation, we summarize the current research into a multisource heterogeneous social signal-based traffic decision knowledge automation framework and further exploit the computational paradigm and applications scenarios of this framework. Finally, we discuss future challenges for designing and realizing knowledge automation on CPSS in transportation.
- Published
- 2021
41. Product family lean improvement based on matching deep mining of customer group preference
- Author
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Zhaoxu Yu, Yueming Li, Hanyu Lu, Fang Liu, Yuqi Zhang, and Shugang Li
- Subjects
Matching (statistics) ,Knowledge management ,Product design ,Computer science ,business.industry ,Mechanical Engineering ,media_common.quotation_subject ,Semantic data model ,Industrial and Manufacturing Engineering ,Preference ,Empirical research ,Architecture ,New product development ,Product (category theory) ,business ,Function (engineering) ,Civil and Structural Engineering ,media_common - Abstract
Mining user preferences from online reviews to understand the representative preferences of different customer groups plays a critical role in product development and improvement, especially in personalized product design. Previous research on mining user preferences usually assumes that all consumers' preferences are homogenous and does not take differences in consumers’ personalities into account. Besides, traditional online review deep mining methods are too broad to focus on precise and detailed mining of customer preferences. To fill the gaps in existing research, our study develops a template matching deep mining method to segment customers and narrow the mining scope of customer group preference, and then proposes a product family lean improvement model. Firstly, K-means and structural change model are applied to cluster customers reliably based on the similarity of user preferences. Secondly, in order to decrease down mining scope of customer group preference, the Improved Deep Structured Semantic Model is designed to determine sentimental polarity sentimental polarity of different groups by matching the standard sentimental polarity review templates and online reviews. Finally, a KANO mapping model is developed to decide the user preferences for product attributes in each customer group according to their sentimental polarity and further summarize the common preferences and personalized preferences of various groups according to the Preference Commonality Measurement Function. Accordingly, product family lean improvement strategies are proposed to provide product developers with improvement directions. An empirical study is carried out on laptop data on JD.COM to verify the validity of the proposed model and product family lean improvement suggestions are put forward.
- Published
- 2021
42. Cross-Layer MAC Protocol for Semantic Wireless Sensor Network
- Author
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Sushovan Das, Chandan Giri, and Suman Bhowmik
- Subjects
Computer science ,Wireless network ,business.industry ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,020206 networking & telecommunications ,02 engineering and technology ,Energy consumption ,Semantic data model ,Computer Science Applications ,Transmission (telecommunications) ,Asynchronous communication ,0202 electrical engineering, electronic engineering, information engineering ,Semantic technology ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,business ,Wireless sensor network ,Computer network ,Efficient energy use - Abstract
Wireless sensor networks (WSN) are ad-hoc wireless networks used in many domains involving the analysis of various properties. Along with the growth in the number of deployments of WSNs, there has been an increase in the size of the networks as well. These systems tend to generate a tremendous amount of data that require further processing and analysis. Transmission of data requires a huge amount of energy considering that the components are powered by tiny batteries with a short life cycle. Consequently, energy efficiency is a dominant factor in the performance measure of WSNs. One major contributor to the overall energy consumption of a WSN is overhearing, a condition, in which the same data is picked up and transmitted by multiple nodes. Reducing overhearing results in a marked decrement in energy usage. This paper proposes to use semantic technology for the observation and collection of sensor data. With the use of an ontology, sensor nodes can be remarkably interoperable and configurable in the receipt and transmission of semantic data. Using this interoperability, we introduce asynchronous semantic preamble listening to avoid overhearing in a semantic sensor network. Performance comparisons to LPL by real experiments show stark improvements in energy consumption.
- Published
- 2021
43. Design of a self-learning multi-agent framework for the adaptation of modular production systems
- Author
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Daniele Scrimieri, Svetan Ratchev, and Shukri Afazov
- Subjects
Production line ,0209 industrial biotechnology ,Process (engineering) ,Computer science ,business.industry ,Mechanical Engineering ,Distributed computing ,020208 electrical & electronic engineering ,02 engineering and technology ,Modular design ,Semantic data model ,Industrial and Manufacturing Engineering ,Computer Science Applications ,020901 industrial engineering & automation ,Control and Systems Engineering ,Component-based software engineering ,0202 electrical engineering, electronic engineering, information engineering ,Layer (object-oriented design) ,business ,Adaptation (computer science) ,Implementation ,Software - Abstract
This paper presents the design of a multi-agent framework that aids engineers in the adaptation of modular production systems. The framework includes general implementations of agents and other software components for self-learning and adaptation, sensor data analysis, system modelling and simulation, as well as human-computer interaction. During an adaptation process, operators make changes to the production system, in order to increase capacity or manufacture a product variant. These changes are automatically captured and evaluated by the framework, building an experience base of adjustments that is then used to infer adaptation knowledge. The architecture of the framework consists of agents divided in two layers: the agents in the lower layer are associated with individual production modules, whereas the agents in the higher layer are associated with the entire production line. Modelling, learning, and adaptations can be performed at both levels, using a semantic model to specify the structure and capabilities of the production system. An evaluation of a prototype implementation has been conducted on an industrial assembly system. The results indicate that the use of the framework in a typical adaptation process provides a significant reduction in time and resources required.
- Published
- 2021
44. BIM and Semantic Enrichment Methods and Applications: A Review of Recent Developments
- Author
-
Ana Sofia Guimarães, João Poças Martins, Fábio Matoseiro Dinis, and Bárbara Rangel
- Subjects
Computer science ,business.industry ,Applied Mathematics ,02 engineering and technology ,Linked data ,computer.file_format ,Semantic data model ,01 natural sciences ,Computer Science Applications ,010101 applied mathematics ,Semantic similarity ,Building information modeling ,Industry Foundation Classes ,0202 electrical engineering, electronic engineering, information engineering ,SPARQL ,020201 artificial intelligence & image processing ,Semantic integration ,0101 mathematics ,Software engineering ,business ,computer ,Semantic Web - Abstract
While considered a relatively emergent area of research (Bloch and Sacks in Autom Constr 91:256–272, 2018), Semantic Enrichment (SE) and Semantic Web services are among the prominent topics and trends in BIM research (BIM handbook a guide to building information modeling for owners, designers, engineers, contractors, and facility managers, John Wiley & Sons Inc, New Jersey). SE computational approaches provide valuable means to overcome current BIM limitations such as interoperability, topology relationships, extensions to standard schemas, among many others. Therefore, the study herein consists of a semi-systematic literature review on BIM-based SE systems and applications developed during the last decade. The article describes the computational methods and approaches identified, a classification of the screened papers according to their primary BIM Use ( https://bim.psu.edu/uses/ ), as well as reported limitations and recommendations for future developments. From the selected articles, main developments in SE techniques encompass multidisciplinary approaches comprising the use of Semantic Web technologies; inference rules, and rule processing engines; artificial intelligence methods; ontology mapping and semantic similarity; application of Industry Foundation Classes (IFC) libraries; and custom plugins. Considering BIM Uses, research was mostly focused on "Capture Existing Conditions" and "Validate Code Compliance". Other identified BIM Uses " verified three or fewer occurrences. Reported limitations state that more user-friendly interfaces are required to handle SPARQL queries (Lee et al. in Autom Constr 68:102–113, 2016). Moreover, the ontologies development process is deemed as time-consuming (Zhong et al. in Build Environ 141:127–142, 2018), and constraints were identified when trying to share semantic data between BIM and Geographic Information System (GIS) platforms (Zhong et al. in Build Environ 141:127–142, 2018). Future research may be expected in transitions to a more extended BIM paradigm, such as the formalization of Digital Twinning processes; discussions concerning a standard format for SPARQL query results (Karan and Irizarry in Autom Constr 53:1–12, 2015); as well as enhanced support through the transition to Linked Data and ontology-based systems.
- Published
- 2021
45. A Segmentation Algorithm of Image Semantic Sequence Data Based on Graph Convolution Network
- Author
-
Zheshu Jia and Deyun Chen
- Subjects
Science (General) ,Article Subject ,Computer Networks and Communications ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,Semantic data model ,Data segment ,Field (computer science) ,030218 nuclear medicine & medical imaging ,Convolution ,Q1-390 ,03 medical and health sciences ,0302 clinical medicine ,Data point ,Feature (computer vision) ,Computer Science::Computer Vision and Pattern Recognition ,0202 electrical engineering, electronic engineering, information engineering ,T1-995 ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,Segmentation ,Algorithm ,Technology (General) ,Information Systems - Abstract
Image semantic data have multilevel feature information. In the actual segmentation, the existing segmentation algorithms have some limitations, resulting in the fact that the final segmentation accuracy is too small. To solve this problem, a segmentation algorithm of image semantic sequence data based on graph convolution network is constructed. The graph convolution network is used to construct the image search process. The semantic sequence data are extracted. After the qualified data points are accumulated, the gradient amplitude forms complete rotation field and no scatter field in the diffusion process, which enhances the application scope of the algorithm, controls the accuracy of the segmentation algorithm, and completes the construction of the data segmentation algorithm. After the experimental dataset is prepared and the semantic segmentation direction is defined, we compare our method with four methods. The results show that the segmentation algorithm designed in this paper has the highest accuracy.
- Published
- 2021
46. OBJECT RE-IDENTIFICATION USING MULTIMODAL AERIAL IMAGERY AND CONDITIONAL ADVERSARIAL NETWORKS
- Author
-
V. V. Kniaz and P. Moshkantseva
- Subjects
lcsh:Applied optics. Photonics ,Matching (graph theory) ,lcsh:T ,business.industry ,Computer science ,Deep learning ,lcsh:TA1501-1820 ,Semantic data model ,Object (computer science) ,lcsh:Technology ,Object detection ,Synthetic data ,Image (mathematics) ,Set (abstract data type) ,lcsh:TA1-2040 ,Computer vision ,Artificial intelligence ,lcsh:Engineering (General). Civil engineering (General) ,business - Abstract
Object Re-Identification (ReID) is the task of matching a given object in the new environment with its image captured in a different environment. The input for a ReID method includes two sets of images. The probe set includes one or more images of the object that must be identified in the new environment. The gallery set includes images that may contain the object from the probe image. The ReID task’s complexity arises from the differences in the object appearance in the probe and gallery sets. Such difference may originate from changes in illumination or viewpoint locations for multiple cameras that capture images in the probe and gallery sets. This paper focuses on developing a deep learning ThermalReID framework for cross-modality object ReID in thermal images. Our framework aims to provide continuous object detection and re-identification while monitoring a region from a UAV. Given an input probe image captured in the visible range, our ThermalReID framework detects objects in a thermal image and performs the ReID. We evaluate our ThermalReID framework and modern baselines using various metrics. We use the IoU and mAP metrics for the object detection task. We use the cumulative matching characteristic (CMC) curves and normalized area-under-curve (nAUC) for the ReID task. The evaluation demonstrated encouraging results and proved that our ThermalReID framework outperforms existing baselines in the ReID accuracy. Furthermore, we demonstrated that the fusion of the semantic data with the input thermal gallery image increases the object detection and localization scores. We developed the ThermalReID framework for cross-modality object re-identification. We evaluated our framework and two modern baselines on the task of object ReID for four object classes. Our framework successfully performs object ReID in the thermal gallery image from the color probe image. The evaluation using real and synthetic data demonstrated that our ThermalReID framework increases the ReID accuracy compared to modern ReID baselines.
- Published
- 2021
47. Webpage Recommendation System Based on the Social Media Semantic Details of the Website
- Author
-
R.Rooba et.al
- Subjects
Web server ,Information retrieval ,business.industry ,Computer science ,General Mathematics ,Semantic search ,Recommender system ,Pointwise mutual information ,Markov model ,computer.software_genre ,Semantic data model ,Education ,Computational Mathematics ,Computational Theory and Mathematics ,Web page ,business ,tf–idf ,computer - Abstract
The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection using Kullback-Leibler Divergence method. In this paper, socio semantic information is included with the similarity score which improves the recommendation accuracy. The social information from the social websites such as twitter is considered for web page recommendation. Initially number of web pages is collected and the similari-ty between web pages is computed by comparing their semantic information. The term frequency and inverse document frequency (tf-idf) is used to produce a composite weight, the most important terms in the web pages are extracted. Then the Pointwise Mutual Information (PMI) between the most important terms and the terms in the twitter dataset are calculated. The PMI metric measures the closeness between the twitter terms and the most important terms in the web pages. Then this measure is added with the similarity score matrix to provide the socio semantic search information for recommendation generation. The experimental results show that the pro-posed method has better performance in terms of prediction accuracy, precision, F1 measure, R measure and coverage.
- Published
- 2021
48. Guest Editorial Diversification in Urban Transportation Systems and Beyond: Integrating People and Goods for the Future of Mobility
- Author
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Sandro Battisti, Antonio Bucchiarone, Philip Feldman, and Teresa Galvão Dias
- Subjects
Value (ethics) ,050210 logistics & transportation ,Mechanical Engineering ,media_common.quotation_subject ,05 social sciences ,Diversification (marketing strategy) ,Destinations ,Semantic data model ,Computer Science Applications ,0502 economics and business ,Automotive Engineering ,Key (cryptography) ,Doors ,Quality (business) ,Business ,Industrial organization ,Edge computing ,media_common - Abstract
The increasing growing need for optimization of transportation in a sustainable and green environment is fundamental for the future of mobility. Companies, governments, and non-governmental institutions are trying to find new ways to contribute to this challenge. In particular, the approach is to improve the efficiency and the quality of the movement of diversification of an integrated way of transportation of people and goods. Many people may need to be at some location at some point in time, or many goods may need to be transported to nearby destinations with the same packaging conditions. The main goal of this Special Issue was to integrate the most recent advances in transportation that connect the hybrid point of view. In particular, key contributions integrate several interesting topics related to earning algorithms for the integration of passengers and goods, gamification techniques, semantic data, blockchain, and edge computing. Other topics in urban mobility, railway, and highways were considered, and hot topics on shared autonomous vehicles (SAVs) brought huge value. Moreover, the Internet of Things (IoT) opens the doors for new development in Vehicle to Everything (V2X), which are crucial for the development of new services that generate business and social impact in the future of a diversified way of the integration of passengers and goods mobility.
- Published
- 2021
49. Seamless Authentication: For IoT-Big Data Technologies in Smart Industrial Application Systems
- Author
-
B. D. Deebak and Fadi Al-Turjman
- Subjects
Authentication ,business.product_category ,Multimedia ,Computer science ,business.industry ,020208 electrical & electronic engineering ,Big data ,Cloud computing ,02 engineering and technology ,computer.software_genre ,Semantic data model ,Computer Science Applications ,Control and Systems Engineering ,Authentication protocol ,0202 electrical engineering, electronic engineering, information engineering ,Internet access ,The Internet ,Electrical and Electronic Engineering ,business ,computer ,Information Systems - Abstract
Technological developments in communication technologies in the form of hardware and software have made unilateral sensor connectivity over Internet access that facilitates data observation and measurement of physical entities. A technology known as Internet of Things (IoT) is commonly referred to as the connectivity of Internet devices that provides the communication interactivity between the physical and the cyber objects. One of the key objectives of Internet computing is to simplify human activities and improve the user experience and device access. To explore its basic challenges, big data is somehow diversified into smart-data intelligence that transforms the raw semantic data into smart-data. The transformation approaches realize the significance of productivity and financial gain, which in turn offers a better decision-making process and privacy preservation. Moreover, the intelligent system collects raw data from different devices that analyze the extracted information. Since IoT plays a significant role in the development of a new source dataset, a seamless authentication protocol (SAP) is preferably chosen to coalesce data inference, algorithm development, and technological advancement. The comparative analysis proves that the proposed SAP consumes less computation and communication overhead as compared to other authentication schemes.
- Published
- 2021
50. Need for developing a security robot-based risk management for emerging practices in the workplace using the Advanced Human-Robot Collaboration Model
- Author
-
Hai Tao, Du Pengxuan, Cui Zheyuan, Yao Liu, Zaher Mundher Yaseen, and Arafatur Rahman
- Subjects
Computer science ,business.industry ,Rehabilitation ,Public Health, Environmental and Occupational Health ,Poison control ,Robotics ,Semantic data model ,Risk Assessment ,Human–robot interaction ,law.invention ,Industrial robot ,Risk analysis (engineering) ,Artificial Intelligence ,law ,Humans ,Robot ,Artificial intelligence ,Workplace ,Adaptation (computer science) ,business ,Occupational Health ,Risk management - Abstract
BACKGROUND: The increasing use of robotics in the work of co-workers poses some new problems in terms of occupational safety and health. In the workplace, industrial robots are being used increasingly. During operations such as repairs, unmanageable, adjustment, and set-up, robots can cause serious and fatal injuries to workers. Collaborative robotics recently plays a rising role in the manufacturing filed, warehouses, mining agriculture, and much more in modern industrial environments. This development advances with many benefits, like higher efficiency, increased productivity, and new challenges like new hazards and risks from the elimination of human and robotic barriers. OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace. RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk. CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.
- Published
- 2021
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