Relevant bibliographies by topics / Metadata Features

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Metadata Features'

Author: Grafiati
Published: 25 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Metadata Features"

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Odier, Jérôme, Fabian Lambert, and Jérôme Fulachier. "The ATLAS Metadata Interface (AMI) 2.0 metadata ecosystem: new design principles and features." EPJ Web of Conferences 214 (2019): 05046. http://dx.doi.org/10.1051/epjconf/201921405046.

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ATLAS Metadata Interface (AMI) is a generic ecosystem for metadata aggregation, transformation and cataloging. Benefiting from 18 years of feedback in the LHC context, the second major version was recently released. This paper describes the design choices and their benefits for providing high-level metadata-dedicated features. In particular, the Metadata Querying Language (MQL) - a domain-specific language allowing to query databases without knowing the relation between entities - and on the AMI Web framework are described.
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Li, Yaping. "Glowworm Swarm Optimization Algorithm- and K-Prototypes Algorithm-Based Metadata Tree Clustering." Mathematical Problems in Engineering 2021 (February 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/8690418.

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The main objective of this paper is to present a new clustering algorithm for metadata trees based on K-prototypes algorithm, GSO (glowworm swarm optimization) algorithm, and maximal frequent path (MFP). Metadata tree clustering includes computing the feature vector of the metadata tree and the feature vector clustering. Therefore, traditional data clustering methods are not suitable directly for metadata trees. As the main method to calculate eigenvectors, the MFP method also faces the difficulties of high computational complexity and loss of key information. Generally, the K-prototypes algor
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SCHERP, ANSGAR, CARSTEN SAATHOFF, and STEFAN SCHEGLMANN. "A PATTERN SYSTEM FOR DESCRIBING THE SEMANTICS OF STRUCTURED MULTIMEDIA DOCUMENTS." International Journal of Semantic Computing 06, no. 03 (2012): 263–88. http://dx.doi.org/10.1142/s1793351x12400089.

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Today's metadata models and metadata standards often focus on a specific media type only, lack combinability with other metadata models, or are limited with respect to the features they support. Thus they are not sufficient to describe the semantics of rich, structured multimedia documents. To overcome these limitations, we have developed a comprehensive model for representing multimedia metadata, the Multimedia Metadata Ontology (M3O). The M3O has been developed by an extensive analysis of related work and abstracts from the features of existing metadata models and metadata standards. It is b
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Rastogi, Ajay, Monica Mehrotra, and Syed Shafat Ali. "Effective Opinion Spam Detection: A Study on Review Metadata Versus Content." Journal of Data and Information Science 5, no. 2 (2020): 76–110. http://dx.doi.org/10.2478/jdis-2020-0013.

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AbstractPurposeThis paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam detection.Design/methodology/approachBased on spam-detection perspectives, our approach works in three settings: review-centric (spam detection), reviewer-centric (spammer detection) and product-centric (spam-targeted product detection). Besides this, to negate any kind of classifier-bias, we employ four classifiers to get a better and unbiased reflection of the obtained results. In addition, we have proposed a new set of features wh
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Li, Chunqiu, and Shigeo Sugimoto. "Provenance description of metadata application profiles for long-term maintenance of metadata schemas." Journal of Documentation 74, no. 1 (2018): 36–61. http://dx.doi.org/10.1108/jd-03-2017-0042.

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Purpose Provenance information is crucial for consistent maintenance of metadata schemas over time. The purpose of this paper is to propose a provenance model named DSP-PROV to keep track of structural changes of metadata schemas. Design/methodology/approach The DSP-PROV model is developed through applying the general provenance description standard PROV of the World Wide Web Consortium to the Dublin Core Application Profile. Metadata Application Profile of Digital Public Library of America is selected as a case study to apply the DSP-PROV model. Finally, this paper evaluates the proposed mode
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Kim, Jihyeok, Reinald Kim Amplayo, Kyungjae Lee, Sua Sung, Minji Seo, and Seung-won Hwang. "Categorical Metadata Representation for Customized Text Classification." Transactions of the Association for Computational Linguistics 7 (November 2019): 201–15. http://dx.doi.org/10.1162/tacl_a_00263.

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The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e.g., using user/product information for sentiment classification. This information has been used to modify parts of the model (e.g., word embeddings, attention mechanisms) such that results can be customized according to the metadata. We observe that current representation methods for categorical metadata, which are devised for human consumption, are not as effective as claimed in popular classification
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Li, Fang, and Jie Zhang. "Case study: a metadata scheme for multi-type manuscripts for the T.D. Lee Archives Online." Library Hi Tech 32, no. 2 (2014): 219–28. http://dx.doi.org/10.1108/lht-11-2013-0149.

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Purpose – The purpose of this paper is to propose a solution for designing a metadata scheme for multi-type manuscripts based on a comparison of various existing metadata schemes. Design/methodology/approach – The diversity of manuscript types is analysed. Descriptive scheme based on machine-readable MARC and metadata specifications-based descriptive scheme are compared. User tasks and resource features are analysed. Several challenges are posed and resolved through the design and establishment of a metadata scheme for the T.D. Lee Archives Online. Findings – Clarify an approach for developing
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Gong, Minseo, Jae-Yoon Cheon, Young-Suk Park, Jeawon Park, and Jaehyun Choi. "User Musical Taste Prediction Technique Using Music Metadata and Features." International Journal of Multimedia and Ubiquitous Engineering 11, no. 8 (2016): 163–70. http://dx.doi.org/10.14257/ijmue.2016.11.8.18.

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Ahmed, Muhammad Waqas, and Muhammad Tanvir Afzal. "FLAG-PDFe: Features Oriented Metadata Extraction Framework for Scientific Publications." IEEE Access 8 (2020): 99458–69. http://dx.doi.org/10.1109/access.2020.2997907.

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Tali, Dmitry, and Oleg Finko. "Cryptographic Recursive Control of Integrity of Metadata Electronic Documents. Part 2. Complex of Algorithms." Voprosy kiberbezopasnosti, no. 6(40) (2020): 32–47. http://dx.doi.org/10.21681/2311-3456-2020-06-32-47.

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The purpose of the study is to develop a set of algorithms to increase the level of security of metadata of electronic documents in conditions of destructive influences from authorized users (insiders). Research methods: the principle of chain data recording technology, methods of the theory of algorithms, theoretical provisions for the construction of automated information systems of legally significant electronic document management. The result of the research: a complex of algorithms for cryptographic recursive 2-D control of the integrity of metadata of electronic documents has been develo
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Dissertations / Theses on the topic "Metadata Features"

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Bogdanov, Dmitry. "From music similarity to music recommendation : computational approaches based on audio features and metadata." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123776.

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Aquest treball es centra en el modelatge d'usuari per la recomanació musical i desenvolupa algoritmes per la comprensió automàtica i visualització de preferències musicals. Primer, es proposa un model d'usuari construït a partir d'un conjunt de peces musicals. En segon lloc, s'estudien mètodes d’estimació de similitud musical, treballant exclusivament en el contingut d'àudio. Es proposen noves mètriques basades en la informació tímbrica, temporal, tonal i semàntica. En tercer lloc, es proposen diversos mètodes de recomanació musical que utilitzen aquestes mètriques i que milloren amb un filtr
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Gängler, Thomas. "Metadaten und Merkmale zur Verwaltung von persönlichen Musiksammlungen." Thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-72442.

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Roxbergh, Linus. "Language Classification of Music Using Metadata." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-379625.

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The purpose of this study was to investigate how metadata from Spotify could be used to identify the language of songs in a dataset containing nine languages. Features based on song name, album name, genre, regional popularity and vectors describing songs, playlists and users were analysed individually and in combination with each other in different classifiers. In addition to this, this report explored how different levels of prediction confidence affects performance and how it compared to a classifier based on audio input. A random forest classifier proved to have the best performance with a
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Gajová, Veronika. "Automatické třídění fotografií podle obsahu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236565.

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Purpose of this thesis is to design and implement a tool for automatic categorization of photos. The proposed tool is based on the Bag of Words classification method and it is realized as a plug-in for the XnView image viewer. The plug-in is able to classify a selected group of photos into predefined image categories. Subsequent notation of image categories is written directly into IPTC metadata of the picture as a keyword.
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Borggren, Lukas. "Automatic Categorization of News Articles With Contextualized Language Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177004.

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This thesis investigates how pre-trained contextualized language models can be adapted for multi-label text classification of Swedish news articles. Various classifiers are built on pre-trained BERT and ELECTRA models, exploring global and local classifier approaches. Furthermore, the effects of domain specialization, using additional metadata features and model compression are investigated. Several hundred thousand news articles are gathered to create unlabeled and labeled datasets for pre-training and fine-tuning, respectively. The findings show that a local classifier approach is superior t
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Kovach, Bob. "Next Generation Feature Roadmap for IP-Based Range Architectures." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596390.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV<br>The initial efforts that resulted in the migration of range application traffic to an IP infrastructure largely focused on the challenge of obtaining reliable transport for range application streams including telemetry and digital video via IP packet-based network technology. With the emergence of architectural elements that support robust Quality of Service, multicast routing, and redundant opera
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"Leveraging Metadata for Extracting Robust Multi-Variate Temporal Features." Master's thesis, 2013. http://hdl.handle.net/2286/R.I.18794.

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abstract: In recent years, there are increasing numbers of applications that use multi-variate time series data where multiple uni-variate time series coexist. However, there is a lack of systematic of multi-variate time series. This thesis focuses on (a) defining a simplified inter-related multi-variate time series (IMTS) model and (b) developing robust multi-variate temporal (RMT) feature extraction algorithm that can be used for locating, filtering, and describing salient features in multi-variate time series data sets. The proposed RMT feature can also be used for supporting multiple analy
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Gängler, Thomas. "Metadaten und Merkmale zur Verwaltung von persönlichen Musiksammlungen." Thesis, 2009. https://tud.qucosa.de/id/qucosa%3A25671.

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Feng, Kuan-Jen, and 馮冠仁. "An Efficient Hierarchical Metadata Classifier based on SVM and Feature Selection Methods." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/71704686561687980607.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>94<br>Constructing a Web portal via integrating different contents from various information systems is crucial for providing public, popular and friendly services. In this thesis, we propose a hierarchical classifier system toward to fusing heterogeneous categories from various information systems. Employing traditional text classification methods that classify documents into predefined categories to deal with the problem is a possible solution. However, traditional methods suffer from drawbacks of huge text features and flat classification without considering hiera
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Mohamed, Ghouse S. M. Z. S. "Modeling spatial variation of data quality in databases." 2008. http://repository.unimelb.edu.au/10187/3544.

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The spatial data community relies on the quality of its data. This research investigates new ways of storing and retrieving spatial data quality information in databases. Given the importance of features and sub-feature variation, three different data quality models of spatial variation in quality have been identified and defined: per-feature, feature-independent and feature-hybrid. Quality information is stored against each feature in the per-feature model. In the feature-independent model, quality information is independent of the feature. The feature-hybrid is derived from a combination of
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Book chapters on the topic "Metadata Features"

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Zinke-Wehlmann, Christian, Amit Kirschenbaum, Raul Palma, et al. "Linked Data and Metadata." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_7.

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AbstractData is the basis for creating information and knowledge. Having data in a structured and machine-readable format facilitates the processing and analysis of the data. Moreover, metadata—data about the data, can help discovering data based on features as, e.g., by whom they were created, when, or for which purpose. These associated features make the data more interpretable and assist in turning it into useful information. This chapter briefly introduces the concepts of metadata and Linked Data—highly structured and interlinked data, their standards and their usages, with some elaboration on the role of Linked Data in bioeconomy.
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Sawadogo, Pegdwendé N., Étienne Scholly, Cécile Favre, Éric Ferey, Sabine Loudcher, and Jérôme Darmont. "Metadata Systems for Data Lakes: Models and Features." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30278-8_43.

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Yadav, Asmita. "Bug Assignment-Utilization of Metadata Features Along with Feature Selection and Classifiers." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3067-5_7.

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Adler, B. Thomas, Luca de Alfaro, Santiago M. Mola-Velasco, Paolo Rosso, and Andrew G. West. "Wikipedia Vandalism Detection: Combining Natural Language, Metadata, and Reputation Features." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19437-5_23.

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Wen, Jingran, Ramkiran Gouripeddi, and Julio C. Facelli. "Metadata Discovery of Heterogeneous Biomedical Datasets Using Token-Based Features." In IT Convergence and Security 2017. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6451-7_8.

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Hirota, Masaharu, Shohei Yokoyama, Naoki Fukuta, and Hiroshi Ishikawa. "Constraint-Based Clustering of Image Search Results Using Photo Metadata and Low-Level Image Features." In Computer and Information Science 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15405-8_14.

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Elhadad, Mohamed K., Kin Fun Li, and Fayez Gebali. "A Novel Approach for Selecting Hybrid Features from Online News Textual Metadata for Fake News Detection." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33509-0_86.

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Consoli, Sergio, Luca Tiozzo Pezzoli, and Elisa Tosetti. "Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_18.

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AbstractThe Global Data on Events, Location, and Tone (GDELT) is a real time large scale database of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. In this work, we first describe a data crawler, which collects metadata of the GDELT database in real-time and stores them in a big data management system based on Elasticsearch, a popular and efficient search engine relying on the Lucene library. Then, by exploiting and engineering the detailed information of each news encoded in GDELT, we build indicators capturing investor’s emotions which are useful to analyse the sovereign bond market in Italy. By using regression analysis and by exploiting the power of Gradient Boosting models from machine learning, we find that the features extracted from GDELT improve the forecast of country government yield spread, relative that of a baseline regression where only conventional regressors are included. The improvement in the fitting is particularly relevant during the period government crisis in May-December 2018.
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Rajaram, Gangothri, and K. R. Manjula. "Multi-standard Schema-Based Classification of Geospatial Metadata in Spatial Data Infrastructures Using Feature Weight Induced Probabilistic Learning Scheme." In Lecture Notes in Networks and Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1941-0_66.

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"Metadata and Features." In Cultural Analytics. The MIT Press, 2020. http://dx.doi.org/10.7551/mitpress/11214.003.0011.

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Conference papers on the topic "Metadata Features"

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Gollapalli, Sujatha Das, Yanjun Qi, Prasenjit Mitra, and C. Lee Giles. "Extracting Researcher Metadata with Labeled Features." In Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.85.

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"Combining Visual and Text Features for Learning in Multimedia Direct Marketing Domain." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002337200340047.

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Smutz, Charles, and Angelos Stavrou. "Malicious PDF detection using metadata and structural features." In the 28th Annual Computer Security Applications Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2420950.2420987.

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Safadi, Bahjat, Philippe Mulhem, Georges Quenot, and Jean-Pierre Chevallet. "Lifelog Semantic Annotation using deep visual features and metadata-derived descriptors." In 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 2016. http://dx.doi.org/10.1109/cbmi.2016.7500247.

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Jony, Rabiul Islam, Alan Woodley, and Dimitri Perrin. "Flood Detection in Social Media Images using Visual Features and Metadata." In 2019 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2019. http://dx.doi.org/10.1109/dicta47822.2019.8946007.

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Wang, Xiaolan, K. Selcuk Candan, and Maria Luisa Sapino. "Leveraging metadata for identifying local, robust multi-variate temporal (RMT) features." In 2014 IEEE 30th International Conference on Data Engineering (ICDE). IEEE, 2014. http://dx.doi.org/10.1109/icde.2014.6816667.

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Leung, John, Igor Griva, and William Kennedy. "Using Affective Features from Media Content Metadata for Better Movie Recommendations." In 12th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010056201610168.

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Korovesis, Konstantinos, Georgios Alexandridis, George Caridakis, Pavlos Polydoras, and Panagiotis Tsantilas. "Leveraging aspect-based sentiment prediction with textual features and document metadata." In SETN 2020: 11th Hellenic Conference on Artificial Intelligence. ACM, 2020. http://dx.doi.org/10.1145/3411408.3411433.

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Jony, Rabiul Islam, Alan Woodley, and Dimitri Perrin. "Fusing Visual Features and Metadata to Detect Flooding in Flickr Images." In 2020 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2020. http://dx.doi.org/10.1109/dicta51227.2020.9363418.

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Leung, John, Igor Griva, and William Kennedy. "Using Affective Features from Media Content Metadata for Better Movie Recommendations." In 12th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010056201550162.

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