Special Issue "Selected Papers of the Third Workshop on Intelligent Cross-Data Analytics and Retrieval"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 2736

Special Issue Editors

National Institute of Information and Communications Technology, Koganei, Tokyo 184-8795, Japan
Interests: big data; multimedia modeling; information retrieval; vision-language matching; computer vision; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals
School of Computing, Dublin City University, Dublin 9, Ireland
Interests: multimedia information retrieval; digital memories; mobile device access
Special Issues, Collections and Topics in MDPI journals
Dr. Duc Tien Dang Nguyen
E-Mail Website
Guest Editor
Department of Information Science and Media Studies, University of Bergen, 5020 Bergen, Norway
Interests: multimedia forensics; multimedia retrieval; data science; information and communication technology
Department of Sciences and Informatics, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran, Hokkaido 050-8585, Japan
Interests: wireless networks; cloud computing; cyberphysical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The focus of this Special Issue is cross-data, domain or platform analysis (in cooperation with The Third Workshop on Intelligent Cross-Data Analytics and Retrieval - ICDAR 2022, https://www.xdata.nict.jp/icdar_icmr2022/index.html). More specifically, it will present contributions that discuss how we can use a set of data (i.e., multimodal data) from certain domains with analytic models built on one platform to infer (e.g., prediction, interpolation, query) data from another domain(s) and vice versa. 

The latest research on cross-modal analysis, where, for example text and images are combined, shows great potential. In addition, training and testing of models cross datasets can lead to better and more robust results. Although research on multimodal data analytics is growing fast, little cross-data (e.g., cross-modal data, cross-domain, cross-platform) research has been performed thus far. In order to promote intelligent cross-data analytics and retrieval research, this Special Issue welcomes contributions from diverse research domains and disciplines such as well-being, disaster prevention and mitigation, mobility, climate change, tourism, healthcare, and food computing.

Potential topics of interest include (but are not limited to) the following:

  • Event-based cross-data retrieval data mining and AI technology
  • Complex event processing for dynamically linking sensors data from individual regions to broad areas
  • Transfer learning and transformers
  • Hypotheses development of the associations within the heterogeneous data
  • Realisation of a prosperous and independent region in which people and nature coexist
  • Applications leverage intelligent cross-data analysis for a particular domain
  • Cross-datasets for repeatable experimentation
  • Federated analytics and federated learning for cross-data
  • Private–public data collaboration
  • Integration of diverse multimodal data
  • Explainable and interpretability methods for cross-data
  • Evaluation metrics and methods

Dr. Minh-Son Dao
Dr. Cathal Gurrin
Dr. Duc Tien Dang Nguyen
Dr. Mianxiong Dong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

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Article
Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs
Algorithms 2023, 16(1), 30; https://doi.org/10.3390/a16010030 - 04 Jan 2023
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Abstract
To maintain and improve an amateur athlete’s fitness throughout training and to achieve peak performance in sports events, good nutrition and physical activity (general and training specifically) must be considered as important factors. In our context, the terminology “amateur athletes” represents those who [...] Read more.
To maintain and improve an amateur athlete’s fitness throughout training and to achieve peak performance in sports events, good nutrition and physical activity (general and training specifically) must be considered as important factors. In our context, the terminology “amateur athletes” represents those who want to practice sports to protect their health from sickness and diseases and improve their ability to join amateur athlete events (e.g., marathons). Unlike professional athletes with personal trainer support, amateur athletes mostly rely on their experience and feeling. Hence, amateur athletes need another way to be supported in monitoring and recommending more efficient execution of their activities. One of the solutions to (self-)coaching amateur athletes is collecting lifelog data (i.e., daily data captured from different sources around a person) to understand how daily nutrition and physical activities can impact their exercise outcomes. Unfortunately, not all factors of the lifelog data can contribute to understanding the mutual impact of nutrition, physical activities, and exercise frequency on improving endurance, stamina, and weight loss. Hence, there is no guarantee that analyzing all data collected from people can produce good insights towards having a good model to predict what the outcome will be. Besides, analyzing a rich and complicated dataset can consume vast resources (e.g., computational complexity, hardware, bandwidth), and this therefore does not suit deployment on IoT or personal devices. To meet this challenge, we propose a new method to (i) discover the optimal lifelog data that significantly reflect the relation between nutrition and physical activities and training performance and (ii) construct an adaptive model that can predict the performance for both large-scale and individual groups. Our suggested method produces positive results with low MAE and MSE metrics when tested on large-scale and individual datasets and also discovers exciting patterns and correlations among data factors. Full article
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Review

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Review
A Survey of Intellectual Property Rights Protection in Big Data Applications
Algorithms 2022, 15(11), 418; https://doi.org/10.3390/a15110418 - 08 Nov 2022
Viewed by 1553
Abstract
Big Data applications have the potential to transform any digital business platform by enabling the analysis of vast amounts of data. However, the biggest problem with Big Data is breaking down the intellectual property barriers to using that data, especially for cross-database applications. [...] Read more.
Big Data applications have the potential to transform any digital business platform by enabling the analysis of vast amounts of data. However, the biggest problem with Big Data is breaking down the intellectual property barriers to using that data, especially for cross-database applications. It is a challenge to achieve this trade-off and overcome the difficulties of Big Data, even though intellectual property restrictions have been developed to limit misuse and regulate access to Big Data. This study examines the scope of intellectual property rights in Big Data applications with a security framework for protecting intellectual property rights, watermarking and fingerprinting algorithms. The emergence of Big Data necessitates the development of new conceptual frameworks, security standards, and laws. This study addresses the significant copyright difficulties on cross-database platforms and the paradigm shift from ownership to control of access to and use of Big Data, especially on such platforms. We provide a comprehensive overview of copyright applications for multimedia data and a summary of the main trends in the discussion of intellectual property protection, highlighting crucial issues and existing obstacles and identifying the three major findings for investigating the relationship between them. Full article
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