Advanced Methods for Multi-Source Information Management, Modeling, and Analysis

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Theory and Methodology".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 4070

Special Issue Editors

1. School of International Education, Nanjing Institute of Technology, Nanjing 211167, China
2. Waterford Institute, Nanjing University of Information Science and Technology, Nanjing 211800, China
Interests: cyber security; applied cryptography; multimedia security; privacy protection; biometrics; security management; location based service; cloud computing security
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Guest Editor
Blockchain R&D Laboratory, Curtin University, Bentley 6102, Australia
Interests: blockchain; smart grids; cyber security; wireless sensor networks; IoT
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Software, Nanjing University of Information Science and Technology, Nanjing 210000, China
Interests: information security; artificial intelligence; information protection

Special Issue Information

Dear Colleagues,

It is no exaggeration to say that our lives are shaped by multi-source information. In every aspect of our work, study, communication and consumption, we constantly create, access and utilize various kinds of data. Data can not only help us understand ourselves, others and the environment, but can also enable us to enhance efficiency, generate value, solve problems and achieve goals. Exploring advanced methods and technologies to guarantee availability and security of multi-source information is directly linked to our life and social progress.

The application of artificial intelligence and computer technology has brought people closer to data exploration. These emerging technologies can leverage data more profoundly in different scenarios, which help to deliver personalized content and services, enhance user satisfaction, improve urban management and service quality, and lower cost and risk. A common challenge that has arisen is that mining and optimizing multi-source heterogeneous data requires reliable and accurate data models as well as detailed and suitable system frameworks so as to monitor, analyze and make decisions in real time. Furthermore, it is worthwhile to actively promote the research of advanced technologies and methods to support the management of data from different sources, levels or dimensions. Additionally, for the acquisition and mining of privacy information such as user behavior and emotions, protecting the value of data information itself and the privacy and security of users is crucial.

The aim of this Special Issue is to explore various methods for multi-source information management, modeling and analysis, as well as foster a wider application of data technology in industrial engineering and various disciplines. Topics of interest include but are not limited to:

  • Architecture, tools and systems for multi-source data analysis;
  • Artificial intelligence in multi-source data mining and analysis;
  • Bio-inspired optimization in engineering and sciences;
  • Computer vision and natural language processing;
  • Information security, reliability, trust and privacy;
  • Multi-source information fusion;
  • Multidimensional data structures and indexing strategies;
  • Smart environments.

Dr. Yuan Tian
Dr. Le Sun
Dr. Vidyasagar Potdar
Dr. Biao Song
Guest Editors

Manuscript Submission Information

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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. Information 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.

Keywords

  • data mining and analysis
  • engineering optimization
  • computer science
  • smart environment
  • information security and privacy
  • information fusion
  • artificial intelligence

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Published Papers (4 papers)

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Research

17 pages, 669 KiB  
Article
Unsupervised Decision Trees for Axis Unimodal Clustering
by Paraskevi Chasani and Aristidis Likas
Information 2024, 15(11), 704; https://doi.org/10.3390/info15110704 - 5 Nov 2024
Viewed by 412
Abstract
The use of decision trees for obtaining and representing clustering solutions is advantageous, due to their interpretability property. We propose a method called Decision Trees for Axis Unimodal Clustering (DTAUC), which constructs unsupervised binary decision trees for clustering by exploiting the concept of [...] Read more.
The use of decision trees for obtaining and representing clustering solutions is advantageous, due to their interpretability property. We propose a method called Decision Trees for Axis Unimodal Clustering (DTAUC), which constructs unsupervised binary decision trees for clustering by exploiting the concept of unimodality. Unimodality is a key property indicating the grouping behavior of data around a single density mode. Our approach is based on the notion of an axis unimodal cluster: a cluster where all features are unimodal, i.e., the set of values of each feature is unimodal as decided by a unimodality test. The proposed method follows the typical top-down splitting paradigm for building axis-aligned decision trees and aims to partition the initial dataset into axis unimodal clusters by applying thresholding on multimodal features. To determine the decision rule at each node, we propose a criterion that combines unimodality and separation. The method automatically terminates when all clusters are axis unimodal. Unlike typical decision tree methods, DTAUC does not require user-defined hyperparameters, such as maximum tree depth or the minimum number of points per leaf, except for the significance level of the unimodality test. Comparative experimental results on various synthetic and real datasets indicate the effectiveness of our method. Full article
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18 pages, 1657 KiB  
Article
Identification of Emerging Technological Hotspots from a Multi-Source Information Perspective: Case Study on Blockchain Financial Technology
by Ruiyu Hu, Zemenghong Bao, Juncheng Jia and Kun Lv
Information 2024, 15(9), 581; https://doi.org/10.3390/info15090581 - 19 Sep 2024
Viewed by 827
Abstract
In recent years, propelled by societal transformations and technological advancements, emerging technologies founded upon diverse disciplines such as financial and information technology have rapidly evolved. Identifying the trends associated with these emerging technologies and extracting their salient topics is crucial in order to [...] Read more.
In recent years, propelled by societal transformations and technological advancements, emerging technologies founded upon diverse disciplines such as financial and information technology have rapidly evolved. Identifying the trends associated with these emerging technologies and extracting their salient topics is crucial in order to accurately grasp the developmental trajectory of these tools and for their efficient utilization. In this study, we chronologically categorize information derived from five types of multi-source data, including journal articles, patent inventions, and industry reports, into distinct periods. We employ the LDA (Latent Dirichlet Allocation) topic model to identify emerging technological themes within these periods and utilize a dual-index theme lifecycle analysis method to construct a hotspot theme distribution map, thereby facilitating the extraction of significant themes. Through empirical research on blockchain financial technology, we ultimately identify 22 thematic areas of blockchain finance and extracted eight prominent themes, including financial technology, cross-border payments, digital invoices, supply chain finance, and decentralization. By analyzing these themes alongside their respective popularity levels, we validate that the methods above can be used to effectively identify emerging technological hotspots and illuminate their developmental directions. Full article
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15 pages, 3312 KiB  
Article
Robust Mixed-Rate Region-of-Interest-Aware Video Compressive Sensing for Transmission Line Surveillance Video
by Lisha Gao, Zhoujun Ma, Shuo Han, Tiancheng Zhao, Qingcheng Liu and Zhangjie Fu
Information 2024, 15(9), 555; https://doi.org/10.3390/info15090555 - 10 Sep 2024
Viewed by 628
Abstract
Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention. We propose a robust mixed-rate ROI-aware video [...] Read more.
Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention. We propose a robust mixed-rate ROI-aware video compressive sensing algorithm for transmission line surveillance video compression. The proposed method compresses foreground targets and background frames separately and uses reversible neural network to reconstruct original frames. The result on transmission line surveillance video data shows that the proposed compressive sensing method can achieve 26.47, 34.71 PSNR and 0.6839, 0.9320 SSIM higher than existing methods on 1.5% and 15% measurement rates, and the proposed ROI extraction net can precisely retrieve regions under high noise levels. This research not only demonstrates the potential for a more efficient video compression technique in resource-constrained environments, but also lays a foundation for future advancements in video compressive sensing techniques and their applications in various real-time surveillance systems. Full article
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13 pages, 2487 KiB  
Article
SiamSMN: Siamese Cross-Modality Fusion Network for Object Tracking
by Shuo Han, Lisha Gao, Yue Wu, Tian Wei, Manyu Wang and Xu Cheng
Information 2024, 15(7), 418; https://doi.org/10.3390/info15070418 - 19 Jul 2024
Viewed by 1061
Abstract
The existing Siamese trackers have achieved increasingly successful results in visual object tracking. However, the interactive fusion among multi-layer similarity maps after cross-correlation has not been fully studied in previous Siamese network-based methods. To address this issue, we propose a novel Siamese network [...] Read more.
The existing Siamese trackers have achieved increasingly successful results in visual object tracking. However, the interactive fusion among multi-layer similarity maps after cross-correlation has not been fully studied in previous Siamese network-based methods. To address this issue, we propose a novel Siamese network for visual object tracking, named SiamSMN, which consists of a feature extraction network, a multi-scale fusion module, and a prediction head. First, the feature extraction network is used to extract the features of the template image and the search image, which is calculated by a depth-wise cross-correlation operation to produce multiple similarity feature maps. Second, we propose an effective multi-scale fusion module that can extract global context information for object search and learn the interdependencies between multi-level similarity maps. In addition, to further improve tracking accuracy, we design a learnable prediction head module to generate a boundary point for each side based on the coarse bounding box, which can solve the problem of inconsistent classification and regression during the tracking. Extensive experiments on four public benchmarks demonstrate that the proposed tracker has a competitive performance among other state-of-the-art trackers. Full article
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