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Advances in Information Sciences and Applications II

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 3823

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Guest Editor
Department of Computer Science and Engineering, Chung-Ang University, 84 Heukseok-ro, Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea
Interests: Artificial Intelligence; machine learning; neural architecture design; feature engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid advancement of artificial intelligence, information science has once again proved its importance as a core technique for realizing effective, efficient, and robust intelligent systems. Additionally, the information techniques frequently used for the purposes of analysis purposes, such as optimizing artificial neural networks' generalization performance, has also impacted across the entire processing pipeline for transforming data into information such as data preparation, preprocessing, modeling, analysis, interpretation, and evaluation. As a result, they play essential roles in diverse fields relating information sciences such as intelligent systems, genetic algorithms and modeling, expert and decision support systems, bioinformatics, self-adaptation systems, self-organizational systems, data engineering, data fusion, perceptions and pattern recognition, and text processing. This Special Issue aims to solicit and publish papers that provide a clear view of state-of-the-art research activities in information sciences and diverse backgrounds in engineering, mathematics, statistics, computer science, biology, cognitive science, neurobiology, behavioral sciences, and biochemistry. We encourage submissions in areas including, but not limited to:

  • Data Preparation, Preprocessing, and Transformation: Data cleansing, data normalization, data quantization, data fusion, missing value treatment, feature creation, feature selection, feature extraction, imbalance data treatment;
  • Search, Optimization, and Planning: Metaheuristic algorithms and modeling, hybrid search algorithms, combinatorial optimization, adaptive and supervisory control, self-adaptation and self-organizational systems, mobile robot path planning;
  • Modeling, Learning, and Analysis: Supervised learning, unsupervised learning, reinforcement learning, metric learning, transfer learning, federated learning, neural architecture search and design, network compression and quantization, symbolic and statistical learning, ensemble system, error optimization;
  • The measure of Information, Dependency, and Uncertainty: Kullback-Leibler divergence, Renyi entropy, cross-entropy, Tsallis entropy, differential entropy, mutual information, interaction information, normalized mutual information, symmetric uncertainty.
  • Applications: Manufacturing, automation robots, mobile robots, virtual reality, image processing systems, computer vision systems, genomics and bioinformatics, language engine design, human–computer interface, text abstraction, text summarization, finance and economics modeling.

Prof. Dr. Jaesung Lee
Guest Editor

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. Entropy 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 2600 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

  • information science
  • data preprocessing
  • search and optimization
  • machine learning
  • deep learning
  • reinforcement learning
  • statistical modeling
  • interpretation
  • evaluation
  • information-based application
  • measure of information

Related Special Issue

Published Papers (3 papers)

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Research

26 pages, 1995 KiB  
Article
Identity-Based Matchmaking Encryption with Equality Test
by Zhen Yan, Xijun Lin, Xiaoshuai Zhang, Jianliang Xu and Haipeng Qu
Entropy 2024, 26(1), 74; https://doi.org/10.3390/e26010074 - 15 Jan 2024
Viewed by 774
Abstract
The identity-based encryption with equality test (IBEET) has become a hot research topic in cloud computing as it provides an equality test for ciphertexts generated under different identities while preserving the confidentiality. Subsequently, for the sake of the confidentiality and authenticity of the [...] Read more.
The identity-based encryption with equality test (IBEET) has become a hot research topic in cloud computing as it provides an equality test for ciphertexts generated under different identities while preserving the confidentiality. Subsequently, for the sake of the confidentiality and authenticity of the data, the identity-based signcryption with equality test (IBSC-ET) has been put forward. Nevertheless, the existing schemes do not consider the anonymity of the sender and the receiver, which leads to the potential leakage of sensitive personal information. How to ensure confidentiality, authenticity, and anonymity in the IBEET setting remains a significant challenge. In this paper, we put forward the concept of the identity-based matchmaking encryption with equality test (IBME-ET) to address this issue. We formalized the system model, the definition, and the security models of the IBME-ET and, then, put forward a concrete scheme. Furthermore, our scheme was confirmed to be secure and practical by proving its security and evaluating its performance. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications II)
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26 pages, 2555 KiB  
Article
Fair Max–Min Diversity Maximization in Streaming and Sliding-Window Models
by Yanhao Wang, Francesco Fabbri, Michael Mathioudakis and Jia Li
Entropy 2023, 25(7), 1066; https://doi.org/10.3390/e25071066 - 14 Jul 2023
Viewed by 954
Abstract
Diversity maximization is a fundamental problem with broad applications in data summarization, web search, and recommender systems. Given a set X of n elements, the problem asks for a subset S of kn elements with maximum diversity, as quantified by the [...] Read more.
Diversity maximization is a fundamental problem with broad applications in data summarization, web search, and recommender systems. Given a set X of n elements, the problem asks for a subset S of kn elements with maximum diversity, as quantified by the dissimilarities among the elements in S. In this paper, we study diversity maximization with fairness constraints in streaming and sliding-window models. Specifically, we focus on the max–min diversity maximization problem, which selects a subset S that maximizes the minimum distance (dissimilarity) between any pair of distinct elements within it. Assuming that the set X is partitioned into m disjoint groups by a specific sensitive attribute, e.g., sex or race, ensuring fairness requires that the selected subset S contains ki elements from each group i[m]. Although diversity maximization has been extensively studied, existing algorithms for fair max–min diversity maximization are inefficient for data streams. To address the problem, we first design efficient approximation algorithms for this problem in the (insert-only) streaming model, where data arrive one element at a time, and a solution should be computed based on the elements observed in one pass. Furthermore, we propose approximation algorithms for this problem in the sliding-window model, where only the latest w elements in the stream are considered for computation to capture the recency of the data. Experimental results on real-world and synthetic datasets show that our algorithms provide solutions of comparable quality to the state-of-the-art offline algorithms while running several orders of magnitude faster in the streaming and sliding-window settings. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications II)
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22 pages, 2618 KiB  
Article
User Real-Time Influence Ranking Algorithm of Social Networks Considering Interactivity and Topicality
by Zhaohui Li, Wenjia Piao, Zhengyi Sun, Lin Wang, Xiaoqian Wang and Wenli Li
Entropy 2023, 25(6), 926; https://doi.org/10.3390/e25060926 - 12 Jun 2023
Cited by 1 | Viewed by 1456
Abstract
At present, the existing influence evaluation algorithms often ignore network structure attributes, user interests and the time-varying propagation characteristics of influence. To address these issues, this work comprehensively discusses users’ own influence, weighted indicators, users’ interaction influence and the similarity between user interests [...] Read more.
At present, the existing influence evaluation algorithms often ignore network structure attributes, user interests and the time-varying propagation characteristics of influence. To address these issues, this work comprehensively discusses users’ own influence, weighted indicators, users’ interaction influence and the similarity between user interests and topics, thus proposing a dynamic user influence ranking algorithm called UWUSRank. First, we determine the user’s own basic influence based on their activity, authentication information and blog response. This improves the problem of poor objectivity of initial value on user influence evaluation when using PageRank to calculate user influence. Next, this paper mines users’ interaction influence by introducing the propagation network properties of Weibo (a Twitter-like service in China) information and scientifically quantifies the contribution value of followers’ influence to the users they follow according to different interaction influences, thereby solving the drawback of equal value transfer of followers’ influence. Additionally, we analyze the relevance of users’ personalized interest preferences and topic content and realize real-time monitoring of users’ influence at various time periods during the process of public opinion dissemination. Finally, we conduct experiments by extracting real Weibo topic data to verify the effectiveness of introducing each attribute of users’ own influence, interaction timeliness and interest similarity. Compared to TwitterRank, PageRank and FansRank, the results show that the UWUSRank algorithm improves the rationality of user ranking by 9.3%, 14.2%, and 16.7%, respectively, which proves the practicality of the UWUSRank algorithm. This approach can serve as a guide for research on user mining, information transmission methods, and public opinion tracking in social network-related areas. Full article
(This article belongs to the Special Issue Advances in Information Sciences and Applications II)
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