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Symmetry
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Symmetry and Asymmetry Phenomena in Incomplete Big Data Analysis
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Symmetry and Asymmetry Phenomena in Incomplete Big Data Analysis

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 12550

Special Issue Editors

Prof. Dr. Xin Luo

grade E-Mail Website
Guest Editor
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
Interests: artificial intelligence; big data; data mining
Prof. Dr. Di Wu

E-Mail Website
Guest Editor
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
Interests: artificial intelligence; big data; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

In this era of information explosion, people are inundated with big data. The global data sum is predicted to grow from 33 ZB in 2018 to 175 ZB by 2025. Meanwhile, data are commonly incomplete in many big-data-related applications such as environmental monitoring systems, e-commerce systems, and wireless sensor networks, as the related information or relationships are unlikely to be fully observed or collected in practice. Although some information is missing from incomplete data, they still contain rich latent knowledge and patterns, e.g., users’ potential preferences on items in e-commerce systems. Hence, identifying how to efficiently and effectively filter valuable knowledge and patterns out of incomplete big data has become a significant challenge.

Generally, data from real applications have two kinds of distributions, i.e., symmetric and asymmetric distributions. For example, social networks and protein networks commonly involve a symmetric interactions relationship. On the other hand, traffic data obviously have asymmetric probability distributions between accidents and normal situations. Therefore, it is extremely crucial to consider symmetry and asymmetry phenomena in incomplete big data analysis.  

This Special Issue aims at exploring the latest up-to-date theory, methods, and applications regarding incomplete big data analysis with symmetry and asymmetry phenomena. In particular, new interdisciplinary approaches, open-source tools, and open-source datasets are especially welcome.

Prof. Dr. Xin Luo
Prof. Dr. Di Wu
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. Symmetry 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 2400 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

  • big data analysis
  • incomplete data
  • data mining
  • deep learning
  • representation learning
  • symmetric and asymmetric distribution

Published Papers (4 papers)

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Research

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15 pages, 8100 KiB  
Article
A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification
by Wanghui Xiao and Yuting Ding
Symmetry 2022, 14(6), 1216; https://doi.org/10.3390/sym14061216 - 12 Jun 2022
Cited by 10 | Viewed by 2936
Abstract
Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese [...] Read more.
Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously, and (b) utilizing the Focal Loss to deal with the extreme imbalance between positive and negative offline signatures. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that compared with state-of-the-art models, our proposed model achieves better performance. Furthermore, this study tries to extend the proposed model to the Chinese signature dataset in the real environment, which is a significant attempt in the field of Chinese signature identification. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Phenomena in Incomplete Big Data Analysis)
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18 pages, 6346 KiB  
Article
Laser Based Navigation in Asymmetry and Complex Environment
by Yuchen Zhao, Keying Xie, Qingfei Liu, Yawen Li and Tian Wu
Symmetry 2022, 14(2), 253; https://doi.org/10.3390/sym14020253 - 27 Jan 2022
Cited by 1 | Viewed by 1834
Abstract
For collision-free navigation in unstructured and cluttered environments, deep reinforcement learning (DRL) has gained extensive successes for being capable of adapting to new environments without much human effort. However, due to its asymmetry, the problems related to its lack of data efficiency and [...] Read more.
For collision-free navigation in unstructured and cluttered environments, deep reinforcement learning (DRL) has gained extensive successes for being capable of adapting to new environments without much human effort. However, due to its asymmetry, the problems related to its lack of data efficiency and robustness remain as challenges. In this paper, we present a new laser-based navigation system for mobile robots, which combines a global planner with reinforcement learning-based local trajectory re-planning. The proposed method uses Proximal Policy Optimization to learn an efficient and robust local planning policy with asynchronous data generation and training. Extensive experiments have been presented, showing that the proposed system achieves better performance than previous methods including end-to-end DRL, and it can improve the asymmetrical performance. Our analysis show that the proposed method can efficiently avoid deadlock points and achieves a higher success rate. Moreover, we show that our system can generalize to unseen environments and obstacles with only a few shots. The model enables the warehouse to realize automatic management through intelligent sorting and handling, and it is suitable for various customized application scenarios. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Phenomena in Incomplete Big Data Analysis)
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18 pages, 14775 KiB  
Article
A Multi-Source Big Data Security System of Power Monitoring Network Based on Adaptive Combined Public Key Algorithm
by Chengzhi Jiang, Chuanfeng Huang, Qiwei Huang and Jian Shi
Symmetry 2021, 13(9), 1718; https://doi.org/10.3390/sym13091718 - 16 Sep 2021
Cited by 3 | Viewed by 1930
Abstract
The multi-source data collected by the power Internet of Things (IoT) provide the data foundation for the power big data analysis. Due to the limited computational capability and large amount of data collection terminals in power IoT, the traditional security mechanism has to [...] Read more.
The multi-source data collected by the power Internet of Things (IoT) provide the data foundation for the power big data analysis. Due to the limited computational capability and large amount of data collection terminals in power IoT, the traditional security mechanism has to be adapted to such an environment. In order to ensure the security of multi-source data in the power monitoring networks, a security system for multi-source big data in power monitoring networks based on the adaptive combined public key algorithm and an identity-based public key authentication protocol is proposed. Based on elliptic curve cryptography and combined public key authentication, the mapping value of user identification information is used to combine the information in a public and private key factor matrix to obtain the corresponding user key pair. The adaptive key fragment and combination method are designed so that the keys are generated while the status of terminals and key generation service is sensed. An identification-based public key authentication protocol is proposed for the power monitoring system where the authentication process is described step by step. Experiments are established to validate the efficiency and effectiveness of the proposed system. The results show that the proposed model demonstrates satisfying performance in key update rate, key generation quantity, data authentication time, and data security. Finally, the proposed model is experimentally implemented in a substation power IoT environment where the application architecture and security mechanism are described. The security evaluation of the experimental implementation shows that the proposed model can resist a series of attacks such as counterfeiting terminal, data eavesdropping, and tampering. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Phenomena in Incomplete Big Data Analysis)
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Review

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23 pages, 1550 KiB  
Review
PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review
by Dynhora-Danheyda Ramírez-Ochoa, Luis Asunción Pérez-Domínguez, Erwin-Adán Martínez-Gómez and David Luviano-Cruz
Symmetry 2022, 14(3), 455; https://doi.org/10.3390/sym14030455 - 24 Feb 2022
Cited by 25 | Viewed by 4060
Abstract
Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods [...] Read more.
Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; route planning for autonomous vehicles, the optimal application of insulin for a type 1 diabetic patient, robotic harvesting of agricultural products, hybridization with multi-criteria methods, among others. Finally, the contribution of this article is to propose that the PSO method involves the following steps: (a) initialization, (b) update of the local optimal position, and (c) obtaining the best global optimal position. Therefore, this work contributes to researchers not only becoming familiar with the steps, but also being able to implement it quickly. These improvements open new horizons for future lines of research. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Phenomena in Incomplete Big Data Analysis)
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