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Selected Feature Papers from China Information Theory Conference (CIT) & the Annual Conference on Information Theory of the Chinese Institute of Electronics (CIEIT)

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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 (1 May 2023) | Viewed by 6329

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Special Issue Editors

Prof. Dr. Yunjiang Wang

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1. School of Telecommunication Engineering, Xidian University, Xi'an 710071, China
2. The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
Interests: quantum error correction; error-correcting codes; fault-tolerant quantum computing; quantum compiling; quantum communication; quantum artificial Intelligence

Prof. Dr. Junbin Fang

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Faculty of Science and Technology, Jinan University, Guangzhou 510632, China
Interests: information security; AI security; blockchain
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Prof. Dr. Ke Xiong

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1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
2. Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China
3. Collaborative Innovation Center of Railway Traffic Safety, Beijing Jiaotong University, Beijing 100044, China
4. National Engineering Research Center of Advanced Network Technologies, Beijing Jiaotong University, Beijing 100044, China
Interests: IoT; wireless cooperative networks; wireless powered networks; AI-based network optimization and network information theory
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Fan Cheng

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Guest Editor

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: information theory; network coding and information inequality

Special Issue Information

Dear Colleagues,

The series of the China Information Theory Conference (CIT) & the Annual Conference on Information Theory of the Chinese Institute of Electronics (CIEIT) aims to bring together mainly Chinese researchers from China and around the world in the field of Information Theory.

This Special Issue will collect the most relevant papers dealing with information theory and related topics including but not limited to. information theory, coding, telecommunication, network, information security and cryptography, quantum information, artificial intelligence and their applications in information theory.

We encourage all of the scholars who attend CNSIT 2022 & the 29th CIEIT to make a possible contribution.

Prof. Dr. Yunjiang Wang
Prof. Dr. Junbin Fang
Prof. Dr. Ke Xiong
Prof. Dr. Fan Cheng
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. 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.

Published Papers (5 papers)

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Research

12 pages, 2200 KiB

Open AccessArticle

A Preprocessing Manifold Learning Strategy Based on t-Distributed Stochastic Neighbor Embedding

by Sha Shi, Yefei Xu, Xiaoyang Xu, Xiaofan Mo and Jun Ding

Entropy 2023, 25(7), 1065; https://doi.org/10.3390/e25071065 - 14 Jul 2023

Cited by 4 | Viewed by 1107

Abstract

In machine learning and data analysis, dimensionality reduction and high-dimensional data visualization can be accomplished by manifold learning using a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. We significantly improve this manifold learning scheme by introducing a preprocessing strategy for the t-SNE algorithm. In our preprocessing, we exploit Laplacian eigenmaps to reduce the high-dimensional data first, which can aggregate each data cluster and reduce the Kullback–Leibler divergence (KLD) remarkably. Moreover, the k-nearest-neighbor (KNN) algorithm is also involved in our preprocessing to enhance the visualization performance and reduce the computation and space complexity. We compare the performance of our strategy with that of the standard t-SNE on the MNIST dataset. The experiment results show that our strategy exhibits a stronger ability to separate different clusters as well as keep data of the same kind much closer to each other. Moreover, the KLD can be reduced by about

30 %

at the cost of increasing the complexity in terms of runtime by only 1–2%. Full article

(This article belongs to the Special Issue Selected Feature Papers from China Information Theory Conference (CIT) & the Annual Conference on Information Theory of the Chinese Institute of Electronics (CIEIT))

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13 pages, 997 KiB

Open AccessArticle

Finite-Length Analysis for Spatially Coupled LDPC Codes Based on Base Matrix

by Yang Liu, Sha Sun, Yuzhi Zhang and Bin Wang

Entropy 2023, 25(7), 1041; https://doi.org/10.3390/e25071041 - 11 Jul 2023

Viewed by 744

Abstract

Spatially coupled low density parity check (SC-LDPC) are prominent candidates for future communication standards due to their “threshold saturation” properties. To evaluate the finite-length performance of SC-LDPC codes, a general and efficient finite-length analysis from the perspective of the base matrix is proposed. We analyze the evolution of the residual graphs resulting at each iteration during the decoding process based on the base matrix and then derive the expression for the error probability. To verify the effectiveness of the proposed finite-length analysis, we consider the SC-LDPC code ensembles constructed by parallelly connecting multiple chains (PC-MSC-LDPC). The analysis results show that the predicted error probabilities obtained by using the derived expression for the error probability match the simulated error probabilities. The proposed finite-length analysis provides a useful engineering tool for practical SC-LDPC code design and for analyzing the effects of the code parameters on the performances. Full article

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13 pages, 405 KiB

Open AccessArticle

Weighted BATS Codes with LDPC Precoding

by Wenyue Zhang and Min Zhu

Entropy 2023, 25(4), 686; https://doi.org/10.3390/e25040686 - 19 Apr 2023

Cited by 1 | Viewed by 944

Abstract

Batched Sparse (BATS) codes are a type of network coding scheme that use a combination of random linear network coding (RLNC) and fountain coding to enhance the reliability and efficiency of data transmission. In order to achieve unequal error protection for different data, researchers have proposed unequal error protection BATS (UEP-BATS) codes. However, current UEP-BATS codes suffer from high error floors in their decoding performance, which restricts their practical applications. To address this issue, we propose a novel UEP-BATS code scheme that employs a precoding stage prior to the weighted BATS code. The proposed precoding stage utilizes a partially regular low-density parity-check (PR-LDPC) code, which helps to mitigate the high error floors in the weighted BATS code We derive the asymptotic performance of the proposed scheme based on density evolution (DE). Additionally, we propose a searching algorithm to optimize precoding degree distribution within the complexity range of the precoding stage. Simulation results show that compared to the conventional weighted BATS codes, our proposed scheme offers superior UEP performance and lower error floor, which verifies the effectiveness of our scheme. Full article

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17 pages, 6426 KiB

Open AccessArticle

GLH: From Global to Local Gradient Attacks with High-Frequency Momentum Guidance for Object Detection

by Yuling Chen, Hao Yang, Xuewei Wang, Qi Wang and Huiyu Zhou

Entropy 2023, 25(3), 461; https://doi.org/10.3390/e25030461 - 6 Mar 2023

Cited by 5 | Viewed by 1437

Abstract

The adversarial attack is crucial to improving the robustness of deep learning models; they help improve the interpretability of deep learning and also increase the security of the models in real-world applications. However, existing attack algorithms mainly focus on image classification tasks, and they lack research targeting object detection. Adversarial attacks against image classification are global-based with no focus on the intrinsic features of the image. In other words, they generate perturbations that cover the whole image, and each added perturbation is quantitative and undifferentiated. In contrast, we propose a global-to-local adversarial attack based on object detection, which destroys important perceptual features of the object. More specifically, we differentially extract gradient features as a proportion of perturbation additions to generate adversarial samples, as the magnitude of the gradient is highly correlated with the model’s point of interest. In addition, we reduce unnecessary perturbations by dynamically suppressing excessive perturbations to generate high-quality adversarial samples. After that, we improve the effectiveness of the attack using the high-frequency feature gradient as a motivation to guide the next gradient attack. Numerous experiments and evaluations have demonstrated the effectiveness and superior performance of our from global to Local gradient attacks with high-frequency momentum guidance (GLH), which is more effective than previous attacks. Our generated adversarial samples also have excellent black-box attack ability. Full article

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18 pages, 445 KiB

Open AccessArticle

AeRChain: An Anonymous and Efficient Redactable Blockchain Scheme Based on Proof-of-Work

by Bin Luo and Changlin Yang

Entropy 2023, 25(2), 270; https://doi.org/10.3390/e25020270 - 1 Feb 2023

Cited by 1 | Viewed by 1374

Abstract

Redactable Blockchain aims to ensure the immutability of the data of most applications and provide authorized mutability for some specific applications, such as for removing illegal content from blockchains. However, the existing Redactable Blockchains lack redacting efficiency and protection of the identity information of voters participating in the redacting consensus. To fill this gap, this paper presents an anonymous and efficient redactable blockchain scheme based on Proof-of-Work (PoW) in the permissionless setting, called “AeRChain”. Specifically, the paper first presents an improved Back’s Linkable Spontaneous Anonymous Group (bLSAG) signatures scheme and uses the improved scheme to hide the identity of blockchain voters. Then, in order to accelerate the achievement of redacting consensus, it introduces a moderate puzzle with variable target values for selecting voters and a voting weight function for assigning different weights to puzzles with different target values. The experimental results show that the present scheme can achieve efficient anonymous redacting consensus with low overhead and reduce communication traffic. Full article

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