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Probe Machine Based Computing Model for Maximum Clique Problem

Chinese Journal of Electronics 2022年第2期31卷 304-312页

作者： CUI Jianzhong YIN Zhixiang TANG Zhen YANG Jing Department of Computer Huainan Union University School of Electronic and Information Engineering Anhui University of Science & Technology School of Mathematics Physics and Statistics Shanghai University of Engineering Science School of Mathematics and Big Data Anhui University of Science & Technology

Probe machine(PM) is a recently reported mathematic model with massive parallelism. Herein,we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently,we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution *** the other hand, the proposed model is highly *** work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.

关键词： maximal cliques maximum clique problem searching capacity data library NP-complete problem computational complexity model lie induced subgraph probe operation undirected graph mathematic model graph theory parallel algorithms optimisation parallelism computing model vertex probe library probe machine

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Zero Trust Consumer IoT With Robust Federated Learning Over Main-Side Blockchain

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1180-1189页

作者： Zhu, Hai Xue, Xingsi Xu, Mengmeng Kim, Byung-Gyu Henan University of Engineering School of Computer Zhengzhou451191 China Fujian Uni-versity of Technology School of Computer Science and Mathematics Fuzhou350118 China Sookmyung Women's University Department of AI Engineering Seoul Korea Republic of

The rapid proliferation of smart consumer devices has given rise to the consumer Internet of Things (CIoT), enabling immense data collection and valuable insights for enhancing con-sumer experiences. However, the distributed nature of CIoT sys-tems and the sensitivity of consumer data pose significant chal-lenges in ensuring security, privacy, and zero trust. This paper proposes a novel framework integrating robust federated learning with a main-side blockchain architecture and zero trust principles to enable secure and privacy-preserving data sharing and collabo-rative learning in CIoT environments. The proposed system model consists of CIoT devices as side nodes, edge servers as main nodes, and a cloud server for global aggregation. A lightweight privacy-preserving aggregation protocol is designed based on secret shar-ing to protect raw data during local model updates. To enhance the robustness against Byzantine attacks and data heterogeneity, a resampling-based robust aggregation method is developed, which evaluates the cosine similarity of local updates against a ref-erence gradient securely selected via the main-side blockchain. Ex-periments demonstrate that the proposed framework performs well regarding model accuracy, convergence speed, and resiliency compared with state-of-the-art methods. © 2024 IEEE.

关键词： Federated learning

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A distributed coding-decoding-based Nash equilibrium seeking algorithm over directed communication network

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science China(Technological sciences) 2023年第7期66卷 1975-1986页

作者： RAO XinPei XU WenYing YANG ShaoFu YU WenWu School of Mathematics Southeast UniversityNanjing 211189China School of Computer Science and Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories Nanjing 211111China

This paper is concerned with the distributed Nash equilibrium(NE) computation problem for non-cooperative games subject to partial-decision *** the purpose of congestion mitigation,coding-decoding-based schemes are constructed on the basis of logarithmic and uniform quantizers,*** be specific,the data(decision variable) are first mapped to codewords by an encoder scheme,and then sent to the neighboring agents through a directed communication network(with non-doubly stochastic weighted matrix).By using a decoder scheme,a new distributed algorithm is established for seeking the *** order to eliminate the convergence error caused by quantization,a dynamic variable is introduced and a modified codingdecoding-based algorithm is constructed under the uniform quantization scheme,which ensures the asymptotic convergence to the *** proposed algorithm only requires that the weighted adjacency matrix is row stochastic instead of double ***,one numerical example is provided to validate the effectiveness of our algorithms.

关键词： non-cooperative games quantization coding-decoding projected pseudo-gradient dynamics

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Convolutional neural network-based image watermarking using discrete wavelet transform

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International Journal of Information Technology (Singapore) 2023年第4期15卷 2021-2029页

作者： Tavakoli, Alireza Honjani, Zahra Sajedi, Hedieh Department of Computer Engineering School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Computer Science School of Mathematics Statistics and Computer Science College of Science University of Tehran Tehran Iran

With the growing popularity of the Internet, digital images are used and transferred more frequently. Although this phenomenon facilitates easy access to information, it also creates security concerns and violates intellectual property rights by allowing illegal use, copying, and digital content theft. Using watermarks in digital images is one of the most common ways to maintain security. Watermarking is proving and declaring ownership of an image by adding a digital watermark to the original image. Watermarks can be either text or an image placed overtly or covertly in an image and are expected to be challenging to remove. This paper proposes a combination of convolutional neural networks (CNNs) and wavelet transforms to obtain a watermarking network for embedding and extracting watermarks. The network is independent of the host image resolution, can accept all kinds of watermarks, and has only 11 layers while keeping performance. Performance is measured by two terms;the similarity between the extracted watermark and the original one and the similarity between the host image and the watermarked one. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Convolutional neural networks Neural networks Watermarking Wavelet transform

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Reactive Power Optimization of AC-DC Hybrid System Based on Improved Whale Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第4期9卷 2084-2098页

作者： Huang, Xin-Yu Liang, Quan Chen, Xian-Fu Zhan, Chuang-Tao School of Electronic Electrical Engineering Physics Fujian University of Technology Fuzhou350118 China School of Computer Science Mathematics Fujian University of Technology Fuzhou350118 China

With the continuous construction of HVDC, it has gradually become dis-tances and large capacity transmission, as well as one of the main technologies of regional power grid interconnection. The scale of AC-DC hybrid grid continues to grow, bringing new challenges to reactive power balance and voltage stability of power grid. An improved whale optimization algorithm is proposed to solve the reactive power optimization problem of AC-DC hybrid systems. By introducing local neighborhood search mechanism, adaptive threshold and adaptive inertia weight, the local search accuracy, global search ability and convergence speed of the algorithm are improved. The reactive power optimization model is established with the total network loss and node voltage offset as the objective function, and the solution steps are given. An IEEE 22-node system with DC transmission lines is used as an example to simulate the proposed algorithm, and compared with the traditional whale optimization algorithm and genetic algorithm, the results show that the proposed algorithm has good convergence and stability, and can obtain better reactive power optimization effect. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Optimization algorithms

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Toward quantum unmanned systems

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science China(Technological sciences) 2024年第7期67卷 2277-2280页

作者： CHENG ShuMing QI HongSheng SUN Yue LIU LiJun XIE ShaoRong REN XiaoQiang Department of Control Science and Engineering Tongji UniversityShanghai 200092China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Cipher Cloud Core Ltd. Beijing 100085China College of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041081China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China

1 Quantum information technology Quantum information technology utilizes physical systems at the microscopic level, such as photon, atom, ion, and superconducting, to accomplish information-processing tasks that are impossible for the classical macroscopic world. During the past decade, significant process has been achieved in the pursuit of quantum technology into practical applications,generating great research interest from various domains, with the potential to radically change our information infrastructure [1–3].

关键词： quantum utilize generating

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Foundation models in bioinformatics

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National science Review 2025年第4期12卷 400-418页

作者： Fei Guo Renchu Guan Yaohang Li Qi Liu Xiaowo Wang Can Yang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Xiangjiang Laboratory Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Department of Computer Science Old Dominion University School of Life Sciences and Technology Tongji University Department of Automation Tsinghua University Department of Mathematics State Key Laboratory of Molecular Neuroscience and Big Data Bio-Intelligence Lab The Hong Kong University of Science and Technology

With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,model evaluation and *** demonstrate notable proficiency in managing large-scale,unlabeled datasets,because experimental procedures are costly and labor *** various downstream tasks,FMs have consistently achieved noteworthy results,demonstrating high levels of accuracy in representing biological entities.A new era in computational biology has been ushered in by the application of FMs,focusing on both general and specific biological *** this review,we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks,including genomics,transcriptomics,proteomics,drug discovery and single-cell *** aim is to assist scientists in selecting appropriate FMs in bioinformatics,according to four model types:language FMs,vision FMs,graph FMs and multimodal *** addition to understanding molecular landscapes,AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

关键词： foundation model bioinformatics genomics transcriptomics proteomics drug discovery single-cell analysis

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Comparison of CNN-based deep learning architectures for rice diseases classification

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Artificial Intelligence in Agriculture 2023年第3期9卷 22-35页

作者： Md Taimur Ahad Yan Li Bo Song Touhid Bhuiyan Department of Computer Science and Engineering Daffodil International UniversitySavarBangladesh School of Mathematics Physics and Computing University of Southern QueenslandToowoombaAustralia School of Engineering University of Southern Queensland ToowoombaAustralia Department of Computer Science and Engineering Faculty of Science and Information TechnologyDaffodil International UniversityBangladesh

Although convolutional neural network(CNN)paradigms have expanded to transfer learning and ensemble models from original individual CNN architectures,few studies have focused on the performance comparison of the applicability of these techniques in detecting and localizing rice ***,most CNN-based rice disease detection studies only considered a small number of diseases in their *** these shortcomings were addressed in this *** this study,a rice disease classification comparison of six CNN-based deep-learning architectures(DenseNet121,Inceptionv3,MobileNetV2,resNext101,Resnet152V,and Seresnext101)was conducted using a database of nine of the most epidemic rice diseases in *** addition,we applied a transfer learning approach to DenseNet121,MobileNetV2,Resnet152V,Seresnext101,and an ensemble model called DEX(Densenet121,EfficientNetB7,and Xception)to compare the six individual CNN networks,transfer learning,and ensemble *** results suggest that the ensemble framework provides the best accuracy of 98%,and transfer learning can increase the accuracy by 17%from the results obtained by Seresnext101 in detecting and localizing rice leaf *** high accuracy in detecting and categorisation rice leaf diseases using CNN suggests that the deep CNN model is promising in the plant disease detection domain and can significantly impact the detection of diseases in real-time agricultural *** research is significant for farmers in rice-growing countries,as like many other plant diseases,rice diseases require timely and early identification of infected diseases and this research develops a rice leaf detection system based on CNN that is expected to help farmers to make fast decisions to protect their agricultural yields and quality.

关键词： Deep learning Convolutional neural networks(CNNs) Transfer learning Plant leaf disease detection

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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TranslateKar: An Efficient Transformer Based Konkani Translator 3rd

TranslateKar: An Efficient Transformer Based Konkani Transla...

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3rd International Conference on Emerging Electronics and Automation, E2A 2023

作者： Pai, Shreya Mandrekar, Sajal Phadte, Saylee Naik, Atit Prasad, Shitala Computer Science Goa College of Engineering Goa Ponda India School of Mathematics and Computer Science IIT Goa Goa Ponda India

ISBN: (纸本)9789819730896

In this research, we introduce a bidirectional Neural Machine Translation (NMT) model that employs the Transformer architecture to facilitate translations between Konkani and English languages. Inspired by "Attention is All You Need", we delve into variations in hyperparameters, including dropout rates and vocabulary size, to assess their impact on translation performance. The Transformer model, renowned for its capacity to handle long-range dependencies efficiently and in parallel, proves highly suitable for sequence-to-sequence tasks such as machine translation. We meticulously conduct a series of experiments, training the model with diverse hyperparameters configuration, and subsequently compare the outcomes to discern the optimal setup. Our findings underscore the superiority of the dash model, as it attains the highest BLEU score on the 3k test dataset, demonstrating its enhanced translation accuracy for both Konkani to English and English to Konkani translations. These results shed light on the paramount importance of hyperparameters selection in bidirectional NMT systems, underscoring the Transformer architecture’s potential in multilingual translation tasks. The study’s outcomes represent a noteworthy contribution to the advancement of machine translation techniques and set the stage for more effective communication tools in multilingual contexts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural machine translation

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