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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

引用

4th International Conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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Higher-Order Quantum Genetic Algorithm and Its Application on Non-Linear Equations 6

Higher-Order Quantum Genetic Algorithm and Its Application o...

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2021 6th International Conference on Electronic Technology and Information Science, ICETIS 2021

作者： Jin, Lei Shan, Zheng Hu, Chunchao Xu, Le School of Information Engineering Zhengzhou University Zhengzhou Henan450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan450000 China

The high efficiency of quantum algorithms is caused by the quantum parallelism of the superposition principle and the quantum entangled, but traditional quantum genetic algorithms only use the quantum superposition principle. In order to further improve the performance of the algorithm, this paper proposes a new higher-order quantum genetic algorithm, which adds quantum entanglement properties on the basis of the principle of quantum superposition. Finally, the traditional quantum genetic algorithm and the higher-order quantum genetic algorithm are used to test a set of nonlinear equations many times. The results show that compared with the traditional quantum genetic algorithm, the evolutionary update operation of the higher-order quantum genetic algorithm does not involve multiple judgment conditions of the traditional quantum gate, requires less evolutionary algebra, and can converge quickly. © Published under licence by IOP Publishing Ltd.

关键词： Nonlinear equations

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BContext2Name: Naming Functions in Stripped Binaries with Multi-Label Learning and Neural Networks

BContext2Name: Naming Functions in Stripped Binaries with Mu...

引用

IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Bing Xia Yunxiang Ge Ruinan Yang Jiabin Yin Jianmin Pang Chongjun Tang Key Laboratory of Mathematical Engineering and Advanced Computing dept.ZhongYuan University of Technology Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis dept.ZhongYuan University of Technology Zhengzhou China dept.ZhongYuan University of Technology Zhengzhou China dept.Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Conducting binary function naming helps reverse engineers understand the internal workings of the code and perform malicious code analysis without accessing the source code. However, the loss of debugging information poses the challenge of insufficient high-level semantic information description for stripping binary code function naming. Meanwhile, the existing binary function naming scheme has one function label for only one sample. The long-tail effect of function labels for a single sample makes the machine learning-based prediction models face the challenge. To obtain a function correlation label and improve the propensity score of uncommon tail labels, we propose a multi-label learning-based binary function naming model BContext2Name. This model automatically generates relevant labels for binary function naming by function context information with the help of PfastreXML model. The experimental results show that BContext2Name can enrich function labels and alleviate the long-tail effect that exists for a single sample class. To obtain high-level semantics of binary functions, we align pseudocode and basic blocks based on disassembly and decompilation, identify concrete or abstract values of API parameters by variable tracking, and construct API-enhanced control flow graphs. Finally, a seq2seq neural network translation model with attention mechanism is constructed between function multi-label learning and enhanced control flow graphs. Experiments on the dataset reveal that the F1 values of the BContext2Name model improve by 3.55% and 15.23% over the state-of-the-art XFL and Nero, respectively. This indicates that function multi-label learning can provide accurate labels for binary functions and can help reverse analysts understand the inner working mechanism of binary code. Code and data for this evaluation are available at https://***/CSecurityZhongYuan/BContext2Name.

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Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval 24

Focus, Distinguish, and Prompt: Unleashing CLIP for Efficien...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Zeng, Gangyan Zhang, Yuan Wei, Jin Yang, Dongbao Zhang, Peng Gao, Yiwen Qin, Xugong Zhou, Yu School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Lenovo Research Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China TMCC College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9798400706868

Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which requires complicated text detection and/or recognition processes, resulting in inefficient and inflexible retrieval. Different from them, in this work we propose to explore the intrinsic potential of Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. Through empirical analysis, we observe that the main challenges of CLIP as a text retriever are: 1) limited text perceptual scale, and 2) entangled visual-semantic concepts. To this end, a novel model termed FDP (Focus, Distinguish, and Prompt) is developed. FDP first focuses on scene text via shifting the attention to the text area and probing the hidden text knowledge, and then divides the query text into content word and function word for processing, in which a semantic-aware prompting scheme and a distracted queries assistance module are utilized. Extensive experiments show that FDP significantly enhances the inference speed while achieving better or competitive retrieval accuracy compared to existing methods. Notably, on the IIIT-STR benchmark, FDP surpasses the state-of-the-art model by 4.37% with a 4 times faster speed. Furthermore, additional experiments under phrase-level and attribute-aware scene text retrieval settings validate FDP's particular advantages in handling diverse forms of query text. The source code will be available at https://***/Gyann-z/FDP. © 2024 ACM.

关键词： Semantics

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Research on Quantum Programming Framework Based on NISQ Function 6

Research on Quantum Programming Framework Based on NISQ Func...

引用

2021 6th International Conference on Electronic Technology and Information Science, ICETIS 2021

作者： Liu, Xiaonan Xu, Le Hu, Chunchao Jin, Lei School of Information Engineering Zhengzhou University Zhengzhou Henan450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan450000 China

In the past two decades, the field of quantum computing has developed rapidly, is gradually able to solve some difficult classical problems. With the development of the facilities for Noisy Intermediate-Scale Quantum(NISQ), the NISQ technology addresses the existing Quantum programming languages or frameworks, suggesting that they cannot fully meet the requirements of Quantum computers. In recent years, some quantum programming languages or frameworks have emerged which claim to support the classical computing algorithms of heterogeneous quantum. However, the code is too cumbersome and redundant, and it is difficult to map the code to heterogeneous quantum classical computation while meeting the requirements of timing. Therefore, a modular programming framework of heterogeneous quantum classical computing algorithm with NISQ function is proposed. The framework optimizes the quantum code and retains the quantum classical interactions that satisfy the temporal constraints. © Published under licence by IOP Publishing Ltd.

关键词： Quantum computers

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Embedding Attribute and Relation Information for Person Entity Alignment 6

Embedding Attribute and Relation Information for Person Enti...

引用

6th International Conference on Big Data and Information Analytics, BigDIA 2020

作者： Wang, Can Liu, Xiaonan Yin, Meijuan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Person entity alignment is to distinguish whether the person entity information extracted from different data sources refer to the same entity. Existing methods mainly use attribute and relation information of entity to solve entity alignment problem. Most of them are based on common information of entity, while ignore some non-common but semantically similar attribute information and they only consider the relation between first-order adjacent entity, which makes it difficult to accurately align entity in the case of scarcity of common information. so we propose a person entity alignment method based on joint embedding of attribute and relation information, which makes full use of non-common but related information of entity to align them and improves the Hit@k of alignment results when common information is sparse. In the method, the representation vector of attribute information and the representation vector of relation information are obtained by embedding. During embedding, we consider the identification of different attributes and the second-order relation between entities to improve the role of feature attributes and the second-order relation in entity alignment. When calculating the similarity of entities, binary linear regression model is used to fit the weight of attribute information and relation information similarity, so as to adapt to the different characteristics of the entity data under different circumstances. The experimental results show that, compared with the related methods, the Hit@1 value and Hit@10 value of the proposed method increased by more than 8.4% and more than 9.1% respectively. © 2020 IEEE.

关键词： Embeddings

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Design and Implementation of Links Generation for Inter Domain Routing System 12

Design and Implementation of Links Generation for Inter Doma...

引用

12th International Conference on Information Science and Technology, ICIST 2022

作者： Wang, Yu Qiao, Zhi Yin, Junru Zhang, Mingliang College of Software Henan University of Engineering Zhengzhou China College of Information and Electrical Engineering China Agriculture University Beijing China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Network Research Department Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments. © 2022 IEEE.

关键词： Genetic algorithms

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Variational Quantum Algorithm and Its Application on Non-Linear Equations 2

Variational Quantum Algorithm and Its Application on Non-Lin...

引用

2021 2nd International Conference on Computer Information and Big Data Applications, CIBDA 2021

作者： Yang, Yihang Shan, Zheng Zhao, Bo Xu, Le School of Information Engineering Zhengzhou University Zhengzhou Henan450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan450000 China

Quantum algorithms of factoring problem have been paid more and more attention since Shor's algorithm was proposed. Combining the current quantum computer hardware level and integer factorization quantum algorithms, this paper proposes a "classical + quantum"hybrid solution. This scheme first adopts classical methods and corresponding rules in the preprocessing step to simplify the nonlinear equations, which is used to reduce the number of qubits needed for the cost Hamiltonian. Then the variational quantum algorithm is used to find the approximate ground state of the cost Hamiltonian, which encodes the solution of the nonlinear equations. The program was verified on the IBM QX4 quantum machine, and the results showed that this hybrid solution can effectively reduce the quantum resources required to solve the nonlinear equations. © Published under licence by IOP Publishing Ltd.

关键词： Nonlinear equations

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An Efficient Sieve Algorithm with Evolutionary Technique 20

An Efficient Sieve Algorithm with Evolutionary Technique

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20th IEEE International Conference on Communication Technology, ICCT 2020

作者： Sun, Zedong Gu, Chunxiang Zheng, Yonghui State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728181417

Sieve algorithm plays an important role in solving the shortest vector problem, which is one of the most representative hard lattice problems. In this paper, we propose an efficient sieve algorithm with evolutionary techniques. By combining Gauss Sieve algorithm with global selection, we propose an efficient sieve algorithm. Experimental results show that our algorithm performs better than FPLLL's Gauss Sieve algorithm in solving the shortest vector problem. Furthermore, we explore how mutation behaves in accelerating sieve algorithm. © 2020 IEEE.

关键词： Evolutionary algorithms

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A new method for solving the linear recurrence relation with nonhomogeneous constant coefficients in some cases 4

A new method for solving the linear recurrence relation with...

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2021 4th International Conference on Mechatronics and Computer Technology engineering, MCTE 2021

作者： Yang, Ben-Chao Han, Xue-Feng State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou China School of Mathematics and Information Science Henan Polytechnic University Henan Jiaozuo China

Recursive relation mainly describes the unique law satisfied by a sequence, so it plays an important role in almost all branches of mathematics. It is also one of the main algorithms commonly used in computer programming. This paper first introduces the concept of recursive relation and two common basic forms, then starts with the solution of linear recursive relation with non-homogeneous constant coefficients, gives a new solution idea, and gives a general proof. Finally, through an example, the general method and the new method given in this paper are compared and verified. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Computer programming

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