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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Hu, Lei Zhang, Yang Zhang, Shukui Pan, Wubing Wang, Yingying Soochow University School of Computer Science and Technology Suzhou215006 China Suzhou Global Institute School of Information & Software Suzhou215163 China

ISBN: (纸本)9798331509712

In environments lacking infrastructure, such as the battlefield, a diverse array of intelligent devices continues to evolve. These devices face challenges in flexibly addressing resource allocation and information transmission within their communication range, necessitating exploration of business awareness and resource identification technologies. Addressing these issues hinges on solving object identification problems related to address, identity, content, service, location, and resource. To tackle these challenges, we propose a Mult-IP object identification mechanism that serves as the foundation for information exchange, task allocation, and service provisioning among heterogeneous communication entities. Additionally, we design a multi-dimensional resource and service intelligence matching mechanism based on object identification. Building upon this framework is the intent-driven control algorithm AntEdge which dynamically senses task states using information encapsulated in Mult-IP packets and optimizes task packet transmission according to business intentions. Finally, leveraging the Mult-IP object identification mechanism enables task offloading to achieve timely completion of tasks within limited timeframes. Simulation experiments demonstrate that our proposed algorithm effectively enhances task completion ratios. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

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computers, Materials & Continua 2023年第7期76卷 849-859页

作者： Baowei Wang Neng Wang Yuxiao Zhang Zenghui Xu Junhao Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044JiangsuChina Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044JiangsuChina

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data *** speaking,EHRs are widely used in blockchain-based medical data *** are valuable private assets of patients,and the ownership belongs to *** recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit *** order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate *** paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted *** the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical *** addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical *** the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the *** analysis and security analysis illustrate the completeness and feasibility of the scheme.

关键词： Electronic health records cross-chain medical certificate blockchain data deletion and recovery

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Hijack Vertical Federated Learning Models As One Party

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IEEE Transactions on Dependable and Secure Computing 2024年 1-18页

作者： Qiu, Pengyu Zhang, Xuhong Ji, Shouling Li, Changjiang Pu, Yuwen Yang, Xing Wang, Ting College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China School of Software Technology Zhejiang University Ningbo Zhejiang China College of Information Science and Technology Pennsylvania State University State College PA USA Hefei Interdisciplinary Center National University of Defense Technology Hefei Anhui China

Vertical Federated Learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. However, the security of the VFL model remains underexplored, particularly regarding the Byzantine Generals Problem (BGP), which is a well-known issue in distributed systems. This paper focuses on revealing the threat of BGP in VFL systems. Specifically, we propose two attacks, the replay attack and the generation attack, to evaluate the vulnerability of VFL when there is only one malicious party. The goal of the adversary is to hijack the VFL model to give desired predictions. Moreover, considering the uneven distribution of importance among parties, we combine data poisoning with the aforementioned attacks to explore whether they can bypass the situation where the adversary has few features. The evaluation results demonstrate the effectiveness of our attacks. For instance, the adversary holding only 10 90 capability is limited and usually at the cost of performance loss of the VFL task. Our work highlights the need for advanced defenses to protect the prediction results of a VFL model and calls for more exploration of VFL's security issues. IEEE

关键词： Feature extraction

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RealDriftGenerator: A Novel Approach to Generate Concept Drift in Real World Scenario

RealDriftGenerator: A Novel Approach to Generate Concept Dri...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Borong Huang, Chao Zhu, Xiaohui Jin, Nanlin School of Advance Technology Xi'an Jiaotong-Liverpool University Department of Computer Science and Software Engineering SuZhou China University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9781665410205

Concept drift refers to the probability distribution of data generation changes over time in a data stream environment. In recent years, there has been an increasing interest in drift detection models. However, due to the lack of labeled concept drift datasets, most researchers tend to using synthetic drift data generators for model training. These generators only have relatively simple feature distributions, which fail to capture the complexity found in real-world scenarios. This paper introduces a real scenario concept drift label generator (RealDriftGenerator). This generator aims to preserve the complexity and temporal correlation of real-world scenario while generating concept drifts with user defined drift positions and drift widths. The validation result show that the temporal correlation coefficients of RealDriftGenerator is significantly higher than benchmark drift generators. Additionally, the ability of RealDriftGenerator to capture the complexity in real-world scenarios is 20% higher than benchmark drift generators(measured by model performance). The source code of RealDriftGenerator has been published on https://***/sniperrifle71/realDriftGenerator. © 2024 IEEE.

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ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization 30

ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean...

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30th International Conference on Principles and Practice of Constraint Programming, CP 2024

作者： Chen, Zhihan Lin, Peng Hu, Hao Cai1, Shaowei State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959773362

As a broadly applied technique in numerous optimization problems, recently, local search has been employed to solve Pseudo-Boolean Optimization (PBO) problem. A representative local search solver for PBO is LS-PBO. In this paper, firstly, we improve LS-PBO by a dynamic scoring mechanism, which dynamically strikes a balance between score on hard constraints and score on the objective function. Moreover, on top of this improved LS-PBO, we develop the first parallel local search PBO solver. The main idea is to share good solutions among different threads to guide the search, by maintaining a pool of feasible solutions. For evaluating solutions when updating the pool, we propose a function that considers both the solution quality and the diversity of the pool. Furthermore, we calculate the polarity density in the pool to enhance the scoring function of local search. Our empirical experiments show clear benefits of the proposed parallel approach, making it competitive with the parallel version of the famous commercial solver Gurobi. © Zhihan Chen, Peng Lin, Hao Hu, and Shaowei Cai.

关键词： Local search (optimization)

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Parallel software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of computer science & technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios 20

A Method Based on Genetic Programming to Automatically Const...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ma, Yan Zhang, Changsheng Gao, Yan Guo, Ying School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (纸本)9798350356328

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated. © 2024 IEEE.

关键词： Genetic programming

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Research of Automatic Scoring of Essays Based on Data Augmentation 24

Research of Automatic Scoring of Essays Based on Data Augmen...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Guo, Weiqin Yang, Yong Ren, Ge School of Software Xinjiang University Xinjiang Urumqi China College of Computer Science and Technology Xinjiang Normal University Xinjiang Urumqi China

ISBN: (纸本)9798400710865

The article discusses the research on automated essay scoring based on data augmentation, focusing on the use of data augmentation methods to improve the performance of automated essay scoring systems. In previous studies, researchers have also applied data augmentation methods to the field of automated essay scoring and achieved certain results. However, the past research still has certain defects, such as the problem of the deviation between the representation vectors of the augmented text and the original text labels. This study calculates the cosine similarity between the representation vectors of the augmented text and the original text, and uses the obtained cosine similarity to screen the augmented text, in order to solve the problem of the deviation between the augmented text and the original labels. This study conducts data augmentation on the corpus of automated essay scoring and screens the augmented data to improve the training effect, reducing the negative impact of biased data on model training, and ultimately achieved a QWK (Quadratic Weighted Kappa) score of 80.21 on the ASAP (Automated Student Assessment Prize) automated essay scoring dataset. In model construction, long convolution is adopted to improve performance, and the effect is verified in the experiments, confirming the role of long convolution in the task of automated essay scoring. © 2024 Copyright held by the owner/author(s).

关键词： Natural language processing systems

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DBFed: Debiasing Federated Learning Framework based on Domain-Independent 9

DBFed: Debiasing Federated Learning Framework based on Domai...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Li, Jiale Li, Zhixin Wang, Yibo Li, Yao Wang, Lei Dalian University of Technology School of Software Dalian China Fudan University School of Computer Science Shanghai China

ISBN: (纸本)9798350331240

As digital transformation continues, enterprises are generating, managing, and storing vast amounts of data, while artificial intelligence technology is rapidly advancing. However, it brings challenges in information security and data security. Data security refers to the protection of digital information from unauthorized access, damage, theft, etc. throughout its entire life cycle. With the promulgation and implementation of data security laws and the emphasis on data security and data privacy by organizations and users, Privacy-preserving technology represented by federated learning has a wide range of application scenarios. Federated learning is a distributed machine learning computing framework that allows multiple subjects to train joint models without sharing data to protect data privacy and solve the problem of data islands. However, the data among multiple subjects are independent of each other, and the data differences in quality may cause fairness issues in federated learning modeling, such as data bias among multiple subjects, resulting in biased and discriminatory models. Therefore, we propose DBFed, a debiasing federated learning framework based on domain-independent, which mitigates model bias by explicitly encoding sensitive attributes during client-side training. This paper conducts experiments on three real datasets and uses five evaluation metrics of accuracy and fairness to quantify the effect of the model. Most metrics of DBFed exceed those of the other three comparative methods, fully demonstrating the debiasing effect of DBFed. © 2023 IEEE.

关键词： Sensitive data

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

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41st computer Graphics International Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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