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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Jia, Yifan Duan, Guanhua Song, Yanyun Ye, Lan Liu, Zhi Shandong Sports Rehabilitation Research Center Jinan China The Second Hospital of Shandong University The Cancer Center Jinan China The School of Information Science and Engineering Shandong University Qingdao China State Key Laboratory of High Performance Server and Storage Technology Jinan China

ISBN: (纸本)9798350373820

In order to address the coloring problem in breast cancer, we propose an innovative generator network architecture paradigm and a pooling-based loss function to tackle the challenge of strict alignment between image pairs. Our network architecture primarily consists of a content encoder, a style encoder, a decoder, and a TSA skip-connection module. The content encoder ensures that content features are preserved during coloring, while the style encoder guarantees the accuracy of coloring styles. The TSS skipconnection layer based on a transformer architecture is employed to facilitate the flow of feature information. Experimental evaluations on four coloring datasets demonstrate that our model exhibits superior performance and robustness compared to existing models. © 2024 IEEE.

关键词： Network architecture

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A fast, scalable, and energy-efficient edge acceleration architecture based on FPGA cluster 21

A fast, scalable, and energy-efficient edge acceleration arc...

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17th ACM International Conference on emerging Networking EXperiments and Technologies, CoNEXT 2021

作者： Li, Rengang Su, Dongdong Kan, Hongwei Inspur Electronic Information Industry Co. Ltd. State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9781450390989

FPGA-based acceleration has been emerged to avoid the cloud computing overload problem by accelerating the compute-intensive workload on edge networks. Though existing studies for FPGA-based edge acceleration have focused on optimizing the computing time, they did not address the burden of the FPGA-aided server under an enormous computing requests circumstance. The massive computing requests can cause delays in transmission and processing at the FPGA-aided server, leading to long response times and high system energy consumption. Therefore, we propose an emerging edge acceleration architecture based on FPGA cluster over the low-latency RDMA-based network. Preliminary simulation results demonstrate that our architecture is fast, scalable, and energy-efficient in comparison with the FPGA-aided servers cluster. © 2021 Owner/Author.

关键词： Field programmable gate arrays (FPGA)

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Research on Application of Genetic Algorithms in Corporal Portal Search Engines 5

Research on Application of Genetic Algorithms in Corporal Po...

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5th IEEE Advanced Information technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Qiu, Ye Wang, Dechao Yan, Hongli Jinan Rail Transit Group Co. Ltd Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

This paper introduces the genetic algorithm followed by implementation steps of it. We began our work with researching and summarizing the application of genetic algorithm in Search Engine Optimization (SEO) of enterprise portal, which shows the effectiveness of genetic algorithm deployed in solving the query optimization problem. Later on, we gave the implementation steps and system framework of the query optimization method based on genetic algorithm. Related studies have proved that genetic algorithm applied in SEO have the potential to optimize the search engine of enterprise portal and achieve fair results. © 2021 IEEE.

关键词： Genetic algorithms

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ROWE-tree: A Read-Optimized and Write-Efficient B+-tree for Persistent Memory 51

ROWE-tree: A Read-Optimized and Write-Efficient B+-tree for ...

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51st International Conference on Parallel Processing, ICPP 2022

作者： Zou, Xiaomin Wang, Fang Feng, Dan Guan, Tianjin Su, Nan Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen Wuhan China State Key Laboratory of High-end Server and Storage Technology China Shandong Massive Information Technology Research Institute Jinan China

ISBN: (纸本)9781450397339

Persistent memory (PM) exhibits a huge potential to provide B+-tree indexes with high performance, efficient persistence, and instant recovery. A large number of PM-optimized B+-tree indexes have been proposed, but most of them fail to provide high performance for both read and write operations because: (1) their designs of search optimization and insert improvement are often traded off against each other, and (2) they overlook the read/write interference problem of PM which incurs unpredictable performance degradation. In this paper, we propose ROWE-tree, a read-optimized and write-efficient B+-tree for PM. The designs of our ROWE-tree consist of three key points. First, we propose two techniques to make a good trade-off between write and read performance: self-verifying insertion, which reduces consistency overhead by using the key itself as a persist mark instead of additional metadata, and semi-sorted leaf nodes, which use append-only insertion to avoid the shifting overhead of sorting nodes but keep intra-cache-line items sorted to accelerate the lookup. Second, based on the observation that data accesses are highly skewed in real-world workloads, we build an in-DRAM cache of hot items to outsource accesses to hot items to DRAM. By doing so, we can alleviate the read/write interference of PM and significantly improve overall performance. Third, to cope with the dynamic changes of hot items, we exploit a lightweight mechanism to track such changes at run-time. Using Intel Optane DCPMM, our evaluations show that ROWE-tree obtains up to 3.86 × higher performance than the state-of-the-art PM B+-tree indexes under YCSB workloads. © 2022 ACM.

关键词： Dynamic random access storage

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DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

arXiv

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arXiv 2025年

作者： Liu, Guanlin Yu, Xiaomei Liu, Zihao Li, Xue Fan, Xingxu Zheng, Xiangwei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China State Key Laboratory of High-end Server and Storage Technology Jinan250300 China

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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A Survey of Methods for Handling Disk Data Imbalance

SSRN

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SSRN 2023年

作者： Yuan, Shuangshuang Wu, Peng Chen, Yuehui Li, Qiang School of Information Science and Engineering University of Jinan Jinan China State Key Laboratory of High-end Server & Storage Technolog Jinan China

Class imbalance exists in many classification problems, and since the data is designed for accuracy, imbalance in data classes can lead to classification challenges with a few classes having higher misclassification costs. The Backblaze dataset, a widely used dataset related to hard discs, has a small amount of failure data and a large amount of health data, which exhibits a serious class imbalance. This paper provides a comprehensive overview of research in the field of imbalanced data classification. The discussion is organized into three main aspects: data-level methods, algorithmic-level methods, and hybrid methods. For each type of method, we summarize and analyze the existing problems, algorithmic ideas, strengths, and weaknesses. Additionally, the challenges of unbalanced data classification are discussed, along with strategies to address them. It is convenient for researchers to choose the appropriate method according to their needs. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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A SURVEY OF METHODS FOR HANDLING DISK DATA IMBALANCE

arXiv

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arXiv 2023年

关键词： Classification (of information)

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Application of Text Dimensionality Reduction Method in Information Filtering on Railway Transit Cloud Platform

Application of Text Dimensionality Reduction Method in Infor...

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2020 International Conference on Modeling, Big Data Analytics and Simulation, MBDAS 2020

作者： Qiu, Ye Li, Gang An, Junfeng Jinan Rail Transit Group Co. Ltd. Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

This paper focuses on the useful information data extraction of urban rail transportation's urban rail cloud platform. Through the correlation and redundancy between feature items in the text vector space of a large amount of information data, a method of clustering text vector space dimensionality reduction is proposed and applied to the information data extraction of urban rail cloud platform. The vector aggregation theory and feature selection are applied to reduce the dimensionality of the feature space, so that the reduced dimensional feature items are more representative of the category. Through test experiments, it is proved that this text dimensionality reduction method algorithm is applied to the information data filtering of urban rail cloud platform, which effectively reduces the dimensionality of vector space and improves the accuracy and speed of text classification in urban rail cloud platform information data. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Adaptive Prediction Of Online Learning Based On Machine Learning Algorithms

Adaptive Prediction Of Online Learning Based On Machine Lear...

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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Qiang Yin Xiaomei Yu Lixiang Zhao Yongqin Li School of Information Science and Engineering Shandong Normal University Jinan China China State Key Laboratory of High–end Server & Storage Technology Jinan China Faculty of Education Shandong Normal University Jinan China

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

In recent years, with the advancement of society and the rapid development of internet technology, the popularity of online education has been steadily increasing. Presently, nearly every educational institution worldwide, from elementary schools to universities, has incorporated some level of online teaching. While online instruction offers certain conveniences, it will also be accompanied by the emergence of some problems, given the substantial surge in data related to online learning among students, there is an urgent need for robust data processing methods to forecast their suitability for online education. Previous research underscores the effectiveness of machine learning in tackling diverse data classification tasks. However, there is a scarcity of studies comprehensively comparing various machine learning approaches in the context of online education data. This article utilizes seven distinct, parameter-optimized machine learning approaches to explore a range of performance predictions, spanning both binary and multi-class tasks. During the experimental process, four evaluation metrics and visual aids were employed to assess and compare the methods across these tasks, accompanied by a comprehensive discussion of the experimental results. The results of the experiments reveal that the random forest method demonstrates strong versatility across all selected datasets.

关键词： Measurement Adaptation models Visualization Machine learning algorithms Education Machine learning Predictive models Market research Surges Random forests

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A Parallelizable Counterfactual Generation Method Based on Gradient Optimization 29

A Parallelizable Counterfactual Generation Method Based on G...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Ding, Haorun Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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