检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Label Distribution Feature Selection Based on Neighborhood Rough Set

SSRN

引用

SSRN 2024年

作者： Wu, Yilin Guo, Wenzhong Lin, Yaojin College of Computer and Data Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Minnan Normal University Zhangzhou363000 China

In label distribution learning, an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in label distribution learning is to evaluate the discriminable ability of each feature. Motivated by neighborhood rough set, which can be used to measure the dependency degree of feature via constructing neighborhood relations on feature space and label space, respectively, this paper proposes a novel lable distribution feature selection method. In this paper, the neighborhood class of instance in label distribution space is defined, which is beneficial to recognize the logical class of target instance. Then, a new neighborhood rough set model for label distribution learning is proposed. Specially, the dependency degree of feature combining label weight is defined. Finally, a label distribution feature selection based on neighborhood rough set is presented. Extensive experiments on 12 data sets show the effectiveness of the proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Risk Management Using Big Real Time Data

Risk Management Using Big Real Time Data

引用

IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Jie Cheng Chunming Rong Huijuan Ye Xianghan Zheng Department of Electrical Engineering and Computer Science Risk Management Using Big Real Time Data Department of Computer Science and Electronic Engineering University of Stavanger Stavanger Norway College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

关键词： Delays Biological system modeling Meteorology Atmospheric modeling Predictive models Airports Splines (mathematics)

来源：评论

学校读者我要写书评

暂无评论

A Cost-Driven Fuzzy Scheduling Strategy for intelligent Workflow Decision Making Systems in Uncertain Edge-Cloud Environments

arXiv

引用

arXiv 2021年

作者： Lin, Bing Lin, Chaowei Chen, Xing College of Physics and Energy Fujian Normal University Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China School of Computer Science Peking University 100871 China College of Mathematics and Computer Science Fuzhou University Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China

Workflow decision making is critical to performing many practical workflow applications. Scheduling in edge-cloud environments can address the high complexity problem of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, because of the heterogeneous resources in edge-cloud environments and the complicated data dependencies among the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. The existing studies have been mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of workflow applications mainly caused by task computation and data transmission, while satisfying the workflow deadline in uncertain edge-cloud environments. The Triangular Fuzzy Numbers (TFNs) are adopted to represent the task processing time and data transferring time. A cost-driven fuzzy scheduling strategy based on an Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm is proposed, which employs the operators of Genetic Algorithm (GA). This strategy introduces the randomly two-point crossover operator, neighborhood mutation operator, and adaptive multipoint mutation operator of GA to effectively avoid converging on local optima. The experimental results show that our strategy can effectively reduce the workflow execution cost in uncertain edge-cloud environments, compared with other benchmark solutions. Copyright © 2021, The Authors. All rights reserved.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Interpretable multi-view attention network for drug-drug interaction prediction

Interpretable multi-view attention network for drug-drug int...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Qi Wen Sijie Yang Zu-Guo Yu Yahui Long Xiangxiang Zeng College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research(A*STAR) Singapore College of Information Science and Engineering Hunan University China

Drug-drug interaction (DDI) plays an increasingly crucial role in drug discovery. Predicting potential DDI is also essential for clinical research. Given the high cost and risk of wet-lab experiments, in-silico DDI prediction is an alternative choice. Recently, deep learning methods have been developed for DDI prediction. However, most of existing methods focus on feature extraction from either molecular SMILES sequences or drug interactive networks, ignoring the valuable complementary information that can be derived from these two views. In this paper, we propose a novel interpretable Multi-View Attention network (MVA-DDI) for DDI prediction. MVA-DDI can effectively extracts drug representations from different perspectives to improve DDI prediction. Specifically, for a given drug, we design a transformer-based encoder and a graph convolutional networkbased encoder to learn sequence and graph representations from SMILES sequence and molecular graph, respectively. To fully exploit the complementary information between the sequence and molecular views, an attention mechanism is further adopted to adaptively aggregate the sequence and graph representations by taking the importance of different views into accounts, generating the final drug representations. Comparison experiments demonstrated that our MVA-DDI 1 model achieved superior performance to state-of-the-art models on DDI prediction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analysis of smart glucose meter stability from the economic perspective

Boletin Tecnico/Technical Bulletin

引用

Boletin Tecnico/Technical Bulletin 2017年第9期55卷 457-462页

作者： Lin, Zhongyan Song, Libin Zheng, Dachuan Li, Zuoyong Internet Innovation Research Center College's Research Base of Humanities and Social Science in Fujian Province Minjiang University Fuzhou Fujian350108 China Fuzhou Digital Health Industry Technology Innovation Center Minjiang University Fuzhou Fujian350108 China Fuzhou Comvee Network Technology Co. Ltd. Fuzhou Fujian350001 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou Fujian350108 China

Objective: to explore and interpret the model of diabetes mobile online management from the perspective of web model. Methods: from the interdisciplinary perspective, combined with economics, management science and computer science, with the only dynamic model in microeconomics—the cobweb model, from chronic disease management, Internet + medical application, analyze the application of Internet technology in diabetes management. Results: through mobile Internet technology, optimized the unit of diabetes management and realized the "monitoring - assessment - intervention" closed-loop management. Conclusion: explore the digital medical health industry in periodic production commodity price fluctuations and other activities, can establish early warning system and mechanism, to improve patients and their families to the cognitive level of diabetes, according to the medical behavior and self management ability.

关键词： Medical applications

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence-Generated Terahertz Multi-Resonant Metasurfaces via Improved Transformer and CGAN Neural networks

arXiv

引用

arXiv 2023年

作者： Huang, Yangpeng Feng, Naixing Cai, Yijun Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen University of Technology Xiamen361000 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China

It is well known that the inverse design of terahertz (THz) multi-resonant graphene metasurfaces by using traditional deep neural networks (DNNs) has limited generalization ability. In this paper, we propose improved Transformer and conditional generative adversarial neural networks (CGAN) for the inverse design of graphene metasurfaces based upon THz multi-resonant absorption spectra. The improved Transformer can obtain higher accuracy and generalization performance in the StoV (Spectrum to Vector) design compared to traditional multilayer perceptron (MLP) neural networks, while the StoI (Spectrum to Image) design achieved through CGAN can provide more comprehensive information and higher accuracy than the StoV design obtained by MLP. Moreover, the improved CGAN can achieve the inverse design of graphene metasurface images directly from the desired multi-resonant absorption spectra. It is turned out that this work can finish facilitating the design process of artificial intelligence-generated metasurfaces (AIGM), and even provide a useful guide for developing complex THz metasurfaces based on 2D materials using generative neural networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

来源：评论

学校读者我要写书评

暂无评论

Cost-Driven Scheduling for Deadline-Constrained Workflow on Multi-clouds

Cost-Driven Scheduling for Deadline-Constrained Workflow on ...

引用

IEEE International Symposium on Parallel and Distributed processing Workshops and Phd Forum (IPDPSW)

作者： Bing Lin Wenzhong Guo Guolong Chen Naixue Xiong Rongrong Li College of Mathematics and Computer Sciences Fuzhou University Fuzhou PR China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Proccssing Fuzhou PR China School of Computer Science Colorado Technical University Colorado Springs CO USA

ISBN: (纸本)9781467376853

The tremendous parallel computing ability of Cloud computing as a new service provisioning paradigm encourages investigators to research its drawbacks and advantages on processing large-scale scientific applications such as workflows. The current Cloud market is composed of numerous diverse Cloud providers and workflow scheduling is one of the biggest challenges on Multi-Clouds. However, the existing works fail to either satisfy the Quality of Service (QoS) requirements of end users or involve some fundamental principles of Cloud computing such as pay-as-you-go pricing model and heterogeneous computing resources. In this paper, we adapt the Partial Critical Paths algorithm (PCPA) for the multi-cloud environment and propose a scheduling strategy for scientific workflow, called Multi-Cloud Partial Critical Paths (MCPCP), which aims to minimize the execution cost of workflow while satisfying the defined deadline constrain. Our approach takes into account the essential characteristics on Multi-Clouds such as charge per time interval, various instance types from different Cloud providers as well as homogeneous intra-bandwidth vs. Heterogeneous inter-bandwidth. Various well-know workflows are used for evaluating our strategy and the experimental results show that the proposed approach has a good performance on Multi-Clouds.

关键词： Scheduling Schedules Cloud computing Processor scheduling Data transfer Quality of service Bandwidth

来源：评论

学校读者我要写书评

暂无评论

Image Color Correction Database for Subjective Perceptual Consistency Assessment

引用

Tien Tzu Hsueh Pao/Acta Electronica Sinica 2017年第7期45卷 1677-1685页

作者： Zhang, Hai-Feng Niu, Yu-Zhen Xue, Yu-Yang College of Mathematics and Computer Science Fuzhou University FuzhouFujian350108 China Fujian Province Key Laboratory of Network Computing and Intelligent Information Process FuzhouFujian350108 China

To achieve objective image correction quality assessment results that are consistent with subjective perception, we create an Image Color Correction Database (ICCD). ICCD contains a variety of types and scales of color difference. The types of color difference include the differences in brightness, hue, saturation, exposure, contrast and R and G channels. Each type has three different scales. We select six state-of-the-art color correction algorithms to perform color correction for each target image. Then we design and conduct user study to get users' Mean Opinion Score (MOS). Based on ICCD, we evaluate the performance of six color correction algorithms. For most of types and scales of color difference, Pitie's iterative color distribution transfer algorithm performs best. We also evaluate the 14 objective image quality assessment metrics and pick out three better assessment metrics that achieve better consistency with MOS than existing methods. © 2017, Chinese Institute of Electronics. All right reserved.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

ANDROIDOFF:Offloading android application based on cost estimation

引用

Journal of Systems and Software 2019年 158卷 110418-000页

作者： Chen, Xing Chen, Jiaqing Liu, Bichun Ma, Yun Zhang, Ying Zhong, Hao College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China School of Software Tsinghua University Beijing100084 China National Engineering Research Center of Software Engineering Peking University China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Computation offloading is a promising way of improving the performance and reducing the battery power consumption, since it moves some time-consuming computation activities to nearby servers. Although various approaches have been proposed to support computation offloading, we argue that there is still sufficient space for improvements, since existing approaches cannot accurately estimate the execution costs. As a result, we find that their offloading plans are less optimized. To handle the problem, in this paper, given an Android application, we propose a novel approach, called ANDROIDOFF, that supports offloading at the granularity of objects. Supporting such capability is challenging due to the two reasons: (1) through dynamic execution, it is feasible to collect the execution costs of only partial methods, and (2) it is difficult to accurately estimate the execution costs of the remaining methods. To overcome the challenges, given an Android application, ANDROIDOFF first combines static and dynamic analysis to predict the execution costs of all its methods. After all the costs are estimated, ANDROIDOFF synthesizes an offloading plan, in which determines the offloading details. We evaluate ANDROIDOFF on a real-world application, with two mobile devices. Our results show that, compared with other approaches, ANDROIDOFF saves the response time by 8%–49% and reduces the energy consumption by 12%–49% on average for computation-intensive applications. © 2019 Elsevier Inc.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：