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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34221 条记录，以下是4841-4850 订阅

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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Proceedings of 2020 IEEE/ACM Workshop on Machine Learning in High Performance computing Environments, MLHPC 2020 and Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, AI4S 2020 - Held in conjunction with SC 2020: The international conference for High Performance computing, Networking, Storage and Analysis

Proceedings of 2020 IEEE/ACM Workshop on Machine Learning in...

引用

6th IEEE/ACM Workshop on Machine Learning in High Performance computing Environments, MLHPC 2020 and 1st Workshop on Artificial Intelligence and Machine Learning for Scientific Applications, AI4S 2020

ISBN: (纸本)9780738110783

The proceedings contain 11 papers. The topics discussed include: accelerate distributed stochastic descent for Nonconvex optimization with momentum;accelerating GPU-based machine learning in python using MPI library: a case study with MVAPICH2-GDR;deep learning-based low-dose tomography reconstruction with hybrid-dose measurements;EventGraD: event-triggered communication in parallel stochastic gradient descent;a benders decomposition approach to correlation clustering;high-bypass learning: automated detection of tumor cells that significantly impact drug response;deep generative models that solve PDEs: distributed computing for training large data-free models;automatic particle trajectory classification in plasma simulations;reinforcement learning-based solution to power grid planning and operation under uncertainties;and predictions of steady and unsteady flows using machine-learned surrogate models.

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A Path Allocation Method for Opportunistic Networks with Limited Delay 10th

A Path Allocation Method for Opportunistic Networks with Lim...

引用

10th international conference on Computer Engineering and Networks, CENet 2020

作者： An, Zhiyuan Liu, Yan Wang, Lei Zhang, Ningning Dong, Kaili Liu, Xin Xiao, Kun Communication and Dispatching Center State Grid Henan Information and Telecommunication Company Zhengzhou450000 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811584619

In the future heterogeneous integrated wireless network environment, delay limited network has the characteristics of distributed self-organization, multi hop transmission, delay tolerance and intermittent link connection. Although it has great application value in many scenarios, it still needs to provide certain QoS guarantee according to the business requirements. Therefore, how to effectively transmit information to meet the needs of different services in terms of transmission rate and delay is one of the key issues in the research of path allocation in delay constrained networks. In this paper, a path allocation method based on service priority is proposed. Network nodes use local information to evaluate the network state and calculate the initial service priority, and then select relay nodes according to congestion degree and encounter probability. Simulation results show that the algorithm improves the quality of service of high priority services in congestion, and provides high performance services for low priority services when the network resources are sufficient. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Free Text Keystroke Dynamics-based Authentication with Continuous Learning: A Case Study

Free Text Keystroke Dynamics-based Authentication with Conti...

引用

IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Fouad Trad Ali Hussein Ali Chehab Electrical and Computer Engineering American University of Beirut Beirut Lebanon

Authenticating users based on their typing patterns has always been a target for cybersecurity professionals and researchers. Although previous studies have worked on free text-based authentication on mobile devices, none of them addressed the necessity of continuous learning under these settings. In this work, we propose a variational autoencoder (VAE) model that deals with this issue. As a case study, we consider a scenario where the model is initially trained on data collected from the user in a particular language (English). Then, the model is supposed to recognize the user when typing in another language (Korean). One way to adapt to that change is to retrain the model on a subset of the Korean data when it becomes available. By then, two scenarios can arise: 1) The English data still exists and the model is trained on the combination of English and Korean data; 2) The English data does not exist for security reasons or limited storage issues, and thus, we use the decoder part of our VAE to generate data based on what has been learned and then retrain the model on the mix. The average Equal Error Rate achieved among 50 participants was 3.23% and 3.55% for scenarios 1 and 2, respectively (~14% less than the baseline case where the model is not retrained). These results prove the need for continuous retraining of authentication models and highlight the efficiency of the proposed model and its ability to continuously learn, even without having access to the previous training data.

关键词： Degradation Adaptation models Error analysis Authentication Training data Switches Ubiquitous computing

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Keynote Lecture : Towards Robust, Large-scale Concurrent and distributed Programming

Keynote Lecture : Towards Robust, Large-scale Concurrent and...

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international Symposium on parallel and distributed computing

作者： Philipp Haller KTH Royal Institute of Technology Stockholm Sweden

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Software systems must satisfy rapidly increasing demands imposed by emerging applications. For example, new AI applications, such as autonomous driving, require quick responses to an environment that is changing continuously. At the same time, software systems must be fault-tolerant in order to ensure a high degree of availability. As it stands, however, developing these new distributed software systems is extremely challenging even for expert software engineers due to the interplay of concurrency, asynchronicity, and failure of components. The objective of our research is to develop reusable solutions to the above challenges by means of novel programming models and frameworks that can be used to build a wide range of applications. This talk reports on our work on the design, implementation, and foundations of programming models and languages that enable the robust construction of large-scale concurrent and distributed software systems.

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The Design and Implementation of a High-performance Portfolio Optimization Platform 23

The Design and Implementation of a High-performance Portfoli...

引用

23rd IEEE international conference on Computational Science and Engineering (CSE)

作者： Chen, Yidong Lu, Zhonghua Yang, Xueying Univ Chinese Acad Sci Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

ISBN: (纸本)9780738111162

Aiming at the high complexity of parameter optimization for portfolio models, this paper designs a distributed high-performance portfolio optimization platform(HPPO) based on parallel computing framework and event driven architecture. The platform consists of the data layer, the model layer, and the excursion layer, which is built in a component, pluggable, and loosely coupled way. The platform adopts parallelization acceleration for backtesting and optimizing parameters of portfolio models in a certain historical interval. The platform is able to docking portfolio model with real-time market. Based on the HPPO platform, a parallel program is designed to optimize the parameters of the value at risk(VAR) model. The performance of the platform are summarized by analyzing the experimental results and comparing with the open source framework Zipline and Rqalpha.

关键词： high-performance computing portfolio optimization portfolio backtesting periodic data parameter optimization

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Detection and Analysis Framework of Anomalous Internet Crime Data Based on Edge computing 5

Detection and Analysis Framework of Anomalous Internet Crime...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Cai, Yijun Li, Dian Wang, Yuyue Shanghai Univ Polit Sci & Law Shanghai 201701 Peoples R China Jiaxing Univ Fac Literature & Law Jiaxing 314033 Zhejiang Peoples R China Autodesk Software China Co Ltd Shanghai 200122 Peoples R China

ISBN: (纸本)9781728146850

Detection and analysis framework of anomalous Internet crime data based on edge computing is designed in this paper. The edge server is both the edge data processing center and the data storage center. The edge server receives the edge device data for processing and returns the processing result to the edge device. It complements cloud computing and cloud services, is close to users and data sources, and provides a new computing model for intelligent computing. Therefore, edge computing is another new computing model after distributed computing, grid computing, and cloud computing. Inspired by the features of this technology, this paper proposes the novel data crime analytic framework. The numerical experiment has proven the satisfactory performance of the proposed method.

关键词： Edge computing Internet Crime Data Mining Anomalous Information Detection and Analysis

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Location-Based Service Recommendation for Cold-Start in Mobile Edge computing 17th

Location-Based Service Recommendation for Cold-Start in Mobi...

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17th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2020

作者： Yu, Mengshan Fan, Guisheng Yu, Huiqun Chen, Liang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030794774

With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Service recommendation in such a distributed environment is a hot issue. However, the cold-start problem for service recommendation in mobile edge computing is still urgent to be solved. In this paper, we propose a service recommendation method based on collaborative filtering (CF) and user location, by comprehensively considering the characteristic of services at the edge and mobility of users. In detail, we first synthesize the service characteristics of each dimension through multidimensional weighting method. We further introduce the idea of Inverse_CF_Rec to the traditional CF and then predict the lost QoS value to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS value and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on collaborative filtering (MDICF) outperforms Inverse_CF_Rec in terms of the accuracy of recommendation. © 2021, IFIP international Federation for Information Processing.

关键词： Location based services

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KubeShare: A Framework to Manage GPUs as First-Class and Shared Resources in Container Cloud 20

KubeShare: A Framework to Manage GPUs as First-Class and Sha...

引用

29th international conference on High-Performance parallel and distributed computing (HPDC)

作者： Yeh, Ting-An Chen, Hung-Hsin Chou, Jerry Natl Tsing Hua Univ Hsinchu Taiwan

ISBN: (纸本)9781450370523

Container has emerged as a new technology in clouds to replace virtual machines (VM) for distributed applications deployment and operation. With the increasing number of new cloud-focused applications, such as deep learning and high performance applications, started to reply on the high computing throughput of GPUs, efficiently supporting GPU in container cloud becomes essential. While GPU virtualization has been extensively studied for VM, limited work has been done for containers. One of the key challenges is the lack of support for GPU sharing between multiple concurrent containers. This limitation leads to low resource utilization when a GPU device cannot be fully utilized by a single application due to the burstiness of GPU workload and the limited memory bandwidth. To overcome this issue, we designed and implemented KubeShare, which extends Kubernetes to enable GPU sharing with fine-grained allocation. KubeShare is the first solution for Kubernetes to make GPU device as a first class resources for scheduling and allocations. Using real deep learning workloads, we demonstrated KubeShare can significantly increase GPU utilization and overall system throughput around 2x with less than 10% performance overhead during container initialization and execution.

关键词： Cloud computing GPU Container Scheduling

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A Ransomware Classification Method Based on Entropy Map

A Ransomware Classification Method Based on Entropy Map

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： XiZhen Deng M. Jiang MingCan Cen College of Electronics and Engineering Guangxi Normal University China

With the rapid development and popularization of internet technologies such as AI and 5G, more and more organizations and individuals have suffered a dramatic increase in the number of ransomware attacks, which has brought substantial economic losses to them. Ransomware is an illegal act that blackmails victims into paying a ransom by locking their devices or encrypting files. Achieving fast and effective ransomware classification, attack intent and pattern analysis can improve the efficiency of security analysts and discover ransomware variants earlier. Therefore, we propose a new ransomware classification method, which uses the entropy map extracted from the ransomware binary file for classification. The entropy map retains more fine-grained features in the ransomware family, which can improve the classification result. Aiming at the problem of data imbalance among ransomware families, we propose a data augmentation method based on the Cycle-GAN network, which combines the fine-tuning technology in transfer learning to improve the classification result of the framework further. At the same time, the attention mechanism is introduced into VGG-16. It is used to enhance the ability of feature extraction of the network. The experimental results show that the proposed method achieves the best performance on 14 ransomware families, and the accuracy rate can reach 97.16%, which is better than other traditional ransomware visualization classification methods. Our proposed method still has the best classification performance compared with other neural networks.

关键词： Visualization Transfer learning Neural networks Organizations Network security Feature extraction Ubiquitous computing

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