以旅游大数据为基础,考虑长时间范围内的滞后效应以及不同搜索强度指数(Search Intensity Index,SII)之间的多任务影响,提出一种基于大数据的多任务旅游信息分析(Multi-tasking Tourism Information Analysis Based on Big Data,MTIABD...以旅游大数据为基础,考虑长时间范围内的滞后效应以及不同搜索强度指数(Search Intensity Index,SII)之间的多任务影响,提出一种基于大数据的多任务旅游信息分析(Multi-tasking Tourism Information Analysis Based on Big Data,MTIABD)框架。使用融合信息重排序技术预测旅游需求,具体根据图引导结构模拟历史变量对未来变量的滞后影响。每个变量通过时间维度上的卷积神经网络(Convolutional Neural Network,CNN)进行独立编码,利用二分图动态建模滞后效应,通过图聚合进行挖掘,实现对旅游需求的精准预测。基于上述技术,构建旅游需求预测系统,旅游者能够根据需求检索不同景点的信息。在真实数据集上进行大量实验,结果表明所提出的MTIABD框架在一步和多步预测方面均优于现有方法。在平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)指标下,相较于基于实例的多变量时间序列图预测框架(Instance-wise Graph-rased Framework for Multivariate Time Series Forecasting,IGMTF),MTIABD在HK-2021数据集上的性能提高了16.75%,在MO-2021数据集上的性能提高了19.79%。展开更多
图对比学习因其可有效缓解数据稀疏问题被广泛应用在推荐系统中.然而,目前大多数基于图对比学习的推荐算法均采用单一视角进行学习,这极大地限制了模型的泛化能力,且图卷积网络本身存在的过度平滑问题也影响着模型的稳定性.基于此,提出...图对比学习因其可有效缓解数据稀疏问题被广泛应用在推荐系统中.然而,目前大多数基于图对比学习的推荐算法均采用单一视角进行学习,这极大地限制了模型的泛化能力,且图卷积网络本身存在的过度平滑问题也影响着模型的稳定性.基于此,提出一种融合层注意力机制的多视角图对比学习推荐方法.一方面,该方法提出2种不同视角下的3种对比学习,在视图级视角下,通过对原始图添加随机噪声构建扰动增强视图,利用奇异值分解(singular value decomposition)重组构建SVD增强视图,对这2个增强视图进行视图级对比学习;在节点视角下,利用节点间的语义信息分别进行候选节点和候选结构邻居对比学习,并将3种对比学习辅助任务和推荐任务进行多任务学习优化,以提高节点嵌入的质量,从而提升模型的泛化能力.另一方面,在图卷积网络学习用户和项目的节点嵌入时,采用层注意力机制的方式聚合最终的节点嵌入,提高模型的高阶连通性,以缓解过度平滑问题.在4个公开数据集LastFM,Gowalla,Ifashion,Yelp上与10个经典模型进行对比,结果表明该方法在Recall,Precision,NDCG这3个指标上分别平均提升3.12%,3.22%,4.06%,这说明所提方法是有效的.展开更多
图池化作为图神经网络中重要的组件,在获取图的多粒度信息的过程中扮演了重要角色。而当前的图池化操作均以平等地位看待数据点,普遍未考虑利用邻域内数据之间的偏序关系,从而造成图结构信息破坏。针对此问题,本文提出一种基于偏序关系...图池化作为图神经网络中重要的组件,在获取图的多粒度信息的过程中扮演了重要角色。而当前的图池化操作均以平等地位看待数据点,普遍未考虑利用邻域内数据之间的偏序关系,从而造成图结构信息破坏。针对此问题,本文提出一种基于偏序关系的多视图多粒度图表示学习框架(multi-view and multi-granularity graph representation learning based on partial order relationships,MVMGr-PO),它通过从节点特征视图、图结构视图以及全局视图对节点进行综合评分,进而基于节点之间的偏序关系进行下采样操作。相比于其他图表示学习方法,MVMGr-PO可以有效地提取多粒度图结构信息,从而可以更全面地表征图的内在结构和属性。此外,MVMGr-PO可以集成多种图神经网络架构,包括GCN(graph convolutional network)、GAT(graph attention network)以及GraphSAGE(graph sample and aggregate)等。通过在6个数据集上进行实验评估,与现有基线模型相比,MVMGr-PO在分类准确率上有明显提升。展开更多
Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance...Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.展开更多
Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of...Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.展开更多
文摘以旅游大数据为基础,考虑长时间范围内的滞后效应以及不同搜索强度指数(Search Intensity Index,SII)之间的多任务影响,提出一种基于大数据的多任务旅游信息分析(Multi-tasking Tourism Information Analysis Based on Big Data,MTIABD)框架。使用融合信息重排序技术预测旅游需求,具体根据图引导结构模拟历史变量对未来变量的滞后影响。每个变量通过时间维度上的卷积神经网络(Convolutional Neural Network,CNN)进行独立编码,利用二分图动态建模滞后效应,通过图聚合进行挖掘,实现对旅游需求的精准预测。基于上述技术,构建旅游需求预测系统,旅游者能够根据需求检索不同景点的信息。在真实数据集上进行大量实验,结果表明所提出的MTIABD框架在一步和多步预测方面均优于现有方法。在平均绝对百分比误差(Mean Absolute Percentage Error,MAPE)指标下,相较于基于实例的多变量时间序列图预测框架(Instance-wise Graph-rased Framework for Multivariate Time Series Forecasting,IGMTF),MTIABD在HK-2021数据集上的性能提高了16.75%,在MO-2021数据集上的性能提高了19.79%。
文摘图对比学习因其可有效缓解数据稀疏问题被广泛应用在推荐系统中.然而,目前大多数基于图对比学习的推荐算法均采用单一视角进行学习,这极大地限制了模型的泛化能力,且图卷积网络本身存在的过度平滑问题也影响着模型的稳定性.基于此,提出一种融合层注意力机制的多视角图对比学习推荐方法.一方面,该方法提出2种不同视角下的3种对比学习,在视图级视角下,通过对原始图添加随机噪声构建扰动增强视图,利用奇异值分解(singular value decomposition)重组构建SVD增强视图,对这2个增强视图进行视图级对比学习;在节点视角下,利用节点间的语义信息分别进行候选节点和候选结构邻居对比学习,并将3种对比学习辅助任务和推荐任务进行多任务学习优化,以提高节点嵌入的质量,从而提升模型的泛化能力.另一方面,在图卷积网络学习用户和项目的节点嵌入时,采用层注意力机制的方式聚合最终的节点嵌入,提高模型的高阶连通性,以缓解过度平滑问题.在4个公开数据集LastFM,Gowalla,Ifashion,Yelp上与10个经典模型进行对比,结果表明该方法在Recall,Precision,NDCG这3个指标上分别平均提升3.12%,3.22%,4.06%,这说明所提方法是有效的.
文摘图池化作为图神经网络中重要的组件,在获取图的多粒度信息的过程中扮演了重要角色。而当前的图池化操作均以平等地位看待数据点,普遍未考虑利用邻域内数据之间的偏序关系,从而造成图结构信息破坏。针对此问题,本文提出一种基于偏序关系的多视图多粒度图表示学习框架(multi-view and multi-granularity graph representation learning based on partial order relationships,MVMGr-PO),它通过从节点特征视图、图结构视图以及全局视图对节点进行综合评分,进而基于节点之间的偏序关系进行下采样操作。相比于其他图表示学习方法,MVMGr-PO可以有效地提取多粒度图结构信息,从而可以更全面地表征图的内在结构和属性。此外,MVMGr-PO可以集成多种图神经网络架构,包括GCN(graph convolutional network)、GAT(graph attention network)以及GraphSAGE(graph sample and aggregate)等。通过在6个数据集上进行实验评估,与现有基线模型相比,MVMGr-PO在分类准确率上有明显提升。
文摘顺序任务流(sequential task flow,STF)将对共享数据的访问表示为任务之间的依赖关系,STF运行时系统通过任务构造、依赖分析和任务依赖图(task dependence graph,TDG)生成、任务调度实现异步并行,这3个环节的开销直接影响并行程序的性能.目前以STF为核心的AceMesh运行时系统,在SW39000处理器上仅使用单主核构图、多从核执行的方式.然而,SW39000处理器离散访存性能较弱,细粒度任务构图离散访存增多,构图更容易成为瓶颈.对此,提出了一种利用多从核辅助主核进行构图的算法.首先,分析在依赖分析和TDG生成过程中的并行性,在SW39000处理器上实现了一种基于胖任务依赖图(fatTDG)的多核辅助并行构图算法PFBH(parallelized fatTDG building algorithm with helpers)并进行优化.其次,针对线程间的主存资源竞争问题,提出构图与执行并行中从核资源调节方法及参数选择.最终,在5类典型应用下进行实验测试.与单核串行构图系统相比,在细粒度任务场景下最高加速为1.75倍;与SW39000处理器上的OpenACC模型相比,AceMesh最高可达2倍加速.
基金supported by the National Natural Science Foundation of China (61104180)the National Basic Research Program of China(973 Program) (97361361)
文摘Satellite observation scheduling plays a significant role in improving the efficiency of satellite observation systems.Although many scheduling algorithms have been proposed,emergency tasks,characterized as importance and urgency（e.g.,observation tasks orienting to the earthquake area and military conflict area）,have not been taken into account yet.Therefore,it is crucial to investigate the satellite integrated scheduling methods,which focus on meeting the requirements of emergency tasks while maximizing the profit of common tasks.Firstly,a pretreatment approach is proposed,which eliminates conflicts among emergency tasks and allocates all tasks with a potential time-window to related orbits of satellites.Secondly,a mathematical model and an acyclic directed graph model are constructed.Thirdly,a hybrid ant colony optimization method mixed with iteration local search（ACO-ILS） is established to solve the problem.Moreover,to guarantee all solutions satisfying the emergency task requirement constraints,a constraint repair method is presented.Extensive experimental simulations show that the proposed integrated scheduling method is superior to two-phased scheduling methods,the performance of ACO-ILS is greatly improved in both evolution speed and solution quality by iteration local search,and ACO-ILS outperforms both genetic algorithm and simulated annealing algorithm.
文摘Heterogeneous computing is one effective method of high performance computing with many advantages. Task scheduling is a critical issue in heterogeneous environments as well as in homogeneous environments. A number of task scheduling algorithms for homogeneous environments have been proposed, whereas, a few for heterogeneous environments can be found in the literature. A novel task scheduling algorithm for heterogeneous environments, called the heterogeneous critical task （HCT） scheduling algorithm is presented. By means of the directed acyclic graph and the gantt graph, the HCT algorithm defines the critical task and the idle time slot. After determining the critical tasks of a given task, the HCT algorithm tentatively duplicates the critical tasks onto the processor that has the given task in the idle time slot, to reduce the start time of the given task. To compare the performance of the HCT algorithm with several recently proposed algorithms, a large set of randomly generated applications and the Gaussian elimination application are randomly generated. The experimental result has shown that the HCT algorithm outperforms the other algorithm.
