Deep multi-task learning attracts much attention in recent years as it achieves good performance many applications. Feature is important to deep for sharing common information among tasks. In this paper, we propose a Hierarchical Graph Neural Network (HGNN) learn augmented features learning. The HGNN consists of two-level graph neural networks. the low level, an intra-task network responsible p...

Knowledge graph embedding, which aims at learning the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge embedding methods focus on structural relationships in fixed triples while ignoring temporal information. Currently, existing time-aware only factual plausibility, smoothness, models interactions betwee...

Cross-domain graph few-shot learning attempts to address the prevalent data scarcity issue in mining problems. However, utilization of cross-domain induces another intractable domain shift which severely degrades generalization ability models. The combat with is hindered due coarse source domains and ignorance accessible prompts. To these challenges, this paper, we design a novel Task Coordinat...

We conducted an experiment to investigate whether spatial processing is used in graph comprehension tasks. Using an interference paradigm, we demonstrate that a graph task interfered more with performance on a spatial memory task than on a visual (non-spatial) memory task. Reaction times showed there was no speed-accuracy tradeoff. We conclude that it was the spatial nature of the graph task th...

This paper presents a generic technique for mapping parallel algorithms onto parallel architectures. The proposed technique is a fast recursive mapping algorithmwhich is a component of the Cluster-M programming tool. The other components of Cluster-M are the Speciication module and the Representation module. In the Speciication module, for a given task speciied by a high-level machine-independe...

Task assignment is one of the most challenging problems in distributed computing environment. An optimal task assignment guarantees minimum turnaround time for a given architecture. Several approaches of optimal task assignment have been proposed by various researchers ranging from graph partitioning based tools to heuristic graph matching. Using heuristic graph matching, it is often impossible...

Knowledge-intensive work plays an increasingly important role in organisations of all types. Knowledge workers contribute their effort to achieve a common purpose; they are part of (business) processes. Workflow Management Systems support them during their daily work, featuring guidance and providing intelligent resource delivery. However, the emergence of richly structured, heterogeneous datas...

Several approaches for handling the multi-processor scheduling problem have been presented over the last decades, such as scheduling techniques, clustering techniques and task merging techniques. However, the increasing gap between processor speed and communication speed demand more accurate models of parallel computation and efficient algorithms to work under the restrictions of these models. ...