Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

Effective extraction of human body parts and operated objects participating in action is the key issue of fine-grained action recognition. However, most of the existing methods require intensive manual annotation to train the detectors of these interaction components. In this paper, we represent videos by mid-level patches to avoid the manual annotation, where each patch corresponds to an action-related interaction component. In order to capture mid-level patches more exactly and rapidly, candidate motion regions are extracted by motion saliency. Firstly, the motion regions containing interaction components are segmented by a threshold adaptively calculated according to the saliency histogram of the motion saliency map. Secondly, we introduce a mid-level patch mining algorithm for interaction component detection, with object proposal generation and mid-level patch detection. The object proposal generation algorithm is used to obtain multi-granularity object proposals inspired by the idea of the Huffman algorithm. Based on these object proposals, the mid-level patch detectors are trained by K-means clustering and SVM. Finally, we build a fine-grained action recognition model using a graph structure to describe relationships between the mid-level patches. To recognize actions, the proposed model calculates the appearance and motion features of mid-level patches and the binary motion cooperation relationships between adjacent patches in the graph. Extensive experiments on the MPII cooking database demonstrate that the proposed method gains better results on fine-grained action recognition.