In partial label data, the ground-truth label of a training example is concealed in a set of candidate labels associated with the instance. As the ground-truth label is inaccessible, it is difficult to train the classifier via the label information. Consequently, manifold structure information is adopted, which is under the assumption that neighbor/similar instances in the feature space have similar labels in the label space. However, the real-world data may not fully satisfy this assumption. In this paper, a partial label metric learning method based on likelihood-ratio test is proposed to make partial label data satisfy the manifold assumption. Moreover, the proposed method needs no objective function and treats the data pairs asymmetrically. The experimental results on several real-world PLL datasets indicate that the proposed method outperforms the existing partial label metric learning methods in terms of classification accuracy and disambiguation accuracy while costs less time.

Software-defined networking (SDN) has rapidly emerged as a promising new technology for future networks and gained considerable attention from both academia and industry. However, due to the separation between the control plane and the data plane, the SDN controller can easily become the target of denial-of service (DoS) attacks. To mitigate DoS attacks in OpenFlow networks, our solution, MinDoS, contains two key techniques/modules: the simplified DoS detection module and the priority manager. The proposed architecture sends requests into multiple buffer queues with different priorities and then schedules the processing of these flow requests to ensure better controller protection. The results show that MinDoS is effective and adds only minor overhead to the entire SDN/OpenFlow infrastructure.