In biomedical data, the imbalanced data problem occurs frequently and causes poor prediction performance for minority classes. It is because the trained classifiers are mostly derived from the majority class. In this paper, we describe an ensemble learning method combined with active example selection to resolve the imbalanced data problem. Our method consists of three key components: 1) an active example selection algorithm to choose informative examples for training the classifier, 2) an ensemble learning method to combine variations of classifiers derived by active example selection, and 3) an incremental learning scheme to speed up the iterative training procedure for active example selection. We evaluate the method on six real-world imbalanced data sets in biomedical domains, showing that the proposed method outperforms both the random under sampling and the ensemble with under sampling methods. Compared to other approaches to solving the imbalanced data problem, our method excels by 0.03-0.15 points in AUC measure.

As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.

Cloud computing and mobile edge computing techniques supply efficient ways to solve the contradiction between the increasing computing and storage demands of portable terminals and the limited capacity. In this paper, we conduct a three-tier hierarchical service system with multiple UEs, multiple MECs, and a single cloud center. It's worth noting that multiple UEs with personalized options generate a large number of different tasks in real time. To deal with this offloading problem, a response ratio offloading strategy (RROS) centered on user preference and real-time nature is designed to make MECs or CC serve as many UEs as possible. Therefore, a MEC-choosing preference list of each UE is created based on its past experiences at first. Then, each MEC iteratively sorts UEs with its ranking in the UEs' preference list. In order to avoid that the first task arriving at MEC occupies too many resources of MEC and cannot achieve global optimization, we also adopt loop iterative sequencing for multiple tasks arriving within a stipulated time. Lastly, by comparing the optimal response ratio on different MECs and CC, multiple MECs and the CC collaborative offload computing tasks of multiple UEs. Experimental results show that the algorithm significantly outperforms conventional techniques.