High-dimensional imbalanced data presents the problems of massive invalid features and class imbalance, making it arduous for classifiers to gain respectable outcomes. Compared to the individual classifier, classifier ensemble has great potential to elevate the performance. In this paper, an adaptive progressive optimization ensemble approach (APOEA) is designed for high-dimensional imbalanced data classification. First, a local subview optimization (LSO) is designed, it enables each ensemble member to focus on learning information from different local regions, which helps to achieve dimensionality reduction while maintaining diversity. Then, an adaptive critical subview optimization (ACSO) is developed for supplemental learning, which can adaptively identify the suitable critical regions and execute critical subview optimization. Finally, based on the critical subview, APOEA implements an over-sampling scheme to mitigate the effect of class imbalance for base classifier. Experimental results demonstrate that our APOEA outperforms other mainstream imbalanced learning algorithms.
Yuyang Deng, Yu-Hong Xu, Pei-Jie Huang et al.· IEEE Transactions on Knowled...· 0 citations
Interactive services typically over-provision CPU resources to meet Service Level Objectives (SLOs) for tail latency amidst workload fluctuations. This inefficiency motivates emerging research into workload co-location, where batch jobs are hosted alongside interactive services to harvest underutilized resources. However, the Linux Completely Fair Scheduler (CFS) limits potential resource efficiency gains. CFS’s fairness-oriented design lacks support for workload-specific scheduling policies and is unable to simultaneously enforce performance isolation while facilitating fine-grained resource sharing across co-located workloads. Therefore, we present G1Stack, a scheduler framework designed for workload co-location. Specifically, G1Stack incorporates: (1) A parallel dual-policy architecture that schedules latency-critical (LC) tasks from interactive services and best-effort (BE) tasks from batch jobs separately, guaranteeing responsiveness for the former while enhancing computational throughput for the latter; (2) A learning-assisted load-balancing approach that dynamically interleaves LC and BE tasks across cores with fine-grained temporal and spatial distribution to optimize resource efficiency; (3) Integrated workload-aware auto-scaling and load-shedding mechanisms to minimize latency during up-scaling under load spikes, ensuring system responsiveness under dynamic workloads. Stress-testing results demonstrate that G1Stack reduces the non-productive resource ratio to at most 7.55% and shortens the average completion time of co-located batch jobs by up to 62.45% compared to baselines.
Di-Shi Xu, Fagui Liu, Bin Wang et al.· IEEE transactions on compute...· 0 citations
Continual learning (CL) is a key paradigm that enables intelligent agents to operate autonomously in edge networks over the long term. However, continuous model updates can lead to catastrophic forgetting and representation instability in edge deployment scenarios, which may further induce Decision Boundary Drift (DBD). We propose a DBD-based adversarial attack framework that exploits class-level drift modeling and leverages the deformation of decision boundaries caused by incremental updates. We introduce multiple statistical metrics to quantify boundary drift, based on which class-level adversarial perturbations are constructed and further optimized in the input space to generate effective adversarial examples. Extensive experiments on multiple datasets and continual learning models demonstrate that the proposed method can significantly degrade model robustness, revealing non-negligible security risks in continuously evolving learning systems. Inspired by the security and trustworthiness requirements of edge intelligent agents, we systematically study and quantify DBD and its associated security risks in continual learning. Our findings reveal a practical yet underestimated attack surface and provide a foundation for future research on secure and robust continual learning systems.
Kaixiang Yang, Yue-Bin Xu, Zhi-Hao Li et al.· IEEE Transactions on Network...· 0 citations
A role-of-learning taxonomy is proposed that categorizes existing methods according to how learning participates in the planning pipeline, including direct policy learning, learning-augmented classical planning, hybrid planning, and training enhancement methods.
Zong-Yuan Shen, Shalabh Gupta, Shan-Cheng Zhao et al.· 1 citation
A summarization pipeline around Global Entity Unification and Robust Importance Scoring is built, but unlike earlier efforts, each object is traced across frames and attached consistent identifiers to it, and Fragmented, isolated descriptions become a single, object-aware text corpus that unifies the storyline.
Donglei Chen, Shaoyu Huang, Xuemiao Xu et al.· The Visual Computer· 0 citations
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