In the area of image recognition, the quality of image label data has a significant impact on the performance of classification models. Therefore, manual annotation has been used as a means to label images. However, manual annotation is laborious and time-consuming and can introduce additional noise. To address these issues, this paper investigates an automatic algorithm for improving a cat breed classification model based on meta loss correction. The proposed algorithm leverages web crawling techniques to obtain unlabeled images of cats, filters them through object recognition, and selects only images containing cats. These images are then fed into the algorithm, which utilizes a pretrained initial model to generate pseudo-labels. These pseudo-labeled data are subsequently refined using a meta loss function, correcting the inaccuracies associated with the pseudo-labels. Finally, the labeled new data is merged with the original dataset, gradually increasing both the quantity and quality of the dataset. Experimental results demonstrate that as the merged dataset expands, the model's error decreases gradually, and its performance improves.
Purpose This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian. Design/methodology/approach This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content. Findings Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer. Originality/value The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.
Mobile crowdsourcing (MCS) can solve problems that are difficult for computers to solve accurately or efficiently. Current crowdsourcing workers face the challenges of overload, task recommendation is presented to deal with the above issue. The existing MCS task recommendation methods only consider the workers themselves, without taking the potential relationships between workers and workers and tasks, which may fail to grasp the long-term behavioral preferences of workers and cannot solve the cold start problem efficiently. To solve the above problems, this paper proposes a task recommendation method based on heterogeneous graph link prediction. First, we try to obtain the connection relationships between crowdsourcing tasks and workers and build a worker-task heterogeneous graph accordingly. Then, majority voting and DeepWalk are used to generate initial node features of workers. Second, we design two efficient message passing mechanisms to aggregate and update node features between task and worker nodes to explore the potential relationships between nodes. Finally, we obtain attention weights among nodes and use Bi-GRU to capture the long-term behavioral preferences of workers and recommend appropriate tasks to workers based on the similarity between workers and tasks to improve perceptual quality. Evaluations are conducted on seven real datasets. Experimental results show that our method is superior to the state-of-the-art baselines.
Microservice workflows are widely used in real-time mobile computing scenarios such as face recognition and speech recognition. The key challenge is to develop efficient, stable, and robust algorithms capable of handling uncertain and fuzzy workflow tasks. In this paper, we consider the real-time microservice workflow fuzzy scheduling problem under VM-Container two-tier resources. A novel model based on triangle fuzzy numbers is formulated. The model encompasses two metrics: cost and degree of satisfaction. In response, this paper proposes an ARIMA prediction-based workflow fuzzy scheduling method (PFSM), comprising five key components: prediction of workflow arrival number, predistribution of task sub-deadlines, ordering of the task pool, task scheduling strategy, and resource management. To assess the performance of the proposed algorithms, several comparison algorithms are selected for analysis, and their performance differences are evaluated using ANOVA. The experimental results demonstrate the significant superiority of the proposed algorithms over the other compared algorithms in terms of overall performance.
Knowledge tracing is the fundamental technology for constructing learner models that dynamically estimate and predict a learner's knowledge state. While current research on knowledge tracing has improved the predictive capacity of the model by investigating the relationship between learners and problem concepts, these models become static after training. This limits their ability to adapt to the varying developmental stages of learners due to human diversity. Drawing inspiration from the synaptic plasticity that grants lifelong learning capabilities to the biological brain, this study incorporates plasticity weights into the Transformer architecture. This leads to the proposal of a deep knowledge tracing model designed to adapt to individual learner development. Moreover, it demonstrates significant performance improvements compared to the baseline model.
