To enable automatic disassembly of different product types with uncertain condition and degree wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed. Machine learning algorithms be employed due their generalization capabilities from various variants products. However, reality, datasets a diversity samples used train models difficult obt...

Training an object instance detector where only a few training images are available is challenging task. One solution cut-and-paste method that generates dataset by cutting areas out of and pasting them onto other background images. A trained on generated with suffers from the conventional domain shift problem, which stems discrepancy between source (generated dataset) target (real test dataset...

Music generation remains an attractive and interesting application of machine learning since it is typically characterized by human ingenuity and creativity. Moreover, given the high-dimensionality of time-series data, it is difficult to construct a model that has the representational power necessary to capture the timeand note-invariant patterns throughout a musical piece. In this paper, we de...

This paper introduces and describes a manually generated synchronization ground truth, accurate to the level of the audio sample, for the Jiku Mobile Video Dataset, a dataset containing hundreds of videos recorded by mobile users at different events with drama, dancing and singing performances. It aims at encouraging researchers to evaluate the performance of their audio, video, or multimodal s...

Biomedical research usually requires combining large volumes of data from multiple heterogeneous sources. Such heterogeneity makes difficult not only the generation of research-oriented dataset but also its exploitation. In recent years, the Open Data paradigm has proposed new ways for making data available in ways that sharing and integration are facilitated. Open Data approaches may pursue th...

Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining (SDM). Because of the complexity of spatial data, a traditional method in extracting spatial association rules is to transform spatial database into general transaction database. The Apriori algorithm is one of the most commonly used methods in mining association rules at present. But a sh...

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pas...