Sensor-based human activity recognition is an important problem in pervasive computing, which has attracted lots of attention from the research community in the past few years. The existing relevant studies focused on using handcrafted features or machine learning-based methods to tackle this problem. However, these methods are usually limited to specific datasets, such that the generality is limited. Some methods are also limited to strict experimental environments, which do not take stability into consideration. In this paper, we propose a robust and novel deep learning-based framework, named Snippet Consensus Neural Networks (SCNet), which aims to conquer these challenges. Through a series of experiments, the proposed framework is verified to outperform seven state-of-the-art methods on five datasets in terms of not only accuracy but also generality and stability, averagely improving 10% on mean accuracy.

This paper presents a motion-compensated deinterlacing algorithm featuring spectrum-adaptive interpolation of interlaced field. Using motion-compensated reference pictures, the proposed spectrum-adaptive filter tactically identifies the baseband via spectrum analysis and removes the replicas of interlaced sampling, making overall algorithm adapt to versatile video scene and different degree of motion compensation scenarios. The experimental results indicate that our proposed algorithm has better objective performance than other motion-compensated and non-motion-compensated algorithms do especially in complex moving textures. The subjective results also support the benefits of our spectrum-adaptive filter.

The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper, we present CHARM, an efficient algorithm for mining all frequent closed itemsets. It enumerates closed sets using a dual itemset-tidset search tree, using an efficient hybrid search that skips many levels. It also uses a technique called diffsets to reduce the memory footprint of intermediate computations. Finally, it uses a fast hash-based approach to remove any "nonclosed" sets found during computation. We also present CHARM-L, an algorithm that outputs the closed itemset lattice, which is very useful for rule generation and visualization. An extensive experimental evaluation on a number of real and synthetic databases shows that CHARM is a state-of-the-art algorithm that outperforms previous methods. Further, CHARM-L explicitly generates the frequent closed itemset lattice.