The popularity of convolutional architecture has made sensor-based human activity recognition (HAR) become one primary beneficiary. By simply superimposing multiple convolution layers, the local features can be effectively captured from multi-channel time series sensor data, which could output high-performance activity prediction results. On the other hand, recent years have witnessed great success of Transformer model, which uses powerful self-attention mechanism to handle long-range sequence modeling tasks, hence avoiding the shortcoming of local feature representations caused by convolutional neural networks (CNNs). In this paper, we seek to combine the merits of CNN and Transformer to model multi-channel time series sensor data, which might provide compelling recognition performance with fewer parameters and FLOPs based on lightweight wearable devices. To this end, we propose a new Dual-branch Interactive Network (DIN) that inherits the advantages from both CNN and Transformer to handle multi-channel time series for HAR. Specifically, the proposed framework utilizes two-stream architecture to disentangle local and global features by performing conv-embedding and patch-embedding, where a co-attention mechanism is used to adaptively fuse global-to-local and local-to-global feature representations. We perform extensive experiments on three mainstream HAR benchmark datasets including PAMAP2, WISDM, and OPPORTUNITY, which verify that our method consistently outperforms several state-of-the-art baselines, reaching an F1-score of 92.05%, 98.17%, and 91.55% respectively with fewer parameters and FLOPs. In addition, the practical execution time is validated on an embedded Raspberry Pi P3 system, which demonstrates that our approach is adequately efficient for real-time HAR implementations and deserves as a better alternative in ubiquitous HAR computing scenario. Our model code will be released soon.

With the popularity and development of the wearable devices such as smartphones, human activity recognition (HAR) based on sensors has become as a key research area in human computer interaction and ubiquitous computing. The emergence of deep learning leads to a recent shift in the research of HAR, which requires massive strictly labeled data in supervised learning scenario. In comparison with video data, activity data recorded from accelerometer or gyroscope are often more difficult to interpret and segment. Recently, several attention mechanisms are proposed to handle the weakly labeled human activity data, which do not require accurate data annotation. However, these attention-based models can only handle the weakly labeled dataset whose sample includes one target activity, as a result it limits efficiency and practicality. In the article, we propose a recurrent attention networks (RAN) to handle sequential weakly labeled multiactivity recognition and location tasks. The model can repeatedly perform steps of attention on multiple activities of one sample and each step is corresponding to the current focused activity. The effectiveness of the RAN model is validated on a collected sequential weakly labeled multiactivity dataset and the other two public datasets. The experiment results show that our RAN model can simultaneously infer multiactivity types from the coarse-grained sequential weak labels and determine specific locations of every target activity with only knowledge of which types of activities contained in the long sequence. It will greatly reduce the burden of manual labeling.1

Recently, the state-of-the-art performance in various sensor based human activity recognition (HAR) tasks have been acquired by deep learning, which can extract automatically features from raw data. In order to obtain the best accuracy, many static layers have been always used to train deep neural networks, and their weight connectivity in network remains unchanged. Pursuing the best accuracy in mobile platforms with a very limited computational budget at millions of FLOPs is impractical. In this paper, we make use of shallow convolutional neural networks (CNNs) with channel-selectivity for the use of HAR. As we have known, it is for the first time to adopt channel-selectivity CNN for sensor based HAR tasks. We perform extensive experiments on 5 public benchmark HAR datasets consisting of UCI-HAR dataset, OPPORTUNITY dataset, UniMib-SHAR dataset, WISDM dataset, and PAMAP2 dataset. As a result, the channel-selectivity can achieve lower test errors than static layers. The existing performance of deep HAR can be further improved by the CNN with channel-selectivity without any extra cost.

In this paper we propose a two-stage algorithm for robust K-subspaces recovery. In the first stage, a large number of local candidate subspaces are generated by probabilistic farthest insertion, and then the initial near-optimal K-subspaces are solved by combinatorial selection with randomized greedy method. In the second stage, the K-subspaces are further refined by assigning each data vector to the closest subspace and taking proper gradient steps along the geodesic of Grassmannian to update the subspace. Numerical experiments show that our two-stage algorithm can robustly recover K-subspaces even with large fraction of outliers. Experiments on the well-known Hopkins 155 dataset of motion segmentation show that the proposed approach significantly outperforms the baseline LBF algorithm, and can compete with state of the art subspace clustering algorithms with less running time.

In this study, the authors present GASG21 (Grassmannian adaptive stochastic gradient for L 2,1 norm minimisation), an adaptive stochastic gradient algorithm to robustly recover the low‐rank subspace from a large matrix. In the presence of column outliers corruption, the authors reformulate the classical matrix L 2,1 norm minimisation problem as its stochastic programming counterpart. For each observed data vector, the low‐rank subspace is updated by taking a gradient step along the geodesic of Grassmannian. In order to accelerate the convergence rate of the stochastic gradient method, the authors choose to adaptively tune the constant step‐size by leveraging the consecutive gradients. Numerical experiments on synthetic data and the extended Yale face dataset demonstrate the efficiency and accuracy of the proposed GASG21 algorithm even with heavy column outliers corruption.

It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems that utilize low-dimensional subspaces, demonstrating that subsampling can improve computation speed while still allowing for accurate subspace learning. We present GRASTA, Grassmannian Robust Adaptive Subspace Tracking Algorithm, an online algorithm for robust subspace estimation from randomly subsampled data. We consider the specific application of background and foreground separation in video, and we assess GRASTA on separation accuracy and computation time. In one benchmark video example [16], GRASTA achieves a separation rate of 46.3 frames per second, even when run in MATLAB on a personal laptop.

Abstract In this article, we propose to apply the topic model and topic‐level eigenfactor (TEF) algorithm to assess the relative importance of academic entities including articles, authors, journals, and conferences. Scientific impact is measured by the biased PageRank score toward topics created by the latent topic model. The TEF metric considers the impact of an academic entity in multiple granular views as well as in a global view. Experiments on a computational linguistics corpus show that the method is a useful and promising measure to assess scientific impact.

Modeling hyperspectral imagery via nonlinear manifold learning approaches can successfully capture the intrinsic geometries of the underlying complex high-dimensional data, which gives the state-of-the-art hyperspectral imagery representation. In this letter, we demonstrate that diffusion geometric coordinates can also represent hyperspectral imagery in a concise way and reveal much more significant structures than traditional linear methods. This diffusion framework tries to form a diffusion operator on the investigated hyperspectral imagery which simulates Markov random walk on the constructed affinity graph. The diffusion geometric coordinates derived from diffusion maps of the hyperspectral data incorporate the intrinsic geometries well where much more details about species-level spatial distributions are revealed in our experiments which show better classification results than principle component analysis (PCA). For$10^{5}$–$10^{6}$or even larger imagery, by exploiting the backbone approach, the computation complexity and memory requirement of the full-scene computation and representation are tractable, which shows the potential significant usefulness in the hyperspectral remote sensing field.