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Physical activity recognition using wearable sensors has gained significant interest from researchers working in the field of ambient intelligence and human behavior analysis. The problem of multi-class classification is an important issue in the applications which naturally has more than two classes. A well-known strategy to convert a multi-class classification problem into binary sub-problems is the error-correcting output coding (ECOC) method. Since existing methods use a single classifier with ECOC without considering the dependency among multiple classifiers, it often fails to generalize the performance and parameters in a real-life application, where different numbers of devices, sensors and sampling rates are used. To address this problem, we propose a unique hierarchical classification model based on the combination of two base binary classifiers using selective learning of slacked hierarchy and integrating the training of binary classifiers into a unified objective function. Our method maps the multi-class classification problem to multi-level classification. A multi-tier voting scheme has been introduced to provide a final classification label at each level of the solicited model. The proposed method is evaluated on two publicly available datasets and compared with independent base classifiers. Furthermore, it has also been tested on real-life sensor readings for 3 different subjects to recognize four activities i.e. Walking, Standing, Jogging and Sitting. The presented method uses same hierarchical levels and parameters to achieve better performance on all three datasets having different number of devices, sensors and sampling rates. The average accuracies on publicly available dataset and real-life sensor readings were recorded to be 95% and 85%, respectively. The experimental results validate the effectiveness and generality of the proposed method in terms of performance and parameters. [ABSTRACT FROM AUTHOR]

One problem of process discovery is the duplicated task, a task that appears multiple times in one process model. This paper presents “Slice and Connect,” an automated tri-dimensional process discovery technique considering duplicated task. It adds the notion of pool as a group of tasks that belongs to a single entity (e.g. department, organization, country). The algorithm entails two steps: first, the event log is sliced according to the pool attribute, and then the pool dependency network (PDN) is discovered. Second, the inter-pool dependency is calculated to connect the PDNs, the result of which is a process model represented as an integrated dependency network (IDN). Duplicated task is only allowed within a different pool to clearly separate the activity context in IDN. The complexity of the process model thereby is increased; therefore, the comprehensibility of the process model is also increased. [ABSTRACT FROM AUTHOR]

• A graph convolution model for skeleton-based action recognition is proposed. • Normalized Laplacian Matrix is utilized to encode the graph information. • An attention-based feature aggregation is proposed to extract the salient features. • The proposed method achieves better results than the baseline models. Skeleton-based human action recognition has attracted a lot of attention due to its capability and potential to provide more information than just using the sequence of RGB images. The use of Graph Convolutional Neural Network (GCN) becomes more popular since it can model the human skeleton very well. However, the existing GCN architectures ignore the different levels of importance on each hop during the feature aggregation and use the final hop information for further calculation, resulting in considerable information loss. Besides, they use the standard Laplacian or adjacency matrix to encode the property of a graph into a set of vectors which has a limitation in terms of graph invariants. In this work, we propose a Sequential Inter-hop Graph Convolution Neural Network (SIhGCN) which can capture salient graph information from every single hop rather than the final hop only and our work utilizes the normalized Laplacian matrix which provides better representation since it relates well to graph invariants. The proposed method is validated on two large datasets, NTU-RBG + D and Kinetics, to demonstrate the superiority of our proposed method. [ABSTRACT FROM AUTHOR]