Federated Learning (FL) has recently attracted great interest in sensor-based human activity recognition (HAR) tasks. However, in real-world environment, sensor data on devices is non-independently and identically distributed (Non-IID), e.g., activity data recorded by most devices is sparse, and sensor data distribution for each client may be inconsistent. As a result, the traditional FL methods in the heterogeneous environment may incur a drifted global model that causes slow convergence and a heavy communication burden. Although some FL methods are gradually being applied to HAR, they are designed for overly ideal scenarios and do not address such Non-IID problem in the real-world setting. It is still a question whether they can be applied to cross-device FL. To tackle this challenge, we propose ProtoHAR, a prototype-guided FL framework for HAR, which aims to decouple the representation and classifier in the heterogeneous FL setting efficiently. It leverages the global prototype to correct the activity feature representation to make the prototype knowledge flow among clients without leaking privacy while solving a better classifier to avoid excessive drift of the local model in personalized training. Extensive experiments are conducted on four publicly available datasets: USC-HAD, UNIMIB-SHAR, PAMAP2, and HARBOX, which are collected in both controlled environments and real-world scenarios. The results show that compared with the state-of-the-art FL algorithms, ProtoHAR achieves the best performance and faster convergence speed in HAR datasets.

In recent years, various collaborative QoS prediction methods have been put forward to coping with the demand for efficient quality-of-service (QoS) evaluation, by drawing lessons from the recommender systems. However, there still remain some challenging issues on this direction, as how to effectively exploit complex contexts to improve prediction accuracy, and how to realize collaborative QoS prediction of multiple attributes. Inspired by the principles of deep learning, we have proposed a universal deep neural model (DNM) for making multiple attributes QoS prediction with contexts. In this model, contextual features are mapped into a shared latent space to semantically characterize them in the embedding layer. The contextual features with their higher-order interactions are captured through the interaction layer and the perception layers. Multi-tasks prediction is realized by stacking task-specific perception layers on the shared neural layers. Armed with these, DNM provides a powerful framework to integrate with various contextual features to realize multi-attributes QoS prediction. Experimental results from a large-scale QoS-specific dataset demonstrate that DNM achieves superior prediction accuracy in term of mean absolute error (MAE) compared with the state-of-the-art collaborative QoS prediction techniques. Additionally, the DNM model has a good robustness and extensibility on exploiting heterogeneous contextual features.

Recently, deep learning has represented an important research trend in human activity recognition (HAR). In particular, deep convolutional neural networks (CNNs) have achieved state-of-the-art performance on various HAR datasets. For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations. A high number of operations in deep leaning increases computational cost and is not suitable for real-time HAR using mobile and wearable sensors. Though shallow learning techniques often are lightweight, they could not achieve good performance. Therefore, deep learning methods that can balance the trade-off between accuracy and computation cost is highly needed, which to our knowledge has seldom been researched. In this paper, we for the first time propose a computation efficient CNN using conditionally parametrized convolution for real-time HAR on mobile and wearable devices. We evaluate the proposed method on four public benchmark HAR datasets consisting of WISDM dataset, PAMAP2 dataset, UNIMIB-SHAR dataset, and OPPORTUNITY dataset, achieving state-of-the-art accuracy without compromising computation cost. Various ablation experiments are performed to show how such a network with large capacity is clearly preferable to baseline while requiring a similar amount of operations. The method can be used as a drop-in replacement for the existing deep HAR architectures and easily deployed onto mobile and wearable devices for real-time HAR applications.

As the number of Web APIs ever increases, choosing the appropriate APIs for mashup creations becomes more difficult. To tackle this problem, various methods have been proposed to recommend APIs to match requirements of mashups and achieved much success. However, there existed some challenges with feature fusion and utilization, textual requirement understanding, utilization of Mashup categories and compatibility evaluation. Therefore, we propose a neural framework (MTFM) based on multi-model fusion and multi-task learning for Mashup-oriented Web API recommendation. MTFM exploits a semantic component to generate representations of requirements and introduces a feature interaction component to model the feature interaction between mashups and Web APIs. Output features of both components are further fused to predict the candidate APIs, and this enables us to have both the advantages of content-based and collaborative filtering methods. We further introduce mashup category judgment as an auxiliary task, where both tasks are viewed as a multi-label learning problem and jointly optimized with multi-task learning. Also, we have extended MTFM to MTFM++ to take advantage of the metadata and quality features of APIs, and proposed a metric for compatibility evaluation. Experimental results on the ProgrammableWeb dataset show that our methods outperform most popular state-of-the-art methods.