Sensor-based human activity recognition is a fundamental research problem in ubiquitous computing, which uses the rich sensing data from multimodal embedded sensors such as accelerometer and gyroscope to infer human activities. The existing activity recognition approaches either rely on domain knowledge or fail to address the spatial-temporal dependencies of the sensing signals. In this paper, we propose a novel attention-based multimodal neural network model called AttnSense for multimodal human activity recognition. AttnSense introduce the framework of combining attention mechanism with a convolutional neural network (CNN) and a Gated Recurrent Units (GRU) network to capture the dependencies of sensing signals in both spatial and temporal domains, which shows advantages in prioritized sensor selection and improves the comprehensibility. Extensive experiments based on three public datasets show that AttnSense achieves a competitive performance in activity recognition compared with several state-of-the-art methods.

The popularity of smartphones has witnessed the rapid growth of the number of mobile applications. Nowadays, there are millions of applications available, and at the same time, many applications are already installed on people's smartphones. Installing numerous apps will cause some troubles in finding the specific apps promptly. Hence it is necessary to predict the next app(s) to be used in a short term and launching them as shortcuts, which will make the smartphone system more efficient and user-friendly. In this paper, we pay attention to two subproblems that are related to the app usage prediction. One is the T app prediction problem that focuses on predicting a set of apps that will be used in a time interval. The other is the Top-K app recommendation problem that focuses on recommending the K most probable APPs to be used next. In order to solve these problems, we propose a generic prediction model based on Long Short-term Memory (LSTM), which is an enhancement of the recurrent neural network (RNN) model. The proposed model converts the temporal-sequence dependency and contextual information into a unified feature representation for next app prediction. We implement the model in the Android platform. Extensive experiments based on real collected dataset demonstrate that the proposed LSTM model outperforms the baselines for app usage prediction, and achieves high accuracy for app recommendation.

High relative permittivity, ε r , over a very wide temperature range, -65°C to 325°C, is presented for ceramics designed to be compatible with base metal electrode multilayer capacitor manufacturing processes. We report a ≥ 300°C potential Class II capacitor material free from Bi or Pb ions, developed by doping Sr 2 NaNb 5 O 15 with Ca 2+ ,Y 3+ and Zr 4+ ions, according to the formulation Sr 2-2z Ca z Y z NaNb 5-z Zr z O 15 . For sample composition z = 0.025, ε r values are 1565 ± 15 % (1 kHz) from -65°C to 325°C. At a slightly higher level of doping, z = 0.05, ε r values are 1310 ± 10 % from -65°C to 300°C. Values of the dielectric loss tangent, tanδ are ≤ 0.025 from −60°C to 290°C, for z = 0.025, with tanδ increasing to 0.035 at 325°C. Microstructural analyses exclude core-shell mechanisms being responsible for the flattening of the ε r -T response.

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