Implicit authentication is a new research direction to enhance the privacy protection of smartphones. However, implicit authentication has low robustness due to its vulnerability to environment. Motivated by this, this paper proposes a context-aware implicit authentication, which is a scheme to improve the robustness of authentication by introducing context awareness module. In the scheme, multi-sensor data (including accelerometer, gyroscope, magnetometer, timestamp, pressure, touch size) is first captured in a fine-grained manner to characterize one’s touch action. Then, based on various sensors data, gesture features and touch features are extracted by employing both statistical method and distance measurement method. Particularly, one’s body posture when touch action happens can be used as context. In each context, we present a weighted sum fusion rule to fuse the results of different features obtained by One-Class SVM (OC-SVM). We have collected 10000+ sensor data from 87 participants for experimental evaluation. The results show that the authentication in an unrestricted environment can achieve a best equal error rate (EER) of 0.0071%, which is more than one percent lower than the non-context-aware authentication. The proposed method can effectively improve the reliability and practicability of implicit authentication.

Next point-of-interest (POI) recommendation has been an important task for location-based intelligent services. However, the application of such promising technique is still limited due to the following three challenges: 1) the difficulty of capturing complicated spatiotemporal patterns of user movements; 2) the hardness of modeling fine-grained long-term preferences of users; and 3) the effective learning of interaction between long- and short-term preferences. Motivated by this, we propose a memory augmented hierarchical attention network (MAHAN), which considers both short-term check-in sequences and long-term memories. To capture the complicated interest tendencies of users within a short-term period, we design a spatiotemporal self-attention network (ST-SAN). For long-term preferences modeling, we employ a memory network to maintain fine-grained preferences of users and dynamically operate them based on users' constantly updated check-ins. Moreover, we first employ a coattention network/mechanism to integrate the proposed ST-SAN and memory network, which can fully learn the dynamic interaction between long- and short-term preferences. Our extensive experiments on two publicly available data sets demonstrate the effectiveness of MAHAN.

Estimating monocular depth and ego-motion via unsupervised learning has emerged as a promising approach in autonomous driving, mobile robots, and augmented reality (AR)/VR applications. It avoids intensive efforts to collect a large amount of ground truth and further improves the scene construction density and long-term tracking accuracy in simultaneous localization and mapping (SLAM) systems. However, existing approaches are susceptible to illumination variations and blurry pictures due to fast movements in real-world driving scenarios. In this article, we propose a novel unsupervised learning framework to fuse the complementary strength of visual and inertial measurements for monocular depth estimation. It learns both forward and backward inertial sequences at multiple subspaces to produce environment-independent and scale-consistent motion features and selectively weights inertial and visual modalities to adapt to various scenes and motion states. In addition, we explore a novel virtual stereo model to adopt such depth estimates in the monocular SLAM system, thus improving the system efficiency and accuracy. Extensive experiments on the KITTI, EuRoC, and TUM datasets have shown our effectiveness in terms of monocular depth estimation, SLAM initialization efficiency, and pose estimation accuracy compared with the state-of-the-art.

Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy, but they largely rely on GPS trajectories which are too coarse to model many personalized driving behaviors, e.g., differentiating novice and veteran drivers. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS trajectories, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a road link traffic database with topology representation, speed statistics, and query distribution. It also calibrates inertial readings, estimates the arbitrary phone’s pose in car, and detects multiple aggressive driving events (e.g., bump judders, sharp turns, sharp slopes, frequent lane shifts, overspeeds, and sudden brakes). Finally, we demonstrate our solution on two typical transportation problems, i.e., predicting traffic speed at holistic level and estimating customized travel time at personal level, within a multi-task learning structure. Experiments on two large-scale real-world traffic datasets from DiDi platform show our effectiveness compared with the state-of-the-art.

A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most existing losses used in RSIR are based on triplets, which have disadvantages of local optimization, slow convergence and insufficient use of similarity structure in a mini-batch. In this paper, we present a novel DML method named as global optimal structured loss to deal with the limitation of triplet loss. To be specific, we use a softmax function rather than a hinge function in our novel loss to realize global optimization. In addition, we present a novel optimal structured loss, which globally learn an efficient deep embedding space with mined informative sample pairs to force the positive pairs within a limitation and push the negative ones far away from a given boundary. We have conducted extensive experiments on four public remote sensing datasets and the results show that the proposed global optimal structured loss with pairs mining scheme achieves the state-of-the-art performance compared with the baselines.