Mobile healthcare (mHealth) enables people to collect and share their personal health records (PHRs) and gain rapid medical treatment via mobile 5G-enabled Industrial Internet of Things (IIoT) devices, which also brings the challenge of keeping the PHRs confidentiality and preventing unauthorized access. By the emerging ciphertext-policy attribute-based encryption (CP-ABE), the PHR owner can encrypt his/her PHR data under self-defined access policies. However, existing CP-ABE schemes are suffering from either heavy computation cost and storage overhead or traitor tracing and direct revocation. In this article, we propose an efficient, traceable, and revocable access control scheme named TRAC for mHealth in 5G-enabled IIoT. In TRAC, the ciphertext is composed of the attribute-relevant ciphertext encrypted under an and -gate access structure and the identity-relevant ciphertext associated with some potential receivers. The malicious user who leaks his/her privilege to unauthorized entities will be precisely tracked and added in the revocation list, by which the cloud server can update the identity-relevant ciphertext by itself. The length of final ciphertext and the time of bilinear pairing operations used in decryption are constant. The security analysis and performance evaluation indicate the security, efficiency, and practicality of TRAC.

The rapid development of pay-as-you-go cloud services motivates the increasing number of cloud resource demands. However, the volatile demands bring new challenges for current techniques to minimize the cost of cloud capacity planning and VM provisioning while satisfying the customer demands. The service vendors will incur enormous revenue loss within the long-term inappropriate planning, especially when the demands fluctuate abruptly and frequently. In this paper, we cast the cloud capacity planning as a classification problem and propose an integrated framework, which effectively predicts the abrupt changing demands, to reduce the cost of cloud resource provisioning. In this framework, we first apply Piecewise Linear Representation to segment the time series of cloud resource demands for labeling the changing trend of each period. Second, Weighted SVM is leveraged to fit the statistical information and the label of each period and predict the changing trend of the following period. Finally, an incremental learning strategy is utilized to ensure the low cost of updating the model using the upcoming requests. We evaluate our framework on the IBM Smart Cloud Enterprise (SCE) trace data and the experimental results show the effectiveness of our proposed framework.

Deep Neural Networks (DNNs) are demonstrated to be vulnerable to adversarial examples, which are elaborately crafted to fool learning models. Since the accuracy and robustness of DNNs are at odds for the adversarial training method, the adversarial example detection algorithms check whether the specific example is adversarial, which is promising to solve the issue of the adversarial example. However, among the existing methods, model-aware detection methods do not generalize well, while the detection accuracies of the generative-based methods are lower compared to the model-aware methods. In this paper, we propose a cascade model-aware generative adversarial example detection method, namely CMAG. CMAG consists of two first-order reconstructors and a second-order reconstructor, which can illustrate what the model sees to the human by reconstructing the logit and feature maps of the last convolution layer. Experimental results demonstrate that our method is effective and is more interpretable compared to some state-of-the-art methods.

Power forecasting has a guiding effect on power-aware scheduling strategies to reduce unnecessary power consumption in data centers. Many metrics related to power consumption can be collected in physical servers, such as the status of CPU, memory, and other components. However, most existing methods empirically exploit a small number of metrics to forecast power consumption. To this end, this article uses feature selection based on causality to explore the metrics that strongly influence the power consumption of different tasks. Moreover, we propose a tensor-based dual-stage attention LSTM to forecast the non-linear and non-periodic power consumption. In the proposed model, a multi-way delay embedding transform is utilized to convert the time series into tensors along the temporal direction. The LSTM combines with the tensor technique and the attention mechanism to capture the temporal pattern effectively. In addition, we adopt the cost-sensitive loss function to optimize the specific power forecasting problem in data centers. The experimental results demonstrate that our method can achieve up to 1.4% to 4.3% forecasting accuracy improvement compared with the state-of-the-art models.

The rapid development of location-based social networks (LBSNs) produces the increasing number of check-in records and corresponding heterogeneous information which bring big challenges of points-of-interest (POIs) recommendation in our daily lives. The emergence of various recommender techniques bridges the gap between the numerous heterogeneous check-ins and the personalized POI recommendation. However, due to the differences between LBSNs and conventional recommendation tasks, besides the user feedback, the spatio-temporal information is also significant to precisely capture the user preferences. In this paper, we propose a multi-task learning model based POI recommender system which exploits a structure of generative adversarial networks (GAN) simultaneously considering temporal check-ins and geographical locations. The GAN-based model is capable of relieving the sparsity of check-in data in POI recommender systems. The temporal check-ins not only present the preference but also show the lifestyle of an individual while the geographical locations describe the active region of users which further filters POIs far from the feasible region. The multi-task learning strategy is capable of combining the information of temporal check-ins and geographical locations to improve the performance of personalized POI recommendation. We conduct the experiments on two real-world LBSNs datasets and the experimental results show the effectiveness of our proposed approach.