The recent development of cloud computing has empowered the Internet-based services, which enable users to gain a broad scope of access to their applications, such as Internet of Medical Things (IoMT). Considering the efficiency performance, the privacy protection is often kept at a lower level in order to ensure the application can offer a higher level performance. However, this mechanism also causes a serious concern of privacy hazards due to the information over collections operated by apps/applications. Addressing this privacy issue, this paper proposes an approach that is designed to provide high-level privacy protection without lowering down the efficiency in cloud/fog computing (especially in biological systems of IoMT). The proposed model is called fog-based access control model. The fine-grained data access control is combined with the implementation of fog computing in the proposed approach. Our simulation experiment has shown that our approach could offer high-level privacy protection within a shortened execution time, thus it can be useful for IoMT-based applications.

Crowd counting is an important task that serves as a preprocessing step in many applications. Despite obvious improvement reported by various convolutional-neural-network-based approaches, they only focus on the role of deep feature maps while neglecting the importance of shallow features for crowd counting. In order to surmount this issue, a dilated convolutional-neural-network-based cross-level contextual information extraction network is proposed in this work, which is abbreviated as CL-DCNN. Specifically, a dilated contextual module (DCM) is constructed by importing cross-level connection between different feature maps. It can effectively integrate contextual information while conserving the local details of crowd scenes. Extensive experiments show that the proposed approach outperforms state-of-the-art approaches using five public datasets, i.e., ShanghaiTech part A, ShanghaiTech part B, Mall, UCF_CC_50 and UCF-QNRF, achieving MAE 52.6, 8.1, 1.55, 181.8, and 96.4, respectively.

An effective and scalable metaheuristic algorithm termed Phase Based Optimization (PBO) for solving big optimization problems is proposed. In the natural system, the individuals with three phases which are gas phase, liquid phase and solid phase have completely different motional characteristics. PBO mimics the above three kinds of motional characteristics of individuals, and three corresponding operators, diffusion operator of gas individuals, flowing operator of liquid individuals and perturbation operator of solid individuals are devised. The diffusion operator and the flowing operator are utilized to perform the task of divergence and convergence respectively, and the perturbation operator plays a role of fine-tune search. Despite its algorithmic simplicity, PBO can effectively find a very better solution even in a high dimensional search space. The experimental results demonstrate that PBO can provide much better accuracy on optimized solutions and lower time complexity than the other state-of-the-art optimization algorithms.

Accurate cellular network traffic prediction is crucial for intelligent network planning and management in 6G. However, the non-stationary characteristics of cellular network traffic present significant challenges when training deep neural networks for traffic forecasting. To address this issue, we propose a two-stage deep learning framework, JO-DPNet, based on Bayesian joint parameter optimization, which integrates data decomposition techniques with Bayesian joint optimization to effectively mitigate the adverse impacts of non-stationarity and error accumulation on prediction accuracy. In the first stage, a data decomposition module uses Variational Mode Decomposition (VMD) to decompose the original data into network traffic subset series(TSS), thereby alleviating the negative effects of non-stationarity. In the second stage, a prediction and construction module leverages a bi-directional LSTM (Bi-LSTM) network to extract deep spatial-temporal features from the TSS in a bidirectional manner. A fully connected layer then captures the relationships between the TSS and reconstructs the predicted results into the final output. The JO module employs the Tree-structured Parzen Estimator based Bayesian optimization algorithm(TP-BO) simultaneously determines the optimal VMD mode number k and the hyperparameters of the Bi-LSTM network through probabilistic surrogate model. Extensive experiments on three real-world cellular traffic datasets demonstrate that the proposed method significantly mitigates the non-stationary characteristics of the traffic data. Compared to state-of-the-art methods, JO-DPNet achieves reductions in MAE by 29%, 3%, and 19% for three type prediction tasks on the Telecom Italia dataset. The source code is available to the public at: https://github.com/VicentZhang259/JO-DPNet.

Differential evolution (DE) is an evolutionary algorithm widely used to solve optimization problems with different characteristics in fields where actions and decisions depend on numerical data such as engineering, economics, and logistics. In this paper, an adaptive differential evolution mechanism with cooperative co-evolution and covariance (A-CC/COV-DE) is proposed to overcome the low efficiency of differential evolution when solving large-scale numerical optimization problems, especially when the correlation between the variables of the problem is unknown. An unknown correlation of variables hinders DE from achieving an optimal search process since different types of correlations ideally require distinct optimization strategies. According to the separability of variables, the appropriate evolutionary strategy is selected adaptively. For separable functions, cooperative coevolution is adopted. After using extended differential grouping to split the problem, the sub-components are optimized by differential evolution. This reduces the dimensionality and complexity of the problem, improving its convergence speed and global search ability. For non-separable functions, a covariance matrix is calculated, and then the eigenvector is used to rotate the coordinate system. This leads to eliminate the correlation between variables and improve the search efficiency of differential evolution. We evaluated the performance of A-CC/COV-DE on the CEC 2014 test suite and compared it with state-of-the-art differential evolution algorithms. The experimental results show that our proposal is quite competitive with recent algorithms.