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Recently, more and more mobile apps are employed in the marketing field with technical advances. Mobile marketing apps have become a prevalent way for enterprise marketing. Therefore, it has been an important and urgent problem to provide personalized and accurate recommendation in mobile marketing, with a large number of items and limited capability of mobile devices. Recommendation have been investigated widely, however, most existing approaches fail to consider the stability or change of users' behaviors over time. In this paper, we first propose to mine the periodic trends of users' consuming behavior from historical records by KNN(K-nearest neighbor) and SVR (support vector regression) based time series prediction, and predict the next time when a user re-purchases the item, so that we can recommend the items which users have purchased before at proper time. Second, we aim to find the regularity of users' purchasing behavior during different life stages and recommend the new items that are needed and proper for their current life stage. In order to solve this, we mine the mapping model from items to user's life stage first. Based on the model, users' current life stage can be estimated from their recent behaviors. Finally, users will be recommended with new items which are proper to their estimated life stage. Experimental results show that it has improved the effectiveness of recommendation obviously by mining users' consuming behaviors with temporal evolution. [ABSTRACT FROM AUTHOR]

Predicting urban traffic volume is of great significance to traffic management and urban construction. An accurate prediction model can help drivers optimize driving routes, allocate resources reasonably and reduce urban traffic congestion. Most of the existing studies do not consider the complex nonlinear spatio-temporal relationship. In the spatial dimension, they do not consider the impact of regional semantics and regional interactions. In the temporal dimension, they ignore the impact of long-term historical information and key time points. Aiming at the complexity of traffic data, in this paper, we design a ResNet-TCN model to predict the urban traffic volume. Firstly, we construct and extract features from the vehicle GPS tracking and external information, such as velocity, time, location and weather. Then, we obtain regional semantic information by the ResNet model and combine the weights of the regional division with the average vehicle velocity into a two-channel matrix. We extract the key features of the matrix sequence and predict the velocity by the TCN model. Finally, we estimate the traffic volume through a traffic volume inference model in the traffic field. We conduct a large number of experiments on the actual dataset of Chengdu and compare our model with the existing models. The experimental results show that our method has better performance on prediction accuracy. [ABSTRACT FROM AUTHOR]

Along with the popularity of intelligent services and mobile services, service recommendation has become a key task, especially the task based on quality-of-service (QoS) in edge computing environment. Most existing service recommendation methods have some serious defects, and cannot be directly adopted in edge computing environment. For example, most of existing methods cannot learn deep features of users or services, but in edge computing environment, there are a variety of devices with different configurations and different functions, and it is necessary to learn deep features behind those complex devices. In order to fully utilize hidden features, this paper proposes a new matrix factorization (MF) model with deep features learning, which integrates a convolutional neural network (CNN). The proposed mode is named Joint CNN-MF (JCM). JCM is capable of using the learned deep latent features of neighbors to infer the features of a user or a service. Meanwhile, to improve the accuracy of neighbors selection, the proposed model contains a novel similarity computation method. CNN learns the neighbors features, forms a feature matrix and infers the features of the target user or target service. We conducted experiments on a real-world service dataset under a batch of cases of data densities, to reflect the complex invocation cases in edge computing environment. The experimental results verify that compared to counterpart methods, our method can consistently achieve higher QoS prediction results. [ABSTRACT FROM AUTHOR]

The first paper titled "Intelligent Semantic Annotation for Mobile Services for IoT Computing from Heterogeneous Data" constructs a heterogeneous information network for service data, and proposes a new model named GoT, which fully utilizes the structural and semantic information. The AI-based Data Intelligent for IoT Computing includes a bunch of emerging techniques in Artificial Intelligence for processing IoT data, such as machine learning algorithms design, collaborative network modelling, system performance analysis and parallel computing architecture application, as well as optimization algorithms in different computing layers. The fourth paper titled "Applying Probabilistic Model Checking to Path Planning for a Smart Multimodal Transportation System Using IoT Sensor Data" proposes probabilistic model checking based path planning for a multimodal transportation system. [Extracted from the article]

According to the features of the AICE protocol, the authors select and extend the SVO logic to conduct the formal analysis of the protocol from the perspective of theoretical analysis of modal logic. The authors give the information flows of the AICE protocol by combining the privacy-preserving authentication (PPA) protocol and integrity checking protocol for MEC (ICE) together. In recent years, mobile networks and wireless networks have experienced explosive growth. [Extracted from the article]

Specifically, partitioning is performed by the improved monarch butterfly optimization algorithm and clustering is selected by computing the importance rate. The fifth article is " I Service Function Chain Placement for Joint Cost and Latency Optimization i " formulates a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. And then the paper solves the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. [Extracted from the article]

With the rapid development of mobile devices, more and more individuals and enterprises move their applications into Apps and enjoy high motility services. They address the performance-variation-aware workflows scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) VM Selection strategy. A real-time multi-feature anomaly detection algorithm based on Non-extensive entropy and Renyi cross entropy is proposed, and a Hidden Markov based state prediction model and a mimic transformation strategy for The Network Topology Mimic Association Graph based on predicted states is proposed. [Extracted from the article]