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Due to the viewing convenience for social media users' fragmented time, the short video has become a new carrier for users' network demands on information spread, news reading, social contact, entertainment, and leisure. Therefore, short video recommendation is one of the most important research topics in social media. Current short video recommendation algorithms mainly focus on detecting user's social attributes, developing cross-domain information and so on, few researchers combine video category information and multi-behavior information together. This paper proposes a content-based recommendation algorithm Category-aided Multi-channel Bayesian Personalized Ranking (CMBPR) for short video recommendation, which integrates users' rich preference information by considering the difference among both different video categories and different user interactions. The experimental results demonstrate the effectiveness of the CMBPR video recommendation algorithm, which achieves a significantly higher recommendation accuracy than the traditional video recommendation algorithms and solves the influence of the “Long Tail” effect.

Fog computing is an epitome to enable provisioning resources and services beyond the cloud, at the edge of the network, and nearer to end devices. Fog computing is not a counterfeit for cloud computing but a persuasive counterpart. However, the mobility of end users and/or fog nodes brings a major dilemma in the implementation of real-life scenarios. Therefore, we deliver the state-of-the-art research about mobility in fog computing. By identifying the mobility problems, requirements, and features of different proposals, we discover the open problems from subsisting studies and summarize the advantages of mobility for readers. This will help the researchers and developers avoid the current misapprehensions and capture the real-life scenarios, such as business, government, and education institutions. In spite of the extensive state-of-the-art research work, we also present the diverse mobility factors to investigate the correlation between dynamic nodes with fog nodes in order to accomplish better successful tasks. We conceive that the investigations of mobility factors in the fog computing will furnish the novel perspective on not only dynamic connections but also network dynamics. To revolutionize the follow-up research, we distinguish and foreground futurity directions concerning real-life scenarios of people and vehicles in a dynamic fog environment.

The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines the impact of COVID-19 on Chinese e-commerce by analyzing behavioral changes observed on a large online shopping platform. We first conduct a time series analysis to identify product categories that faced the most extensive disruptions. The time-lagged analysis shows that behavioral patterns of shopping actions are highly responsive to the epidemic's development. Based on these findings, we present a consumer demand prediction method by encompassing the epidemic statistics and behavioral features of COVID-19-related products. Experimental results demonstrate that our predictions outperform existing baselines and further extend to long-term and province-level forecasts. Finally, we discuss how our market analysis and prediction can help better prepare for future pandemics by gaining extra time to launch preventive measures.

Cloud applications require the ability to customize bandwidth and network policies for desired wide area network (WAN) connections. Unfortunately, currently widely used virtual private network is difficult to achieve such customization where sophisticated manual configurations and operator expertise are required. Software-defined network (SDN) creates the opportunities to provide this ability. In this paper, we design Grace, a SDN-based system to provide diverse connections with flexible bandwidth and customized policies, implementing WAN as a service. Our contributions can be listed as follows: 1) We introduce open APIs for customers by abstracting WAN connections based on connection types, bandwidth, latency sensitivity, and policy-related information; 2) We develop an effective conflict detection algorithm considering both resource reservation and safety guarantee; 3) We propose a linear programming - based bandwidth algorithm for latency-sensitive connections by dynamic scheduling of time and bandwidth, and design a pricing scheme for various connection demands to address the case that network cannot fulfill all the connection requests. A prototype implementation and extensive evaluations show that Grace provides the ability to customize WAN connections without policy conflicts, allocates required bandwidth optimally, translates them into low-level configurations for underlying network devices, and successfully deploys WAN in a short time.

Accurate, real-time and fine-spatial population distribution is crucial for urban planning, government management, and advertisement promotion. Limited by technics and tools, we rely on the census to obtain this information in the past, which is coarse and costly. The popularity of mobile phones gives us a new opportunity to investigate population estimation. However, real-time and accurate population estimation is still a challenging problem because of the coarse localization and complicated user behaviors. With the help of the passively collected human mobility and locations from the mobile networks including call detail records and mobility management signals, we develop a bimodal model beyond the prior work to better estimate real-time population distribution at metropolitan scales. We discuss how the estimation interval, space granularity, and data type will influence the estimation accuracy, and find the data collected from the mobility management signals with the 30 min estimation interval performs better which reduces the population estimation error by 30% in terms of Root Mean Square Error (RMSE). These results show us the great potential of using bimodal model and mobile phone data to estimate real-time population distribution.

The trend for dense deployment in future 5G mobile communication networks makes current wired backhaul infeasible owing to the high cost. Millimetre-wave (mm-wave) communication, a promising technique with the capability of providing a multi-gigabit transmission rate, offers a flexible and cost-effective candidate for 5G backhauling. By exploiting highly directional antennas, it becomes practical to cope with explosive traffic demands and to deal with interference problems. Several advancements in physical layer technology, such as hybrid beamforming and full duplexing, bring new challenges and opportunities for mm-wave backhaul. This article introduces a design framework for 5G mm-wave backhaul, including routing, spatial reuse scheduling and physical layer techniques. The associated optimization model, open problems and potential solutions are discussed to fully exploit the throughput gain of the backhaul network. Extensive simulations are conducted to verify the potential benefits of the proposed method for the 5G mm-wave backhaul design.