Abstract For a long time, the problems of cold start and sparse data have always been the key problems to be solved by the recommendation system. Researchers usually use auxiliary information to deal with the aforementioned problems, thereby achieving the purpose of enhancing the recommendation effect. For example, the multitask feature learning framework (MKR) uses knowledge graphs as auxiliary information to enhance recommendations. However, the MKR algorithm has the problem of insufficient semantic information representation which affect the recommendation results. Thus, a multitask recommendation algorithm based on DeepFM and graph convolutional network (DeepFM_GCN) is proposed. The graph convolution network is used to deeply mine auxiliary entity information in the knowledge graph to supplement the sparse item semantics information in the recommendation task. Through the method of cross compression unit combined with Deep Neural Network to achieve feature sharing items and entities which to make up for the impact of insufficient feature representation. Then the DeepFM_GCN model utilizes DeepFM to deeply mine the interaction feature of users and items to avoid inaccurate items recommended to users. From the analysis of the experimental results, the DeepFM_GCN model can more fully explore user and item features, accordingly avoiding semantic ambiguity and improving prediction accuracy.

Most of the emerging applications such as the Deep Neural Networks (DNN) based smart Internet of Things (IoT) systems need intensive and high-performance computing, which is contradictory to the limited resources of IoT/terminal devices. It is a big challenge to offload all tasks to the cloud due to the bandwidth limitation, processing overhead, and transmission costs. Edge computing as an extension of cloud computing that can provide abundant computing resources near the edge of the network and thereby can potentially improve the QoS of applications. However, offloading tasks to the edge servers is liable to external security threats. How to balance the response time and the security of application services is a big challenge for realizing good application service placement. This paper proposes a time and security efficient task scheduling framework in the edge-cloud environment. The corresponding computing models are established, such as the security-related model, time model, and the risk probability model. Then, a time-guaranteed and security-aware task scheduling algorithm is proposed including the domain construction, the security-aware task ranking, and the task dispatching. Extensive simulation experiments have been conducted. Results show that the proposed method has better performance than the other four compared methods in general.

Fog computing as an extension of the cloud based infrastructure, provides a better computing platform than cloud computing for mobile computing, Internet of Things, etc. One of the problems is how to make full use of the resources of the fog so that more requests of applications can be executed on the edge, reducing the pressure on the network and ensuring the time requirement of tasks. The high mobility of fog nodes also has a great impact on the task completion time and user satisfaction. Thus, a general IoT-Fog-Cloud computing architecture with a contract-based resource sharing mechanism is proposed in this paper. The contract establishment problem of resource sharing mechanism among fog clusters is modeled as a sealed-bid bilateral auction in order to take full advantage of the fog resources and ensure that more tasks could be executed on the fog. Then, we propose a scheduling method based on functional domain construction to mitigate the influence of mobility of fog nodes. It includes the selection of critical fog nodes and the construction of fog function domains based on spectral clustering. The selection of critical fog nodes is used to find the best fog nodes in each fog cluster with respect to the betweenness centrality, computing performance and communication delay to the IoT nodes. The critical nodes are responsible for building the functional domains of the remaining fog nodes in each fog cluster. Functional domain construction is used to determine the set of fog nodes contained in the corresponding functional domain. Finally, through extensive simulation experiments, the performance difference between the proposed method and the other four methods in terms of average service time, average utilization of fog nodes, success rate of tasks, average WLAN delay and the average cost of successful tasks are evaluated. Results show that our method generally outperforms the other four methods in these metrics.

An effective method for addressing the configuration optimization problem (COP) in Software Product Lines (SPLs) is to deploy a multi-objective evolutionary algorithm, for example, the state-of-the-art SATIBEA. In this paper, an improved hybrid algorithm, called SATIBEA-LSSF, is proposed to further improve the algorithm performance of SATIBEA, which is composed of a multi-children generating strategy, an enhanced mutation strategy with local searching and an elite inheritance mechanism. Empirical results on the same case studies demonstrate that our algorithm significantly outperforms the state-of-the-art for four out of five SPLs on a quality Hypervolume indicator and the convergence speed. To verify the effectiveness and robustness of our algorithm, the parameter sensitivity analysis is discussed and three observations are reported in detail.

Nowadays it is common for applications to run on data centers and deliver services to users. With the increase of tasks of multiple applications, it is a challenge for data center providers to make full use of the available resources, and improve task response time without too much computational cost. This paper focuses on load-balance based time efficient resource scheduling. A resource allocation architecture for SDN-enabled data center and a load-balance based resource allocation approach(LBA) are proposed. LBA is mainly used to maintain the the whole resource in a balancing state and assign appropriate resources to tasks, majorly consisting of three parts: Load-balance, VM-selection and Path-selection. Comprehensive simulation experiments are conducted to evaluate the effectiveness of LBA. Experiment results show that LBA can take full advantage of the available resources and improve task response time on the basis of load-balance, making both SLA violation rate and average cost as small as possible.

Forecasting monthly calls for repair from gas consumers is an important part of the gas company to improve the level of service, optimize the allocation of resources and improve the living level of people. In this paper, through the study of historical data of monthly calls for repair from gas consumers, we find that it has the characteristics of seasonal periodic variation. A hybridization methodology based on Seasonal Autoregressive Integrated Moving Average (SARIMA) and back propagation(BP) neural network is proposed, which is used to forecast monthly calls for repair from gas consumers. The time series of monthly calls for repair from gas consumers is decomposed into linear autocorrelation and non-linear structure of two parts. The SARIMA model is used to predict the linear part of the sequence, and the BP neural network model is used to predict the non-linear residual part. Finally, the forecast results of two parts are synthesized into the final result. The case study shows that the hybrid model outperforms either of the models used separately. Moreover, the hybrid model can balance the deviation of a single model with better applicability and higher accuracy.

Credit evaluation of customer is an important task for gas companies to achieve marketing management, and is of great significance to improve the economic efficiency of enterprises. By analyzing various factors influencing gas customer credit, a gas customer credit index hierarchy is established. Then, based on the credit index hierarchy, this paper proposes a hybrid credit evaluation method by cluster analysis and analytic hierarchy process(AHP). This method first divides gas customers into different groups by cluster analysis, then determines the credit index weight by AHP, and finally evaluates gas customer credit rating by combining the above two results. The empirical analysis of the actual data of a gas company shows that as a synthesis of customer data's statistical properties and gas professionals' actual work experience, this model can evaluate gas customer credit rating rationally and effectively.

In recent years, XML technology has won wide attention from both industry and academic. It can be used to mark data, define the data type and their own markup language. It is a cross-platform, context-dependent technology in the Internet environment and an effective tool for todays distributed structure information. The S-XML is a new approach for storing semi-structured data, and it supports query of the node in XML with SQL statements, which has shown impressive performance on many classic data sets. However, it is difficult to store XML data into a relational database, and the S-XML spends much more time and space to store the data. In this paper, we propose an efficient mapping approach, the mini-XML, to mapping XML into the relational database. In addition, path technique and position information are used to indicate the complex node relationship. Finally, two experiments are conducted to prove that the proposed method can achieve better performance in the decreasing of the storage time and storage space, especially dealing with the large amount of data.