With the rapid development of 5G technologies, the demand of quality of service (QoS) from edge users, including high bandwidth and low latency, has increased dramatically. QoS within a mobile edge network is highly dependent on the allocation of edge users. However, the complexity of user movement greatly challenges edge user allocation, leading to privacy leakage. In addition, updating massive data constantly in a dynamic mobile edge network also crucial to ensure efficiency. To address these challenges, this paper proposes a dynamic QoS optimization strategy (MENIFLD_QoS) in mobile edge networks based on incremental learning and federated learning.MENIFLD_QoSoptimizes service cache in edge regions and allocates edge servers to edge users according to the locations of edge servers accessed by edge users in mobile scenarios. While optimizing regional service quality, the system can effectively protect user privacy. In addition, for dynamic incremental data,MENIFLD_QoStrains updated data based on the strategy of incremental learning hence significantly improves optimization speed. Experimental results on an edge QoS dataset show that the proposed strategy achieves global optimization in both multi-variable and multi-peak user allocation scenarios and notably enhances the training efficiency of the regional invocation model.

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make real-time, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results demonstrate that our approach can effectively improve forecasting performance and protect user privacy.

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment – Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make realtime, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results based on public and self-collected data sets demonstrate that our approach can effectively improve forecasting performance and protect user privacy.

Inferring causal relationships is key to data science. Learning causal structures in the form of directed acyclic graphs (DAGs) has been widely adopted for uncovering causal relationships, nonetheless, it is a challenging task owing to its exponential search space. A recent approach formulates the structure learning problem as a continuous constrained optimization task that aims to learn causal relation matrix. Following it are various variants, such as nonlinear variants which uncover nonlinear causal relationships. However, the nonlinear variant which considers an ordinary sparsity constraint as part of its optimization objective may not effectively eliminate spurious edges, i.e., wrongly predicted edges. In this paper, we investigate the defect that the ordinary sparsity constraint inherently contains, that is, when the model predicts spurious edges, the sparsity constraint may be unable to make the relation matrix sparse to help eliminate such spurious edges, and based on the theoretical and empirical analysis of the defect, we propose the proportional sparse penalty constraint PSP which replaces the ordinary sparsity constraint with a normalized first-order matrix norm. We then compare our proposed model GAE-PSP with three models to show considerable performance improvement. We further conduct three comparative experiments and show that the PSP can make the relation matrix sparse to eliminate spurious edges.

Support vector machine (SVM) has been widely applied in flood forecasting models and achieved good results. However, it has been plagued by two problems. One is its over-reliance on the number and quality of the raw input data; the other is that a single model cannot describe the complex relationships hidden in the flood evolution processes. To tackle the two problems, this paper presents an SVM flood forecasting model based on kernel principal component analysis(KPCA) and boosting algorithm. The nonlinear KPCA is applied to extract the useful information from historical flood data. To eliminate the interference caused by redundant information, boosting learning algorithm with multiple SVM models exploits various distribution characteristics of the historical flood data. Each sub-model focuses on the learning of a certain type of samples. Finally the prediction is achieved through combining multiple models. Application to the flood forecasting of Wangjiaba station at Huaihe River shows that the proposed SVM ensemble model based on KPCA and boosting learning can improve the flood forecasting accuracy effectively.

With the development of hyperspectral remote sensing technology, the spectral resolution of the hyperspectral image data becomes denser, which results in large number of bands, high correlation between neighboring bands, and high data redundancy. It is necessary to reduce these bands before further analysis, such as land cover classification and target detection. Aiming at the classification task, this paper proposes an effective band selection method from the novel perspective of spectral shape similarity analysis with key points extraction and thus retains physical information of hyperspectral remote sensing images. The proposed approach takes all the bands of hyperspectral remote sensing images as time series. Firstly, spectral clustering is utilized to cluster all the training samples, which produces the prototypical spectral curves of each cluster. Then a set of initial candidate bands are obtained based on the extraction of key points from the processed hyperspectral curves, which preserve discriminative information and narrow down the candidate band subset for the following search procedure. Finally, filtering contiguous bands according to conditional mutual information and branch and bound search are further performed sequentially to gain the optimal band combination. To verify the effectiveness of the integrated band selection method put forward in this paper, classification employing the Support Vector Machine (SVM) classifier is performed on the selected spectral bands. The experimental results on two publicly available benchmark data sets demonstrate that the presented approach can select those bands with discriminative information, usually about 10 out of 200 original bands. Compared with previous studies, the newly proposed method is competitive with far fewer bands selected and a lower computational complexity, while the classification accuracy remains comparable.

This paper proposes a new technique for hyperspectral band selection from the spectral similarity perspective. Through a newly defined measure for band subset discriminativeness, class-specific important bands are retained which can preserve the spectral similarity of the samples from the same class and narrow down candidate band subset for the following search procedure. Then optimal search is performed in the aggregated band subset from all classes. Experiments on the Indian Pine benchmark data set have proved the efficiency and effectiveness of the proposed method.

Web service composition is a new paradigm to develop distributed and reactive software-intensive systems. Predicting and preventing failures of dynamic composite services is an important and challenge problem due to the dynamically evolving attribute. In previous work, we propose CASSANDRA, a novel proactive monitoring technique with the ability to predict and prevent the potential failures happening in dynamic evolvable system. In this paper, we concretize the approach into web service composition field. By combining runtime information and design-time specification of basic services, the approach can analyse future -step models ahead of the current service execution states. Then, this model can be used to check with a set of desired properties represented by property sequence chart. Initial experiments on an online medicine case study validates our approach and shows encouraging results.

The Web Services Choreography Description Language (WS-CDL) is a specification developed by the W3C and can be viewed as a blueprint for the development of end-point services. Consequently, it is worth providing a systematic approach for its modeling, analysis and verification. The Unified Modeling Language (UML) is an industry standard for modeling. Applying UML to model WS-CDL is obviously a promising solution to bring together academics and practitioners through a unique standard language. In this paper, we propose to use different UML diagrams to model WS-CDL. UML Component Diagram is used to model the underlying structure of WS-CDL. UML Sequence Diagram is utilized to model the activities in WS-CDL. UML State Machine Diagram is utilized to model the behaviors of each role participating in a WS-CDL specification. We then enrich the UML State Machine Diagram with data by the use of UML Class Diagram. Given the UML specification of WS-CDL, we then provide a systematic way of formally analyzing and verifying WS-CDL against desired properties. Some experiments show that our approach can verify structural, behavioral and data properties in a middle-scale data-enriched WS-CDL specification.