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Abstract:In this study, the updated Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset for growing season (April to October), which can better reflect the vegetation vigor, was used to investigate the interannual variations in NDVI and its relationship with climatic factors, in order to preliminarily understand the climate impact on vegetation and provide theoretical basis for the response of ecosystem to climate change. Multivariate linear regression models, including the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), were adopted to analyze the correlation between NDVI and climatic factors (temperature and precipitation) together. Average growing-season NDVI significantly increased at a rate of 0.0015/year from 1982 to 2013, larger than several regions in China. On the whole, its relationship with temperature is positive and also stronger than precipitation, which indicated that temperature may be a limiting factor for the vegetation growth in the Karst region. Moreover, the correlation coefficients between grassland NDVI and climatic factors are the largest. Under the background of NDVI increasing trend from 1982 to 2013, the period of 2009-2012 was chosen to investigate the influencing factors of a sharp decline in NDVI. It can be found that the reduced temperature and solar radiation, caused by the increase in cloud cover and precipitation, may play important roles in the OPEN ACCESS Remote Sens. 2015, 7 11106 vegetation cover change. All in all, the systematic research on the interannual variations of growing-season NDVI and its relationship with climate revealed the heterogeneity and variability in the complicated climate change in the Karst ecosystem for the study area. It is the Karst characteristics that hinder obtaining more representative conclusions and tendencies in this region. Hence, more attention should be paid to promoting Karst research in the future.

Current network intrusion detection systems (NIDS) lack the adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online GMMs are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines (SVM). The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process which uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO and SVM-based algorithm effectively combines the local detection models into the global model in each node: the global model in a node can handle the intrusion types which are found in other nodes, without sharing the samples of these intrusion types.

here is a flurry of activity in the networking community developing advanced services networks. Although the focus of these efforts varies widely from per-flow service definitions like integrated services (IntServ) [1,2] to service frameworks like Xbind [3], they share the overall goal of evolving the Internet service model from what is essentially a basic bitway pipe to a sophisticated infrastructure capable of supporting novel advanced services.In this article we consider a network environment that comprises not only communication services, but storage and computation resources as well. By packaging storage/computation resources together with communication services, value-added service providers will be able to support sophisticated services such as intelligent caching, video/audio transcoding and mixing, virtual private networking, virtual reality games, and data mining. In such a service-oriented network, value-added services can be composed in a hierarchical fashion: applications invoke high-level service providers, which may in turn invoke services from lower-level service providers. Providers in the top of the hierarchy will typically integrate and add value to lower-level services, while the lowest-level services will supply basic communication and computational support. Since services can be composed hierarchically, both applications and service providers will be able to combine their own resources with resources or services delivered by other service providers to create a high-quality service for their clients. The design of such a service-oriented network poses challenges in several areas, such as resource discovery, resource management, service composition, billing, and security. In this article we focus on the resource management architecture and algorithms for such a network.Service-oriented networks have several important differences from traditional networks that make existing network resource management inadequate. First, while traditional communication-oriented network services are provided by switches and links, value-added services will have to manage a broader set of resources that includes computation, storage, and services from other providers. Moreover, interdependencies between differ-0890-8044/01/$10.00 AbstractThe Internet is rapidly changing from a set of wires and switches that carry packets into a sophisticated infrastructure that delivers a set of complex value-added services to end users. Services can range from bit transport all the way up to distributed value-added services like video teleconferencing, virtual private networking, data mining, and distributed interactive simulations. Before such services can be supported in a general and dynamic manner, we have to develop appropriate resource management mechanisms. These resource management mechanisms must make it possible to identify and allocate resources that meet service or application requirements, support both isolation and controlled dynamic sharing of resources across services and applications sharing physical resources, a...

Clustering is an important technology that can divide data patterns into meaningful groups, but the number of groups is difficult to be determined. This paper proposes an automatic approach, which can determine the number of groups using silhouette coefficient and the sum of the squared error.The experiment conducted shows that the proposed approach can generally find the optimum number of clusters, and can cluster the data patterns effectively.

At landscape scale, the normalized difference vegetation index (NDVI) can be used to indicate the vegetation's dynamic characteristics and has been widely employed to develop correlated and dependent relationships with the climatic and environmental factors. However, studies show that NDVI-environment relationships always emerge with complex features such as nonlinearity, scale dependency, and nonstationarity, especially in highly heterogeneous areas. In this study, we used geographically weighted regression (GWR), a local modeling technique to estimate regression models with spatially varying relationships, to investigate the spatially nonstationary relationships between NDVI and climatic factors at multiple scales in North China. The results indicate that all GWR models with appropriate bandwidth represented significant improvements of model performance over the ordinary least squares (OLS) models. The spatial relationships between NDVI and climatic factors varied significantly over space and were more significant and sensitive in the ecogeographical transition zone. Clear spatial patterns of slope parameters and local coefficient of determination (R 2 ) were found from the results of the GWR models. Moreover, the spatial patterns of the local R 2 of NDVI-precipitation are much clearer than the R 2 of NDVI-temperature in the semiarid and subhumid areas, which mean that precipitation has more significant influence on vegetation in these areas. In conclusion, the study revealed detailed site information on the variable relationships in different parts of the study area, especially in the ecogeographical transition zone, and the GWR model can improve model ability to address spatial, nonstationary, and scale-dependent problems in landscape ecology.

Abstract-A Content Discovery System (CDS) allows nodes in the system to discover contents published by some other nodes in the system. Existing CDS systems have difficulties in achieving both scalability and rich functionality. In this paper, we present the design and evaluation of a distributed and scalable CDS. Our system uses Rendezvous Points (RPs) for content registration and query resolution, and can accommodate frequent updates from dynamic contents. Contents stored in our system can be searched via subset matching. We propose a novel mechanism that uses load balancing matrices (LBMs) to dynamically balance both registration and query load across nodes in the system to maintain high system throughput even under skewed load. Our system utilizes existing Distributed Hash Table (DHT) mechanisms for CDS overlay network management and routing. We validate our system's scalability and load balancing properties using extensive simulation.

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