An application of MODIS data to snow cover monitoring in a pastoral area: A case study in Northern Xinjiang, China

Snow is an important land cover on the earth's surface. It is characterized by its changing nature. Monitoring snow cover extent plays a significant role in dynamic studies and prevention of snow-caused disasters in pastoral areas. Using NASA EOS Terra/MODIS snow cover products and in situ observation data during the four snow seasons from November 1 to March 31 of year 2001 to 2005 in northern Xinjiang area, the accuracy of MODIS snow cover mapping algorithm under varied snow depth and land cover types was analyzed. The overall accuracy of MODIS daily snow cover mapping algorithm in clear sky condition is high at 98.5%; snow agreement reaches 98.2%, and ranges from 77.8% to 100% over the 4-year period for individual sites. Snow depth (SD) is one of the major factors affecting the accuracy of MODIS snow cover maps. MODIS does not identify any snow for SD less than 0.5 cm. The overall accuracy increases with snow depth if SD is equal to or greater than 3 cm, and decreases for SD below 3 cm. Land cover has an important influence in the accuracy of MODIS snow cover maps. The use of MOD10A1 snow cover products is severely affected by cloud cover. The 8-day composite products of MOD10A2 can effectively minimize the effect of cloud cover in most cases. Cloud cover in excess of 10% occurs on 99% of the MOD10A1 products and 14.7% of the MOD10A2 products analyzed during the four snow seasons. User-defined multiple day composite images based on MOD10A1, with flexibilities of selecting composite period, starting and ending date and composite sequence of MOD10A1 products, have an advantage in effectively monitoring snow cover extent for regional snow-caused disasters in pastoral areas.     

Alloyed Branch History: Combining Global and Local Branch History for Robust Performance

This paper introduces alloyed prediction, a new hardware-based two-level branch predictor organization that combines global and local history in the same structure, combining the advantages of current two-level predictors with those of hybrid predictors. The alloyed organization is motivated by measurements showing that wrong-history mispredictions are even more important than conflict-induced mispredictions. Wrong-history mispredictions arise because current two-level, history-based predictors provide only global or only local history. The contribution of wrong history to the overall misprediction rate is substantial because most programs have some branches that require global history and others that require local history. This paper explores several ways to implement alloyed prediction, including the previously proposed bi-mode organization. Simulations show that mshare is the best alloyed organization among those we examine, and that mshare gives reliably good prediction compared to bimodal (two-bit), two-level, and hybrid predictors. The robust performance of alloying across a range of predictor sizes stems from its ability to attack wrong-history mispredictions at even very small sizes without subdividing the branch prediction hardware into smaller and less effective components.     

Greedy regression ensemble selection: Theory and an application to water quality prediction

This paper studies the greedy ensemble selection family of algorithms for ensembles of regression models. These algorithms search for the globally best subset of regressors by making local greedy decisions for changing the current subset. We abstract the key points of the greedy ensemble selection algorithms and present a general framework, which is applied to an application domain with important social and commercial value: water quality prediction.     

Mining Customer Value: From Association Rules to Direct Marketing

Direct marketing is a modern business activity with an aim to maximize the profit generated from marketing to a selected group of customers. A key to direct marketing is to select a subset of customers so as to maximize the profit return while minimizing the cost. Achieving this goal is difficult due to the extremely imbalanced data and the inverse correlation between the probability that a customer responds and the dollar amount generated by a response. We present a solution to this problem based on a creative use of association rules. Association rule mining searches for all rules above an interestingness threshold, as opposed to some rules in a heuristic-based search. Promising association rules are then selected based on the observed value of the customers they summarize. Selected association rules are used to build a model for predicting the value of a future customer. On the challenging KDD-CUP-98 dataset, this approach generates 41% more profit than the KDD-CUP winner and 35% more profit than the best result published thereafter, with 57.7% recall on responders and 78.0% recall on non-responders. The average profit per mail is 3.3 times that of the KDD-CUP winner.     

Spatial Data Infrastructures for environmental e-government services: The case of water abstractions authorisations

Environment-related authorisations are a relevant issue for environmental management. They require a considerable effort by the authorities, and this might result in substantial delays for the citizens. Implementing those authorisation processes by means of e-government services would improve efficiency and, consequently, citizen satisfaction. Environment-related authorisations usually require a variety of geospatial information, and have to deal with administrative areas which do not match physical and ecological ones. They also have to integrate heterogeneous information in different formats, data models and languages, and provided by distinct organisations, even from different countries. This paper discusses how Spatial Data Infrastructures (SDIs) can deal with these problems in the environmental domain, while improving the level of service provision in terms of e-government applications. This is even more relevant within the European Union where there is a legal mandate to establish an SDI to support environmental policies and activities with an impact on the environment. As a proof-of-concept, an application to request and manage water abstraction authorisations, based on an SDI, is demonstrated. This application is part of SDIGER, a cross-border inter-administration SDI to support the water framework directive information access for the Adour–Garonne and Ebro River basins, that was a pilot project for the EU INSPIRE Directive. The introduction of this transactional e-government service modifies the administrative process of granting authorisations: it allows to re-use the effort in data capture made by the applicants in their requests, facilitates the submission of more feasible applications and reduces the workload of the office staff.     

Knowledge Discovery in Multiple Spatial Databases

In this paper, a new approach for centralised and distributed learning from spatial heterogeneous databases is proposed. The centralised algorithm consists of a spatial clustering followed by local regression aimed at learning relationships between driving attributes and the target variable inside each region identified through clustering. For distributed learning, similar regions in multiple databases are first discovered by applying a spatial clustering algorithm independently on all sites, and then identifying corresponding clusters on participating sites. Local regression models are built on identified clusters and transferred among the sites for combining the models responsible for identified regions. Extensive experiments on spatial data sets with missing and irrelevant attributes, and with different levels of noise, resulted in a higher prediction accuracy of both centralised and distributed methods, as compared to using global models. In addition, experiments performed indicate that both methods are computationally more efficient than the global approach, due to the smaller data sets used for learning. Furthermore, the accuracy of the distributed method was comparable to the centralised approach, thus providing a viable alternative to moving all data to a central location.     

Conditional quantile estimation based on optimal quantization: From theory to practice

Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quantization, that was recently introduced (Charlier et al., 2015), are investigated. More precisely, (i) the practical implementation of this estimator is discussed (by proposing in particular a method to properly select the corresponding smoothing parameter, namely the number of quantizers) and (ii) its finite-sample performances are compared to those of classical competitors. Monte Carlo studies reveal that the quantization-based estimator competes well in all cases and sometimes dominates its competitors, particularly when the regression function is quite complex. A real data set is also treated. While the main focus is on the case of a univariate covariate, simulations are also conducted in the bivariate case.     

Solving a system of linear equations: From centralized to distributed algorithms

For a wide range of control engineering applications, the problem of solving a system of linear equations is often encountered and has been well studied. Traditionally, this problem has been mainly solved in a centralized manner. However, for applications related to large-scale complex networked systems, centralized algorithms are often subjected to some practical issues due to limited computational power and communication bandwidth. As a promising and viable alternative, distributed algorithms can effectively address the issues associated with centralized algorithms by solving the problem efficiently in a multi-agent setting that accords with the distributed nature of networked systems. Distributed algorithms decompose the entire problem into many sub-problems that are solved by individual agents in a cooperative manner. In this survey paper, we provide a detailed overview of the state of the art relevant to distributed algorithms for solving a system of linear equations. We will first review basic distributed algorithms including both discrete-time and continuous-time algorithms. Then we will discuss the extended algorithms to achieve communication efficiency. Furthermore, we will also introduce distributed algorithms to obtain the minimum-norm solution for a system of linear equations with multiple solutions, as well as the least-squares solution when there is no solution. Finally, the relationship of distributed algorithms for solving a system of linear equations to the existing distributed optimization algorithms is discussed.     

Publishing sensor observations into Geospatial Information Infrastructures: A use case in fire danger assessment

To improve environmental monitoring, the availability of large coverage, interoperable spatio-temporal data is crucial for its integration into environmental models, for example, to compute fire danger models. In order to produce up-to-date and accurate results, these models require data with high temporal and spatial resolution. Thus, it is promising to consider the increasing number of in-situ sensors providing observations of our environment in real-time. Today, interoperable access to such spatio-temporal data is achieved by Geospatial Information Infrastructures (GIIs). From a technical point of view, GIIs provide these data through standards-based Web service interfaces. While those Web service interfaces already enable the interoperable discovery and retrieval of sensor observations, the functionality to publish sensor observations is still an arduous task. Hence, in this paper, we present an approach to improve the registration of sensors and the publication of their observations via standards-based Web service interfaces. We evaluate our approach by extending a standards-based GII and by applying the developed approach as a proof of concept to integrate in-situ weather observations into the European Forest Fire Information System for assessing fire danger in Spain.     

Application of random forest,generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management, but they are either unavailable or unknown. Hence we applied random forest (RF), generalised linear model (GLM) and their hybrid methods with geostatistical techniques to SSR data by addressing relevant issues with variable selection and model selection. It was found that: 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model selection methods are unsuitable for identifying GLM predictive models and joint application of RF and AIC can select accuracy-improved models; 3) highly correlated predictors may improve RF predictive accuracy; 4) hybrid methods for RF can accurately predict count data; and 5) effects of model averaging are method-dependent. This study depicted the non-linear relationships of SSR and predictors, generated spatial distribution of SSR with high accuracy and revealed the association of high SSR with hard seabed features.     

From single to multiple enrollment i-vectors: Practical PLDA scoring variants for speaker verification

The availability of multiple utterances (and hence, i-vectors) for speaker enrollment brings up several alternatives for their utilization with probabilistic linear discriminant analysis (PLDA). This paper provides an overview of their effective utilization, from a practical viewpoint. We derive expressions for the evaluation of the likelihood ratio for the multi-enrollment case, with details on the computation of the required matrix inversions and determinants. The performance of five different scoring methods, and the effect of i-vector length normalization is compared experimentally. We conclude that length normalization is a useful technique for all but one of the scoring methods considered, and averaging i-vectors is the most effective out of the methods compared. We also study the application of multicondition training on the PLDA model. Our experiments indicate that multicondition training is more effective in estimating PLDA hyperparameters than it is for likelihood computation. Finally, we look at the effect of the configuration of the enrollment data on PLDA scoring, studying the properties of conditional dependence and number-of-enrollment-utterances per target speaker. Our experiments indicate that these properties affect the performance of the PLDA model. These results further support the conclusion that i-vector averaging is a simple and effective way to process multiple enrollment utterances.     

LabVIEW多元线性拟合技术在烟叶复烤排风口空气绝对湿度软测量中的应用研究

在烟叶复烤过程中,各干燥区排风口处空气绝对湿度的准确测量与判断其烟叶中的水分是否合格有密切关系。实际中,虽然在排风口处安装有湿度仪,但由于其周围环境温湿等影响,会使测量结果存在误差。为了提高其测量准确性,本文利用虚拟仪器LabVIEW平台,通过调用读取电子表格子VI,将现场采集到的数据存取到数组中,然后利用广义最小二乘线性拟合子V I,对采集到的数据进行多元线性拟合,并找出软测量预测模型的最佳参数,以实现对排风口绝对湿度的预测或校正。     

Hypermedia navigation: Differences between spatial cognitive styles

Recently, many studies have investigated the role of individual and cognitive differences during Web navigation and Web searching. Despite this interest, no works have considered the role may assume individual differences in real-environment navigation during Web navigation. The aim of this work is to investigate the effect of different spatial cognitive styles: Landmark style (LS), Route style (RS) and Survey style (SS), on Web searching behaviour. In real-environment navigation, having a specific style determines the type of information individuals selected to navigate and orient themselves. We hypothesize that LS individuals are less proficient during Web exploration due to their analytical analysis of the environmental features. Vice versa SS individuals will show high performance on Web exploration for their holistic analysis of the World. We asked 30 College Students (10 LS, 10 RS, 10 SS) to solve three Web information tasks. The spatial cognitive style of participants was assessed through the Spatial Cognitive Style Test, and they were also asked to fill in a questionnaire about their internet and computer use. An ad hoc key-logger program for browsers was used to collect Web behaviour measures. In particular, the measures considered were: search engine tools used (e.g. back button), pages visited and revisited, time spent on information searching, and mouse cursor movements. The results showed significant differences between the spatial cognitive styles: LS seems to use a trial and error strategy in order to obtain the relevant information. Differences also emerged in the distribution of mouse cursor movements during Web navigation.     

Quality prediction for polypropylene production process based on CLGPR model

Online measurement of the melt index is typically unavailable in industrial polypropylene production processes, soft sensing models are therefore required for estimation and prediction of this important quality variable. Polymerization is a highly nonlinear process, which usually produces products with multiple quality grades. In the present paper, an effective soft sensor, named combined local Gaussian process regression (CLGPR), is developed for prediction of the melt index. While the introduced Gaussian process regression model can well address the high nonlinearity of the process data in each operation mode, the local modeling structure can be effectively extended to processes with multiple operation modes. Feasibility and efficiency of the proposed soft sensor are demonstrated through the application to an industrial polypropylene production process.     

INTAMAP: The design and implementation of an interoperable automated interpolation web service

INTAMAP is a Web Processing Service for the automatic spatial interpolation of measured point data. Requirements were (i) using open standards for spatial data such as developed in the context of the Open Geospatial Consortium (OGC), (ii) using a suitable environment for statistical modelling and computation, and (iii) producing an integrated, open source solution. The system couples an open-source Web Processing Service (developed by 52°North), accepting data in the form of standardised XML documents (conforming to the OGC Observations and Measurements standard) with a computing back-end realised in the R statistical environment. The probability distribution of interpolation errors is encoded with UncertML, a markup language designed to encode uncertain data. Automatic interpolation needs to be useful for a wide range of applications and the algorithms have been designed to cope with anisotropy, extreme values, and data with known error distributions. Besides a fully automatic mode, the system can be used with different levels of user control over the interpolation process.     

Going to town: Visualized perspectives and navigation through virtual environments

Two experiments examined how spatial learning perspectives support navigation through virtual urban environments. Participants briefly learned the overall layout of a virtual desktop environment, and then were taken on a simulated journey ending at a starting location within the environment. In Experiment 1, during the journey participants watched simulated video feeds either from the front of the vehicle (route perspective), above the vehicle (survey perspective), both feeds simultaneously, or no video at all. Participants then navigated between ten successive landmarks, and we measured indices of spatial and temporal efficiency, and heading error. Results indicated that the route perspective supported a restricted range of local navigation whereas the survey perspective better supported far-space navigation. Experiment 2 demonstrated that the survey perspective also better supports navigation around unexpected detours. Results are discussed with regard to theories of spatial memory and the design of computer-supported spatial visualization technologies.     

From ICT coordination to ICT integration: a longitudinal case study

This study utilizes a school‐improvement perspective to examine the role of curriculum coordination in the integration of information and communication technologies (ICT) into primary schools. The nature and impact of this role is examined in seven primary schools in Australia. These seven schools were drawn from a longitudinal intervention that provided additional ICT‐related resources and personnel to the schools. An instrument, referred to as the Learning Outcomes and Pedagogy Attributes (LOPA) measure, was developed and charted for the seven schools over the 4‐year data collection period. The changes in LOPA score over time were then analysed in terms of the conditions at the school with regard to curriculum ICT coordination. The study concludes that the coordinator role and school leadership in general, play critical but varying roles in the complex process of ICT integration into schools. Success appeared to be associated with the support provided for the role, the extent to which the role was connected to school leadership, personal leadership characteristics of those in the role and the strategies employed within the role.