This article describes the structure and various tools for developing websites and plotting the functions studied in the course of mathematics using Internet technologies. Markup and web programming languages, tools for site design and the use of a database to store information are considered. The presented program codes, in which the construction of graphs of a function with given coefficients is considered, can be used as a visual aid for those of interest, students, applicants, etc.
The ICT revolution has spread rapidly across countries, industries and socio-economic activities over the past few decades. Deep transformational effects. As a result, ICTs are playing an increasingly important role in economic growth and structural change. New technologies and applications are being developed to foster better communication with citizens, to facilitate innovation in organizations and create competitive advantage. We are witnessing a shift from a business culture to a social culture based on networks, where innovations created for users have a significant impact. New technologies and their application in productive activities are causing changes in economic structures and contributing to increased labor force productivity. The use of ICTs leads to a diversification of innovation through various channels. In general, ICTs have an important contribution to economic growth leading to improved well-being and living standards. This document aims to present the relationship between the share of the ICT sector in the national economy, network readiness and competitiveness in the EU Member States. Research methods include documentary and bibliographic analysis, as well as comparative analysis.
K-nearest Neighbor (KNN) is a widely used classiflcation method that operates by a majority vote on a k{ nearest neighbor set. But when the KNN is used to deal with the datasets with difierent characteristics in classiflcation, it is di‐cult to select an appropriate parameter k which afiects obviously the performance and e‐ciency of the algorithm. Therefore, how to select appropriate parameter k value of KNN algorithm has been an open issue in the fleld of data mining. So, Natural Nearest Neighbor (3N) proposed by us is a novel concept on nearest neighbor, which does not need a parameter K and in which the neighbors of each point are obtained by an adaptive algorithm. This paper proposes a Classiflcation Algorithm based on Natural Nearest Neighbor (CAb3N). Comprehensive experimental results on the UCI dataset conflrm the claims that CAb3N not only has the advantage of parameter-free, but also has the better accuracy and overall performance both than the traditional KNN algorithm and hubness-based weighted KNN classiflcation algorithm.
Ontology alignment is regarded as the most perspective way to achieve semantic interoperability among heterogeneous data. The majority of state of art ontology alignment systems used one or more string similarity metrics, while the performance of these metrics were not given much attention. In this paper we flrst analyze naming variations in competing ontologies, then we evaluate a wide range of string similarity metrics, from the experimental result we can get some heuristic strategies to achieve better alignment results with regard to efiectiveness and e‐ciency.
Defense spending should flt for economic development and social afiord ability, thus study on the equilibrium relationship between defense spending and economic development is very important. This paper applies functional data clustering algorithm to categorize the countries by defense burden. It excludes interference of unexpected factors, transforms discrete data to continuous data, and flnally gets two groups of countries with higher and lower defense burden respectively. According to the clustering results, through panel data model, it explores the relationship between defense expenditure and economic development of each category. Results show that using functional data clustering algorithm, we can get difierent cluster results from traditional classiflcation. Each category includes both developing and developed countries. In addition, there exists long-term stable equilibrium relationship between defense expenditure and GDP in each class, but defense expenditure difierently impacts on economic growth in difierent countries.
The virtual coordinate-based geometric routing protocol is widely used in wireless sensor networks. In this paper Schnyder geometric routing algorithm based on coordinate-based geometric is researched and implemented. A novel algorithm called Greedy-Compass Double Model Routing Algorithm is proposed in order to solve the problem of node failure. Finally, an experiment is demonstrated to show that the achievable rate of the improved algorithm is much higher than that of Schnyder algorithm under the same condition.
An optimization method of the NMF algorithm initialization is proposed. This optimization method can be easily integrated with the existing initialization methods of NMF algorithm. The strategy is based on the geometric interpretation of NMF, in the convex hull, the intersection point between the connect of two points and the corresponding boundary of probability simplex, is used to update the initial basis vectors corresponding point in the matrix, so that the base vector matrix extended, and it can better contain the original matrix. Many numerical examples show that, compared with the original initialization, this method can obtain better results.
As a popular leaning algorithm, Support Vector Machine (SVM) have been utilized to solve the problem of data mining and knowledge discovery. However, as far as some unbalanced data sets of multi-group are concerned, the classifler model trained by C-SVM always presents some imbalanced error-rates on separating samples. Based on analysis of Lagrange multiplier, the paper brings forward some novelty concepts including the outer boundary of group, Misleading-SV, prediction-error-rate, etc. An innovative SVM based on C-correction is formulated and a method for correcting slack constant C is designed. On the target of winter wheat seed geometric feature evaluation for quality gradation, the research team constructs some testing experiments for method validation. Analysis of accuracy contour suggests the proposal scheme is able to efiectively separate seeds by their geometric property at an accuracy of 96.5%. In parallel with some known congeneric algorithms, contrast results evidences that as the data set with sparse samples is considered, the method for correcting slack constant can elevate the general separation precision of classifler prominently.
Based on the analysis of structural parameters, the flow field of reverse blowing pickup mouth was calculated with CFD. The dust collection efficiency was validated with gas-solid phrase flow. The results show that the diameter of outlet or the dip angle increases respectively when iD1B ≤ 0:45 or ≤ 110 ◦ . Airflow velocities of four narrow slots (front, rear, left and right) increase; With the sweeper-traveling speed increasing overall removal efficiency and grade efficiency decrease. Especially for grade efficiency, sweeper-traveling speed has great effects on large size particles, while it has slight effects on small size particles under the same operating conditions.
Clustering in textual document attracts more and more attention with the huge Internet news data appears every day. But the performance can be in∞uenced by the high dimension vectors based on wordbag. Many of which are redundant information. Hence, it is very necessary to derive a low dimensional subspace that contain less redundant information in order to make the documents can be clustered more reasonably. In traditional studies, learning a subspace and clustering vectors are divided into two independent steps, e.g., feature selection step and clustering step. In this way, it could not estimate whether the subspace is appropriate for the clustering. To solve this issue, we select the feature flrstly, and combine the subspace learning step and the clustering step into an iterative procedure. First, we take into account similarity between the intra clusters and separability in the inter cluster documents, and then utilize the a‐nity propagation to adopt to partition the number of clusters. The experimental results show that our proposed method outperforms the conventional methods of document clustering using our data set.
In the step-stress tests, the most common model is Nelson’s Cumulative Exposure Model, where the hazard function is discontinued at the change stress level changing point. However, the continuous change hazard function in the step-stress tests is more accordance with actual situation. We addressed this issue by incorporating a lag period in the model, resulting in a continuous hazard function with linearly increasing hazard in the lag period. Also, when a test unit fails, there are often several risk factors associated the cause of failure, which is commonly referred to as competing risks. If we can get the failure time information, but cannot identity the exactly cause, we call this type of data as masked data. In this paper, we deal the parameter estimation of the masked data in competing risks under simple step-stress circumstance with lagged efiects. We apply the maximum likelihood approach via the expectation-maximization algorithm for the parameters estimation, and use the bootstrap method for the parameters confldence interval estimation. To verify the validity of the model, we illustrated it by a numerical example.
Learning Using Privileged Information (LUPI) provides an efiective framework to solve the learning problem under situation of the asymmetric distribution of information between training and test time. It has been successfully applied in the category recognition, e.g., protein classiflcation, hand-writing recognition, animal categorization, etc. However, in the existing methods, various semantic attributes, with the help of experts, were only simply translated into the feature vectors and considered as the privileged data, which restricts the LUPI to the simple applications since it is di‐cult to guarantee that the privileged data is similarly informative about the problem at hand as the original data. Therefore, this paper presents a novel approach based on an attribute-ranking learning algorithm to construct the example-oriented privileged data. The main idea is to provide an efiective means to transfer the midlevel semantic attributes to the original training data. Namely, we flrst obtain a real-valued rank per attribute for each example indicating the relative strength of the attribute presence in all examples, and then the resulting attribute ranking results are used to generate the privileged data. The experimental results show that the proposed approach provides a promising means to apply the privileged ranking attributes, and further demonstrate signiflcant improvements in classiflcation accuracy on three typical databases: PubFig, OSR and AwA.
In this paper, A class new parameter conjugate gradient method and a new hybrid conjugate gradient method are proposed. The global convergence of the algorithms are proved under the Wolfe line search without the descent condition. Numerical experiments show that the hybrid conjugate gradient algorithm is recommendable.
A coach performance evaluation model based on objective statistic makes for the efficiency of coach selection. In this paper, the coach performance evaluation model takes Grey Relating TOPSIS Method which focuses on the curve trend and position into consideration thus the result is the relative closeness, namely score of each coach. And in comprehensive view of the subjective and objective factors and operational factors, AHP and Principal Component Analysis are introduced to confirm the decision matrix in TOPSIS. In addition, the Standard Deviation Standardized Method is employed to eliminate the overall level of different sports. Finally, Modified TOPSIS model is applied to evaluate American college basketball, football and hockey coaches comprehensively.
