Personalized web pages ranking algorithm based on user preferences

In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.

New rank learning algorithm

To overcome the limitation that complex data types with noun attributes cannot be processed by rank learning algorithms, a new rank learning algorithm is designed. In the learning algorithm based on the decision tree, the splitting rule of the decision tree is revised with a new definition of rank impurity. A new rank learning algorithm, which can be intuitively explained, is obtained and its theoretical basis is provided. The experimental results show that in the aspect of average rank loss, the ranking tree algorithm outperforms perception ranking and ordinal regression algorithms and it also has a faster convergence speed. The rank learning algorithm based on the decision tree is able to process categorical data and select relative features.

Probability Distribution of Edge in Adjacent Matrix of Aviation Network of China and Algorithm of Searching Non-overlap Community Structure Based on Complex Network

In order to discover the probability distribution feature of edge in aviation network adjacent matrix of China and on the basis of this feature to establish an algorithm of searching non-overlap community structure in network to reveal the inner principle of complex network with the feature of small world in aspect of adjacent matrix and community structure,aviation network adjacent matrix of China was transformed according to the node rank and the matrix was arranged on the basis of ascending node rank with the center point as original point.Adjacent probability from the original point to extension around in approximate area was calculated.Through fitting probability distribution curve,power function of probability distribution of edge in adjacent matrix arranged by ascending node rank was found.According to the feature of adjacent probability distribution,deleting step by step with node rank ascending algorithm was set up to search non-overlap community structure in network and the flow chart of algorithm was given.A non-overlap community structure with 10 different scale communities in aviation network of China was found by the computer program written on the basis of this algorithm.

Detecting Malicious Uniform Resource Locators Using an Applied Intelligence Framework

The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators(URLs).Three categories of features,both ML and Deep Learning(DL)algorithms and a ranking schema are included in the proposed framework.We apply frequency and prediction-based embeddings,such as hash vectorizer,Term Frequency-Inverse Dense Frequency(TF-IDF)and predictors,word to vector-word2vec(continuous bag of words,skip-gram)from Google,to extract features from text.Further,we apply more state-of-the-art methods to create vectorized features,such as GloVe.Additionally,feature engineering that is specific to URL structure is deployed to detect scams and other threats.For framework assessment,four ranking indicators are weighted:computational time and performance as accuracy,F1 score and type error II.For the computational time,we propose a new metric-Feature Building Time(FBT)as the cutting-edge feature builders(like doc2vec or GloVe)require more time.By applying the proposed assessment step,the skip-gram algorithm of word2vec surpasses other feature builders in performance.Additionally,eXtreme Gradient Boost(XGB)outperforms other classifiers.With this setup,we attain an accuracy of 99.5%and an F1 score of 0.99.

Selecting Best Software Vulnerability Scanner Using Intuitionistic Fuzzy Set TOPSIS

Software developers endeavor to build their products with the least number of bugs.Despite this,many vulnerabilities are detected in software that threatens its integrity.Various automated software i.e.,vulnerability scanners,are available in the market which helps detect and manage vulnerabilities in a computer,application,or a network.Hence,the choice of an appropriate vulnerability scanner is crucial to ensure efficient vulnerability management.The current work serves a dual purpose,first,to identify the key factors which affect the vulnerability discovery process in a network.The second,is to rank the popular vulnerability scanners based on the identified attributes.This will aid the firm in determining the best scanner for them considering multiple aspects.The multi-criterion decision making based ranking approach has been discussed using the Intuitionistic Fuzzy set(IFS)and Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS)to rank the various scanners.Using IFS TOPSIS,the opinion of a whole group could be simultaneously considered in the vulnerability scanner selection.In this study,five popular vulnerability scanners,namely,Nessus,Fsecure Radar,Greenbone,Qualys,and Nexpose have been considered.The inputs of industry specialists i.e.,people who deal in software security and vulnerability management process have been taken for the ranking process.Using the proposed methodology,a hierarchical classification of the various vulnerability scanners could be achieved.The clear enumeration of the steps allows for easy adaptability of the model to varied situations.This study will help product developers become aware of the needs of the market and design better scanners.And from the user’s point of view,it will help the system administrators in deciding which scanner to deploy depending on the company’s needs and preferences.The current work is the first to use a Multi Criterion Group Decision Making technique in vulnerability scanner selection.

Algebraic-Based Nonbinary LDPC Codes with Flexible Field Orders and Code Rates

In this paper, we study the rank of matrices over GF(2~p),and propose two construction methods for algebraic-based nonbinary LDPC codes from an existing LDPC code, referred to as the original code. By multiplying all elements of each column of the binary parity-check matrix H corresponding to the original code with the same nonzero element of any field, the first class of nonbinary LDPC codes with flexible field order is proposed. The second method is to replace the nonzero elements of some columns in H with different nonzero field elements in a given field, and then another class of nonbinary LDPC codes with various rates is obtained. Simulation results show that the proposed nonbinary LDPC codes perform well over the AWGN channel with the iterative decoding algorithms.

An adaptive segmentation method combining MSRCR and mean shift algorithm with K-means correction of green apples in natural environment

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be solved.In this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light conditions.The enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent fruit.After that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold segmentation.Finally,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture features.The users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this study.Compared with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,respectively.Additionally,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared algorithms.The proposed method accurately recognized the green apples under complex illumination conditions and growth environments.Additionally,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

A power fluctuation evaluation method of PV plants based on RankBoost ranking

Fluctuation evaluation is an important task in promoting the accommodation of photovoltaic (PV) power generation. This paper proposes an evaluation method to quantify the power fluctuation of PV plants. This consists of an index system and a ranking method based on the RankBoost algorithm. Eleven indices are devised and included in the index system to fully cover diverse fluctuation features. By handling missing and invalid data effectively, the ranking method fuses multiple indices automatically and provides a systematic and comprehensive comparison of power fluctuation. Simulation results based on power data from six PV plants indicate that the evaluation list obtained by the RankBoost ranking method is better represented and more comprehensive than that derived by the equal weight method.

Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms

Effective identification of pollution sources is particularly important for indoor air quality.Accurate estimation of source strength is the basis for source effective identification.This paper proposes an optimization method for the deconvolution process in the source strength inverse calculation.In the scheme,the concept of time resolution was defined,and combined with different filtering positions and filtering algorithms.The measures to reduce effects of measurement noise were quantitatively analyzed.Additionally,the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and scored.The hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative methods.Results showed that for the filtering position and algorithm,Butterworth filtering performed better,and different filtering positions had little effect on the inverse calculation.For the calculation time step,the optimal Tr(time resolution)was 0.667%and 1.33%in the simulation and experiment,respectively.The hybrid algorithms were found to not perform better than the single algorithms,and the SART(simultaneous algebraic reconstruction technique)algorithm from CAT(computer assisted tomography)yielded better performances in the accuracy and stability of source strength identification.The relative errors of the inverse calculation for source strength were typically below 25%using the optimization scheme.