Enhancing Collaborative Filtering via Topic Model Integrated Uniform Euclidean Distance

Recommendation system can greatly alleviate the "information overload" in the big data era. Existing recommendation methods, however, typically focus on predicting missing rating values via analyzing user-item dualistic relationship, which neglect an important fact that the latent interests of users can influence their rating behaviors. Moreover, traditional recommendation methods easily suffer from the high dimensional problem and cold-start problem. To address these challenges, in this paper, we propose a PBUED(PLSA-Based Uniform Euclidean Distance) scheme, which utilizes topic model and uniform Euclidean distance to recommend the suitable items for users. The solution first employs probabilistic latent semantic analysis(PLSA) to extract users' interests, users with different interests are divided into different subgroups. Then, the uniform Euclidean distance is adopted to compute the users' similarity in the same interest subset; finally, the missing rating values of data are predicted via aggregating similar neighbors' ratings. We evaluate PBUED on two datasets and experimental results show PBUED can lead to better predicting performance and ranking performance than other approaches.

Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification

Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately.This study discusses data classification using a fuzzy soft set method to predict target classes accurately.This study aims to form a data classification algorithm using the fuzzy soft set method.In this study,the fuzzy soft set was calculated based on the normalized Hamming distance.Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function.In the classification step,a generalized normalized Euclidean distance is used to determine the similarity between two sets of fuzzy soft sets.The experiments used the University of California(UCI)Machine Learning dataset to assess the accuracy of the proposed data classification method.The dataset samples were divided into training(75%of samples)and test(25%of samples)sets.Experiments were performed in MATLAB R2010a software.The experiments showed that:(1)The fastest sequence is matching function,distance measure,similarity,normalized Euclidean distance,(2)the proposed approach can improve accuracy and recall by up to 10.3436%and 6.9723%,respectively,compared with baseline techniques.Hence,the fuzzy soft set method is appropriate for classifying data.

Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

RFID unreliable data filtering by integrating adaptive sliding Window and Euclidean distance

Through improving the redundant data filtering of unreliable data filter for radio frequency identification（RFID） with sliding-window,a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed.The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance.The comparison between the results from the method proposed in this paper and previous research shows that it can improve the accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.

AN INEXACT SMOOTHING NEWTON METHOD FOR EUCLIDEAN DISTANCE MATRIX OPTIMIZATION UNDER ORDINAL CONSTRAINTS

When the coordinates of a set of points are known, the pairwise Euclidean distances among the points can be easily computed. Conversely, if the Euclidean distance matrix is given, a set of coordinates for those points can be computed through the well known classical Multi-Dimensional Scaling （MDS）. In this paper, we consider the case where some of the distances are far from being accurate （containing large noises or even missing）. In such a situation, the order of the known distances （i.e., some distances are larger than others） is valuable information that often yields far more accurate construction of the points than just using the magnitude of the known distances. The methods making use of the order information is collectively known as nonmetric MDS. A challenging computational issue among all existing nonmetric MDS methods is that there are often a large number of ordinal constraints. In this paper, we cast this problem as a matrix optimization problem with ordinal constraints. We then adapt an existing smoothing Newton method to our matrix problem. Extensive numerical results demonstrate the efficiency of the algorithm, which can potentially handle a very large number of ordinal constraints.

Euclidean distance harmonic method for establishing theoretical MAPK/Erk signaling pathway in treated breast cancer line MCF-7

Hierarchical clustering algorithms, such as Pearson＇s correlation, Euclidean distance, Euclidean distance harmonic, Spearman rank correlation, Kendall＇s tau, and City-block distance, were used to find the best way to establish theoretical MAPK/Erk signaling pathway on the basis of breast cancer line MCF-7 gene expressions. The algorithm constructs a hierarchy from top to bottom on the basis of a self-organizing tree. It dynamically finds the number of clusters at each level. It was found that only Euclidean distance harmonic is fit for the analysis of the cascade composed from a RAF1 （c-Raf）, a MKNK1, a MAPKK （MEK1/2） to MAPK （Erk） in breast cancer line MCF-7. The result is consistent with the biological experimental MAP/Erk signaling pathway, and the theoretical MAPK/Erk signaling pathway on breast cancer line MCF-7 is set up.

A unified framework for isotropic meshing based on narrowband Euclidean distance transformation

In this paper, we propose a simpleyet-effective method for isotropic meshing relying on Euclidean distance transformation based centroidal Voronoi tessellation(CVT). Our approach improves the performance and robustness of computing CVT on curved domains while simultaneously providing highquality output meshes. While conventional extrinsic methods compute CVTs in the entire volume bounded by the input model, we restrict the computation to a 3D shell of user-controlled thickness. Taking voxels which contain surface samples as sites, we compute the exact Euclidean distance transform on the GPU. Our algorithm is parallel and memory-efficient,and can construct the shell space for resolutions up to 20483 at interactive speed. The 3D centroidal Voronoi tessellation and restricted Voronoi diagrams are also computed efficiently on the GPU. Since the shell space can bridge holes and gaps smaller than a certain tolerance, and tolerate non-manifold edges and degenerate triangles, our algorithm can handle models with such defects, which typically cause conventional remeshing methods to fail. Our method can process implicit surfaces, polyhedral surfaces, and point clouds in a unified framework. Computational results show that our GPU-based isotropic meshing algorithm produces results comparable to state-ofthe-art techniques, but is significantly faster than conventional CPU-based implementations.

The Relation between Genetic Difference for Parents of Hybrid Rice and Heterosis

Exploring the indica-japonica differentiation in parents of hybridization can provide theoretical bases for utilizing inter-subspecific heterosis. In this study, 5 sterile lines and 18 self-bred restorer lines were used as female parents and male parents respectively. Then 90 combinations were constructed by incomplete diallel cross followed by relationship analysis between parental Cheng’s index difference value and Euclidean distance and heterosis. The results showed a significant correlation between several phenotype values, super male parent heterosis and control heterosis and Euclidean distance or Cheng’s index difference value. However, it was no significant correlation for yield. Further analysis found a common interval, 3.41 - 3.46 for Euclidean distance and 3 - 4 for cheng’s index difference value of parents, which was significant or high significant positive correlated with phenotype value, super male parent and control heterosis of main yield traits. This illustrates that the larger the genetic difference of parents was, the stronger the heterosis combinations were, when the genetic differences of parents were in an appropriate range.

Understanding the spatial representativeness of air quality monitoring network and its application to PM_(2.5)in the China's Mainland

Air pollution has seriously endangered human health and the natural ecosystem during the last decades.Air quality monitoring stations(AQMS)have played a critical role in providing valuable data sets for recording regional air pollutants.The spatial representativeness of AQMS is a critical parameter when choosing the location of stations and assessing effects on the population to long-term exposure to air pollution.In this paper,we proposed a methodological framework for assessing the spatial representativeness of the regional air quality monitoring network and applied it to ground-based PM_(2.5)observation in the mainland of China.Weighted multidimensional Euclidean distance between each pixel and the stations was used to determine the representativeness of the existing monitoring network.In addition,the K-means clustering method was adopted to improve the spatial representativeness of the existing AQMS.The results showed that there were obvious differences among the representative area of 1820 stations in the mainland of China.The monitoring stations could well represent the PM_(2.5)spatial distribution of the entire region,and the effectively represented area(i.e.the area where the Euclidean distance between the pixels and the stations was lower than the average value)accounted for 67.32%of the total area and covered 93.12%of the population.Forty additional stations were identified in the Northwest,North China,and Northeast regions,which could improve the spatial representativeness by 14.31%.

Phishing Detection with Image Retrieval Based on Improved Texton Correlation Descriptor

Anti-detection is becoming as an emerging challenge for anti-phishing.This paper solves the threats of anti-detection from the threshold setting condition.Enough webpages are considered to complicate threshold setting condition when the threshold is settled.According to the common visual behavior which is easily attracted by the salient region of webpages,image retrieval methods based on texton correlation descriptor(TCD)are improved to obtain enough webpages which have similarity in the salient region for the images of webpages.There are two steps for improving TCD which has advantage of recognizing the salient region of images:(1)This paper proposed Weighted Euclidean Distance based on neighborhood location(NLW-Euclidean distance)and double cross windows,and combine them to solve the problems in TCD;(2)Space structure is introduced to map the image set to Euclid space so that similarity relation among images can be used to complicate threshold setting conditions.Experimental results show that the proposed method can improve the effectiveness of anti-phishing and make the system more stable,and significantly reduce the possibilities of being hacked to be used as mining systems for blockchain.

Land use induced blurring of forest-grassland transition in north-west Himalaya-A case study using Moving Split Window boundary detection technique

Transition zone or ecotone is a unique community sandwiched between two communities/ecosystems/biomes.These ecotones in Himalaya remained unexplored for many ecological aspects like biodiversity,phyto-sociology,boundary detection and even impact of change in land use pattern(anthropogenic activity).The most accepted and widespread technique called as Moving Split Window(MSW) technique is used for detection of vegetation and environmental boundaries at four different sites in the lesser stratum of north-west Himalaya.All the four sites were at different distances from the nearest human inhabited area.Anthropogenic activities like grazing,herb collection,wood collection etc.were common at proximal sites.Such activities have led to the change in land use pattern.In this study,we have tried to work out the impact of the change in land use pattern(human interference) on the vegetation and basic environmental parameters like soil pH,electrical conductivity and moisture on forestgrassland ecotone in north-west Himalaya.Data on mountain steepness was also collected and analyzed.The dissimilarity profile using the statistical tool Squared Euclidian Distance(SED) indicated that species turnover locations increase with the increase in distance of ecotones from human settlements.The ecotones at distant locations from human villages are characterized with blunt as well as sharp peaks for vegetation data,however,conditions are reverse in case of the proximal sites.The study also reveals that as the distance between the ecotone and human settlements increases,the complex conditions like multiple vegetation boundaries prevails on the transitions.In this regard,land use induced blurring of forest-grassland transition in north-west Himalaya is summed up in the study.

Novel Mapping Design Criterion for BICM-ID with Square QAM Constellations

Mapping design criteria of bit-interleaved coded modulation with iterative decoding (BICM-ID) with square 16QAM are analyzed. Three of the existing criteria are analyzed and compared with each other. Through the comparison, two main characters of the mapping design criteria are found. They are the harmonic mean of the minimum squared Euclidean distance and the average of Hamming distances with the nearest Euclidean distance. Based on these two characters, a novel mapping design criterion is proposed and a label mapping named mixed mapping is searched according to it. Simulation results show that mixed mapping performs better than the other mappings in BICM-ID system.

A Kernel-Based Nonlinear Representor with Application to Eigenface Classification

This paper presents a classifier named kernel-based nonlinear representor (KNR) for optimal representation of pattern features. Adopting the Gaussian kernel, with the kernel width adaptively estimated by a simple technique, it is applied to eigenface classification. Experimental results on the ORL face database show that it improves performance by around 6 points, in classification rate, over the Euclidean distance classifier.

A Normalization Method of Moment Invariants for 3D Objects on Different Manifolds

3D objects can be stored in computer of different describing ways, such as point set, polyline, polygonal surface and Euclidean distance map. Moment invariants of different orders may have the different magnitude. A method for normalizing moments of 3D objects is proposed, which can set the values of moments of different orders roughly in the same range and be applied to different 3D data formats universally. Then accurate computation of moments for several objects is presented and experiments show that this kind of normalization is very useful for moment invariants in 3D objects analysis and recognition.

Phenotypic and molecular diversity-based prediction of heterosis in pearl millet(Pennisetum glaucum L.(R.)Br.)

Genetic distances between hybrid parents based on phenotypic traits and molecular markers were investigated to assess their relationship with heterosis for grain and stover yield and other traits in pearl millet(Pennisetum glaucum [L.] R. Br.). Fifty-one hybrids developed using 101 hybrid parents(B and R lines) and showing a wide range of genetic distance between their parents based on eight phenotypic traits and 28–38 SSRs were evaluated in two sets for two seasons. The correlation between Euclidean distance(phenotypic distance, ED) and simple matching distance(molecular distance, SM) for parents of both sets was low but positive and significant(r = 0.2, P < 0.001).The correlation of ED in parents with better-parent heterosis for grain yield was similar in both sets(r =0.38, P < 0.05). SM was not correlated with heterosis for grain yield in either set of hybrids.The results showed that phenotypic distance could be a better predictor of heterosis than molecular distance. The correlation between phenotypic distance and heterosis was not strong enough to permit the use of phenotypic diversity among parents as a major selection criterion for selection of parental lines displaying high levels of heterosis for grain and stover yield in pearl millet.

An automated detection of glaucoma using histogram features

Glaucoma is a chronic and progressive optic neurodegenerative disease leading to vision deterioration and in most cases produce increased pressure within the eye. This is due to the backup of fluid in the eye; it causes damage to the optic nerve. Hence, early detection diagnosis and treatment of an eye help to prevent the loss of vision. In this paper, a novel method is proposed for the early detection of glaucoma using a combination of magnitude and phase features from the digital fundus images. Local binary patterns(LBP) and Daugman’s algorithm are used to perform the feature set extraction.The histogram features are computed for both the magnitude and phase components. The Euclidean distance between the feature vectors are analyzed to predict glaucoma. The performance of the proposed method is compared with the higher order spectra(HOS)features in terms of sensitivity, specificity, classification accuracy and execution time. The proposed system results 95.45% output for sensitivity, specificity and classification. Also, the execution time for the proposed method takes lesser time than the existing method which is based on HOS features. Hence, the proposed system is accurate, reliable and robust than the existing approach to predict the glaucoma features.

Prospect Theory Based Hesitant Fuzzy Multi-Criteria Decision Making for Low Sulphur Fuel of Maritime Transportation

The environmental impact of maritime transport has now become a relevant issue in sustainable policy formulation and has attracted increasing interest from academia.For the sustainable development of maritime transport,International Maritime Organization stipulates that the sulfur content of ship emissions will reach 0.5 from 2020.With the approaching of the stipulated implementation date,shipowners need to adopt scientific methods to make decision on low sulfur fuel.In this study,we applied a prospect theory based hesitant fuzzy multi-criteria decision-making model to obtain the optimal decision of low Sulphur marine fuel.For this purpose,the hesitant fuzzy decision matrix is established to collect expert opinions,the maximizing deviation method is adopted to determine criteria weights.According to calculate the Euclidean distance from the reference points,we obtain the comprehensive prospect values of alternatives.Lastly,a case study is carried out to illustrate the significance and effectiveness of the proposed methodology.The innovation of this study is that it is the first-time adopting prospect theory and hesitate fuzzy sets to multi-criteria decision making for low Sulphur marine fuel,which provides an effective decision model for shipping companies under Low Sulphur regulations,and can also be extended to other industries.

Radial-curve-based facial expression recognition

A fully automatic facial-expression recognition （FER） system on 3D expression mesh models was proposed. The system didn＇ t need human interaction from the feature extraction stage till the facial expression classification stage. The features extracted from a 3D expression mesh mod- el were a bunch of radial facial curves to represent the spatial deformation of the geometry features on human face. Each facial curve was a surface line on the 3D face mesh model, begun from the nose tip and ended at the boundary of the previously trimmed 3D face points cloud. Then Euclid dis- tance was employed to calculate the difference between facial curves extracted from the neutral face and each face with different expressions of one person as feature. By employing support vector ma- chine （SVM） as classifier, the experimental results on the well-known 3D-BUFE dataset indicate that the proposed system could better classify the six prototypical facial expressions than state-of-art al- gorithms.

Discrimination between Dementia Groups and Healthy Elderlies Using Scalp-Recorded-EEG-Based Brain Functional Connectivity Networks

Objective: To establish a practical method for discriminating dementia groups and healthy elderlies, by using scalp-recorded electroencephalograms (EEGs). Methods: 16-ch EEGs were recorded during resting state for 39 dementia groups and 11 healthy elderlies. The connectivity between any two electrodes was estimated by synchronization likelihood (SL). The brain networks were constructed by normalized SL values. The present leave-one-out cross validation (LOOCV) required the Euclidean distance between any two subjects having 120-dimensional vectors concerned with the SL values for six frequency bands. In order to investigate factors which would affect the LOOCV results, principal component analysis (PCA) was applied to all the subjects. Results: The accuracy for the upper alpha yielded more than 80% and 70% in the dementia groups and the healthy elderlies, respectively. The LOOCV result could be explained in terms of brain networks such as executive control network (ECN) and default mode network (DMN) characterized by factor loadings of principal components. Conclusions: Dementia groups and healthy elderlies could be characterized by principal components of SL values between all the electrode pairs, even less connections, which revealed disruption and preservation of DMN and ECN. Significance: This study will provide a simple and practical method for discriminating dementia groups from healthy elderlies by scalp-recorded EEGs.

Four-Dimensional Signal Constellations Based on Binary Frequency-Shift Keying and <i>M</i>-ary Amplitude-Phase-Shift Keying

In this article, we give the construction of new four-dimensional signal constellations in the Euclidean space, which represent a certain combination of binary frequency-shift keying (BFSK) and M-ary amplitude-phase-shift keying (MAPSK). Description of such signals and the formulas for calculating the minimum squared Euclidean distance are presented. We have developed an analytic building method for even and odd values of M. Hence, no computer search and no heuristic methods are required. The new optimized BFSK-MAPSK (M = 5,6,···,16) signal constructions are built for the values of modulation indexes h =0.1,0.15,···,0.5 and their parameters are given. The results of computer simulations are also provided. Based on the obtained results we can conclude, that BFSK-MAPSK systems outperform similar four-dimensional systems both in terms of minimum squared Euclidean distance and simulated symbol error rate.