Information mining and similarity computation for semi-/un-structured sentences from the social data

In recent years,with the development of the social Internet of Things(IoT),all kinds of data accumulated on the network.These data,which contain a lot of social information and opinions.However,these data are rarely fully analyzed,which is a major obstacle to the intelligent development of the social IoT.In this paper,we propose a sentence similarity analysis model to analyze the similarity in people’s opinions on hot topics in social media and news pages.Most of these data are unstructured or semi-structured sentences,so the accuracy of sentence similarity analysis largely determines the model’s performance.For the purpose of improving accuracy,we propose a novel method of sentence similarity computation to extract the syntactic and semantic information of the semi-structured and unstructured sentences.We mainly consider the subjects,predicates and objects of sentence pairs and use Stanford Parser to classify the dependency relation triples to calculate the syntactic and semantic similarity between two sentences.Finally,we verify the performance of the model with the Microsoft Research Paraphrase Corpus(MRPC),which consists of 4076 pairs of training sentences and 1725 pairs of test sentences,and most of the data came from the news of social data.Extensive simulations demonstrate that our method outperforms other state-of-the-art methods regarding the correlation coefficient and the mean deviation.

Design of ontology mapping framework and improvement of similarity computation

Ontology heterogeneity is the primary obstacle for interoperation of ontologies. Ontology mapping is the best way to solve this problem. The key of ontology mapping is the similarity computation. At present, the method of similarity computation is imperfect. And the computation quantity is high. To solve these problems, an ontology-mapping framework with a kind of hybrid architecture is put forward, with an improvement in the method of similarity computation. Different areas have different local ontologies. Two ontologies are taken as examples, to explain the specific mapping framework and improved method of similarity computation. These two ontologies are about classes and teachers in a university. The experimental results show that using this framework and improved method can increase the accuracy of computation to a certain extent. Otherwise, the quantity of computation can be decreased.

Design and Implementation of FAQ Automatic Return System Based on Similarity Computation

FAQ （frequently asked question） is widely used on the Internet, but most FAQ＇s asking and answering are not automatic. This paper introduces the design and imple mentation of a FAQ automatic return system based on semantic similarity computation, including computation model choo sing, FAQ characters analyzing, FAQ data formal expressing, feature vector indexing, and weight computing and so on. According to FAQ features of sentence length short, two mapping, strong domain characteristics etc. Vector Space Model with special semantic process was selected in system, and corresponding algorithm of similarity computation was proposed too. Experiment shows that the system has a good performance for high frequent and common questions.

Hadoop-Based Similarity Computation System for Composed Documents

There exist a large number of composed documents in universities in the teaching process. Most of them are required to check the similarity for validation. A kind of similarity computation system is constructed for composed documents with images and text information. Firstly, each document is split and outputs two parts as images and text information. Then, these documents are compared by computing the similarities of images and text contents independently. Through Hadoop system, the text contents are easily and quickly separated. Experimental results show that the proposed system is efficient and practical.

A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

The Importance of the Similarity of Profiles of Companies-Partners in the Process of Post-transaction Integration

The aim of this article is to find an answer to the question： Does the similarity of the profiles of companies participating in the acquisition influence the post-transaction integration？ While creating a research concept, a hypothesis was adopted that the range and the dynamics of the post-acquisition integration are higher, if the companies are similar as far as their sizes, structures, life cycles, and organizational cultures are concerned. Five acquisitions from the pharmaceutical sectors were examined. The results presented in this paper are a part of wider research on consolidation processes in the pharmaceutical branch. In each case, the research had a retrospective character. In order to assess the integration activities, the research was conducted three years after the transaction. An inductive analysis of case study type was used and the technique of research triangulation was applied. A part of the research used for the needs of this paper was conducted by the means of two methods： individual in-depth interviews with the presidents/managing directors of the companies which were taken over and panel interviews in which the presidents and managing directors as well as director and managers of the development units, managers of organization as well as the directors/managers of human resource units of the acquired companies participated. The analysis of the profiles of similarity of companies-transaction partners showed that although they belonged to the same branch, the analyzed companies are characterized by a low level of similarity, especially as far as the structures and organizational cultures are concerned. The strategy of functioning after the merger is based in the majority of cases on the model of partnership. It takes for granted a low level of integration referring only to some selected areas and is connected with an adoption of a long-term integration perspective foreseen for a period longer than three years of functioning in the merged structure.

Ontological similarity network reasoning framework

To properly compute the ontological similarity, an ontological similarity network-based reasoning framework is proposed. It structurally integrates extension-based approach, intension-based approach, the similarity network-based reasoning to exploit the implicit similarity, and the feedback from the context to validate the similarity measures. A new similarity measure is also presented to construct concept similarity network, which scales the similarity using the relative depth of the least common super-concept between any two concepts. Subsequently, the graph theory, instead of predefined knowledge rules, is applied to perform the similarity network-based reasoning such that the knowledge acquisition can be avoided. The framework has been applied to text categorization and visualization of high dimensional data. Theory analysis and the experimental results validate the proposed framework.

K-Similarity降噪的LSTM神经网络水质多因子预测模型

针对水质预测问题,以地表水水质监测因子作为研究对象,提出了一种基于长短期记忆(LSTM)神经网络的水质多因子预测模型,同时利用提出的K-Similarity降噪法对模型的输入数据进行降噪,提高模型预测性能.通过与BP神经网络、RNN和传统的LSTM神经网络预测模型进行对比实验,证明了所提出的方法均方误差最小,预测结果更准确.

SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data

The growing collection of scientific data in various web repositories is referred to as Scientific Big Data,as it fulfills the four“V’s”of Big Data—volume,variety,velocity,and veracity.This phenomenon has created new opportunities for startups;for instance,the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs.Traditionally,the content of the papers are compared to list the relevant papers from a repository.The conventional method results in a long list of papers that is often impossible to interpret productively.Therefore,the need for a novel approach that intelligently utilizes the available data is imminent.Moreover,the primary element of the scientific knowledge base is a research article,which consists of various logical sections such as the Abstract,Introduction,Related Work,Methodology,Results,and Conclusion.Thus,this study utilizes these logical sections of research articles,because they hold significant potential in finding relevant papers.In this study,comprehensive experiments were performed to determine the role of the logical sections-based terms indexing method in improving the quality of results(i.e.,retrieving relevant papers).Therefore,we proposed,implemented,and evaluated the logical sections-based content comparisons method to address the research objective with a standard method of indexing terms.The section-based approach outperformed the standard content-based approach in identifying relevant documents from all classified topics of computer science.Overall,the proposed approach extracted 14%more relevant results from the entire dataset.As the experimental results suggested that employing a finer content similarity technique improved the quality of results,the proposed approach has led the foundation of knowledge-based startups.

Similarity Reduction and Integrability for the Nonlinear Wave Equations from EPM Model

Four types of similarity reductions are obtained for the nonlinear wave equation arising in the elasto-plasticmicrostructure model by using both the direct method due to Clarkson and Kruskal and the improved direct method due to Lou. As a result, the nonlinear wave equation is not integrable.

A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment

In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly,a multi-expert system consisting of color component dispersion,similarity and centroid motion is established to identify flames.The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.

A New Method for Calculating Similarity between Sentences and Application on Automatic Abstracting

Sentence similarity computing plays an important role in machine question-answering systems, machine-translation systems, information retrieval and automatic abstracting systems. This article firstly sums up several methods for calculating similarity between sentences, and brings out a new method which takes all factors into consideration including critical words, semantic information, sentential form and sen-tence length. And on this basis, a automatic abstracting system based on LexRank algorithm is implemented. We made several improvements in both sentence weight computing and redundancy resolution. The system described in this article could deal with single or multi-document summarization both in English and Chinese. With evaluations on two corpuses, our system could produce better summaries to a certain degree. We also show that our system is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents. And in the end, existing problem and the developing trend of automatic summariza-tion technology are discussed.

Four-Dimensional Computerized Tomography (4D-CT) Reconstruction Based on the Similarity Measure of Spatial Adjacent Images

Objective:To investigate the feasibility of a 4D-CT reconstruction method based on the similarity principle of spatial adjacent images and mutual information measure. Methods:A motor driven sinusoidal motion platform made in house was used to create one-dimensional periodical motion that was along the longitudinal axis of the CT couch. The amplitude of sinusoidal motion was set to an amplitude of ±1 cm. The period of the motion was adjustable and set to 3.5 s. Phantom objects of two eggs were placed in a Styrofoam block, which in turn were placed on the motion platform. These objects were used to simulate volumes of interest undergoing ideal periodic motion. CT data of static phantom were acquired using a multi-slice general electric (GE) LightSpeed 16-slice CT scanner in an axial mode. And the CT data of periodical motion phantom were acquired in an axial and cine-mode scan. A software program was developed by using VC++ and VTK software tools to resort the CT data and reconstruct the 4D-CT. Then all of the CT data with same phase were sorted by the program into the same series based on the similarity principle of spatial adjacent images and mutual information measure among them, and 3D reconstruction of different phase CT data were completed by using the software. Results:All of the CT data were sorted accurately into different series based on the similarity principle of spatial adjacent images and mutual information measures among them. Compared with the unsorted CT data, the motion artifacts in the 3D reconstruction of sorted CT data were reduced significantly, and all of the sorted CT series result in a 4D-CT that reflected the characteristic of the periodical motion phantom. Conclusion:Time-resolved 4D-CT reconstruction can be implemented with any general multi-slice CT scanners based on the similarity principle of spatial adjacent images and mutual information measure.The process of the 4D-CT data acquisition and reconstruction were not restricted to the hardware or software of the CT scanner and has the feasibility ,which extensive applicability.

Integrable System and Motion of Curves in Projective and Similarity Geometries

Based on the natural frame in the projective geometry, motions of curves in projective geometry are studied. It is shown that several integrable equations including Sawada-Kotera and KK equations arise from motion of plane curves in projective geometries. Motion of space curves described by acceleratlon field and governed by endowing an extra space variable in similarity geometry P^3 is also studied.

Gershgorin and Rayleigh Bounds on the Eigenvalues of the Finite-Element Global Matrices via Optimal Similarity Transformations

The large finite element global stiffness matrix is an algebraic, discreet, even-order, differential operator of zero row sums. Direct application of the, practically convenient, readily applied, Gershgorin’s eigenvalue bounding theorem to this matrix inherently fails to foresee its positive definiteness, predictably, and routinely failing to produce a nontrivial lower bound on the least eigenvalue of this, theoretically assured to be positive definite, matrix. Considered here are practical methods for producing an optimal similarity transformation for the finite-elements global stiffness matrix, following which non trivial, realistic, lower bounds on the least eigenvalue can be located, then further improved. The technique is restricted here to the common case of a global stiffness matrix having only non-positive off-diagonal entries. For such a matrix application of the Gershgorin bounding method may be carried out by a mere matrix vector multiplication.

Semantic Similarity over Gene Ontology for Multi-Label Protein Subcellular Localization

As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.

New Thinking of Writing Teaching Based on Similarity Theory

The accumulation and reorganization of“article similar blocks”constitute people’s writing intelligence,which directly determines the writing ability of the article.The teaching of writing should focus on the core of students’writing intelligence,the construction of“article similarity block”,and the teaching should be organized and implemented according to the law of human brain thinking.The necessary screening and integration of similar blocks is the primary premise for the construction of“similar blocks”of the writing subject.It is undoubtedly an important task for writing teaching to construct these“similar blocks”in the cognitive structure of the subject.Article similarity block is always in a static state in the human brain.Only similarity activation can awaken and activate the sleeping“similarity block”,promote coupling resonance,and move towards similarity innovation.

Group Similarity and Social Influence Analysis in Online Communities

A fundamental open question in the analysis of social networks was to understand the evolution between similarity and group social ties.In general,two groups are similar for two distinct reasons:first,they grow to change their behaviors to the same group due to social influence;second,they tend to merge a group due to similar behaviors,where a process often is termed selection by sociologists.It was important to understand why two groups could merge and what led to high similarities for members in a group,influence or selection.In this paper,the techniques for identifying and modeling interactions between social influence and selection for different groups were developed.Different similarities were computed in three phases where groups came into being,before or after according to the number of common edits in Wikipedia.Experimental results showed selection played a more important role in two group merging.

Similarity Intelligence:Similarity Based Reasoning,Computing,and Analytics

Similarity has been playing an important role in computer science,artificial intelligence(AI)and data science.However,similarity intelligence has been ignored in these disciplines.Similarity intelligence is a process of discovering intelligence through similarity.This article will explore similarity intelligence,similarity-based reasoning,similarity computing and analytics.More specifically,this article looks at the similarity as an intelligence and its impact on a few areas in the real world.It explores similarity intelligence accompanying experience-based intelligence,knowledge-based intelligence,and data-based intelligence to play an important role in computer science,AI,and data science.This article explores similarity-based reasoning(SBR)and proposes three similarity-based inference rules.It then examines similarity computing and analytics,and a multiagent SBR system.The main contributions of this article are:1)Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence.2)Similarity-based reasoning,computing and analytics can be used to create similarity intelligence.The proposed approach will facilitate research and development of similarity intelligence,similarity computing and analytics,machine learning and case-based reasoning.

Hans A.Panofsky’s Integral Similarity Function—At Fifty

Fifty years ago, Hans A. Panofsky published a paper entitled Determination of stress from wind and temperature measurements. In his famous paper, he presented a new profile function for the mean horizontal wind speed under the condition of diabatic stratification that includes his integral similarity function. With his integral similarity function, he opened the door for Monin-Obukhov scaling in a wide range of micrometeorological and microclimatological applications. In a historic survey ranging from the sixties of the past century down to the present days, we present integral similarity functions for momentum, sensible heat, and water vapor for both unstable and stable stratification, where on the one hand free convection condition and on the other hand strongly stable stratification are addressed.