Vector Space Method Research Articles

A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains

Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.

Accurate medical information recommendation system based on big data analysis

In order to solve the problems of low recommendation accuracy and long response time in traditional medical information recommendation system, a medical information accurate-recommendation system based on big data analysis is proposed. The system is designed as medical information data acquisition module, medical information storage module and medical information accurate-recommendation module. In the medical information data acquisition module, crawler technology is used to obtain medical information data, and association rule algorithm is used to mine the medical information data. In the medical information storage module, personalised configuration is set. In the medical information accurate-recommendation module, the user interest model is quantified by vector space method, and BP algorithm and SOM algorithm are introduced to complete the accuracy of medical information recommend. The experimental results show that: the highest accuracy rate of medical information recommendation is 98.8%, and the shortest retrieval response time is 20 ms.

Текстовая и дискурсивная лингвокреативность детской приключенческой повести в англоязычной литературе

The article explores the specificity of linguistic creativity in the discourse of children's English-language adventure fiction of the 1950s. The aim of the research is to develop the parametrization and vector-space method of discourse and text linguistic creativity assessment to evaluate the linguistic creativity potential of individual texts displaying similar discourse features. To serve as the research data three discourse fragments were selected, which represent three basic narrative types, Orientation, Complicating Actions, Evaluation and Resolution. To achieve the aim, the author applies the procedure of parametrization analysis followed by general and analytic statistics analysis and vector-space modelling. With the system of 52 parameters featuring linguistic creativity in phonology, word-formation, morphology, lexicology and phraseology, syntax, and graphics, the author manually annotates and processes the discourse fragments of similar size exemplifying three narrative types of adventure fiction literature, with the total sample size of 55,000 characters. General statistics analysis allowed revealing the absolute and relative parameter values in three discourse fragments and defining the relative parametric activity of single parameters and parameter levels. Analysis of variance helped define the correlation indices of parameter paired combinations, which resulted in detecting significant binary parameter groups . Individual parameter values and their binary groups served to construe the vector-space models of discourse and text linguistic creativity for the discourse narrative types under consideration. Thus, the author obtained an efficient instrument for discourse linguistic creativity evaluation and, furthermore, for assessing the potential of each individual text in terms of displaying stronger or weaker correlation with the vector coordinates of the discourse linguistic creativity vector-space model. With the frequency and variance analysis, the author disclosed two types of discourse linguistic creativity performance techniques, that is the individual parameter activation and the parameter synchronization. Both must be considered when the decision on linguistic creativity assessment in a concrete text is made. The resulting model shows that the parameter values of linguistic creativity in individual texts can manifest themselves in appearing both higher and lower than the reference parameter values of discourse creativity, which can contribute to disclosing new directions in creativity processing and understanding.

Improved VSM Based Candidate Retrieval Model for Detecting External Textual Plagiarism

A rapid growth has occurred for the act of plagiarism with the aid of Internet explosive growth wherein a massive volume of information offered with effortless use and access makes plagiarism the process of taking someone else’s work (represented by ideas, or even words) and representing it as other's own work easy to be performed. For ensuring originality, detecting plagiarism has been massively necessitated in various areas so that the people who aim to plagiarize ought to offer considerable effort for introducing works centered on their research.
 In this paper, work has been proposed for improving the detection of textual plagiarism through proposing a model for candidate retrieval phase. The model proposed for retrieving candidates has adopted the vector space method VSM as a retrieval model and centered on representing documents as vectors consisting of average term weights and considering them as queries for retrieval instead of representing them as vectors of term weight. The detailed comparison task comes as the second phase wherein fuzzy semantic based string similarity has been applied. Experiments have been conducted using PAN-PC-10 as an evaluation dataset for evaluating the proposed system. As the problem statement in this paper is restricted to detect extrinsic plagiarism and works on English documents, experiments have been performed on the portion dedicated to extrinsic detection and on documents in English language only. For evaluating performance of the proposed model for retrieving candidates, Precision, Recall, and F-measure have been used as an evaluation metrics. The overall performance of the proposed system has been assessed through the use of the five standard PAN measures Precision, Recall, F-measure, Granularity and . The experimental results have clarified that the proposed model for retrieving candidates has a positive impact on the overall performance of the system and the system outperforms the other state-of-the-art methods. They clarified that the proposed model has detected about 80% of the plagiarism cases and about 90% of the detections were correct. The proposed model has the ability to detect literal plagiarism in addition to cases containing paraphrasing. Performance comparison has clarified that the proposed system is either comparable or outperforms the other baseline systems in terms of the five evaluation metrics.

Enhanced Security for Dynamic Multi Keyword Ranked Search Using Greedy Best First Search

Today, due to the enormous growth of data technology in cloud computing, the data owners are stimulated to outsource their data in data management to reduce cost and for the convenient. Data confidentiality, in general, can be obtained by encrypting the data before it is outsourced. The client stores the encrypted data to the cloud using Searchable encryption schemes and applies keyword search techniques over cipher text domain. But the main problem in outsourcing is the lack of security and privacy for the sensitive data. So, to overcome this, for privacy requirement, the sensitive data can be encrypted before it is outsourced. Various methods were proposed to preserve the privacy and to provide security to the cloud data which are encrypted. Here in this paper, we proposed a tree-based search method over the encrypted datain the cloud that supports dynamic operation and multi-keyword ranked search. Clearly, the commonly used “inverse document frequency (IDF) term frequency (TF)” model and the vector space method are joined in the query generation and index creation to give multi-keyword ranked search. To get high search efficiency, a tree-type index structure, “Greedy Best-first Search” algorithm is proposed based on the tree- index.

Design and Implementation of Chinese Information Filtering System

With the development of the network information, information processing is vital in all aspects. Information filtering is more important research aspect. Especially Chinese information filtering is urgent affairs. According to research of the domestic and abroad, in the article vector space method and hyB+ tree index method is combined to filter text. Experimental results show that, this method is feasible.

SU-E-T-726: Validation of ProACTive, an Irreducible Vector Space Method of Dose Calculation

Purpose: Monte Carlo dose calculations are considered to be the gold standard by which other techniques are compared, particularly for complex geometries with regions of electronic disequilibrium. The major disadvantage of Monte Carlo is the associated long calculation times. This necessitates limiting the number of histories and/or using interpolative techniques, potentially reducing accuracy. ProACTive is an irreducible vector space method of dose calculation with computation times significantly faster than Monte Carlo. The purpose of the work is to validate ProACTiveˈs accuracy.Method: ProACTive calculations were compared to measurement and/or EGS4/PRESTA Monte Carlo for both homogenous and heterogeneous geometries for 6MV and 18MV. Sufficient Monte Carlo histories were run to minimize statistical uncertainty. Depth dose and profile measurements were taken from an average of measurements obtained on 40 Varian linear accelerators commissioned in the last several years. Heterogeneous phantoms included a complex phantom consisting of water, aluminum and lung irradiated by a 2×2 cm2 18 MV x-ray beam as described by Mohan and Rogers (ICCR2000 Proceedings). Results: ProACTive calculation times were significantly faster than Monte Carlo run with a large number of histories. For homogenous geometries, ProACTive calculations showed good agreement with measurement for a wide range of depths and beam sizes, both on and off axis. For the ICCR2000 phantom, ProACTive and Monte Carlo showed overall good agreement. Both calculation methods showed some difference compared to measurement, especially at a density boundary. It is not clear whether the differences are real or due to uncertainty in the measurements and their interpretation for this extreme geometry. Conclusions: For the geometries tested, ProACTive shows good agreement with measurement and similar computational accuracy to Monte Carlo when the latter is run with sufficient histories to minimize statistical uncertainty. ProACTiveˈs accuracy and computational time make it a feasible clinical solution to accurate dose calculation.

An analysis of latent semantic term self-correlation

Latent semantic analysis (LSA) is a generalized vector space method that uses dimension reduction to generate term correlations for use during the information retrieval process. We hypothesized that even though the dimension reduction establishes correlations between terms, the dimension reduction is causing a degradation in the correlation of a term to itself (self-correlation). In this article, we have proven that there is a direct relationship to the size of the LSA dimension reduction and the LSA self-correlation. We have also shown that by altering the LSA term self-correlations we gain a substantial increase in precision, while also reducing the computation required during the information retrieval process.

Perspective for Extended Group-Contribution Methods for the Prediction of Activity Coefficients Accounting for Sterical Effects. Linking UNIFAC and the Group Vector Space Method

Group contribution methods for the calculation of an activity coefficient such as UNIFAC (universal factorial activity coefficient) currently do not take into account sterical effects. Consequently one of the major deficiencies of such methods is their inability to resolve isomeric systems and their often insufficient accuracy for systems including molecules of very different size. In this paper an approach is presented by which sterical information can be implemented into those methods by means of a modified group vector space (GVS) procedure. (Wen, X.; Qiang, Y. Group Vector Space method for estimating melting and boiling points of organic compounds. Ind. Eng. Chem. Res. 2002, 41, 5534−5537.) Exemplary calculations with UNIFAC/GVS underline the potential of this new approach resulting in a perspective for substantially improved group contributions methods with a wider range of applicability and even more reliable predictive performance.

Composite group vector space method for estimating melting and boiling point of pure organic compound

Composition Group Vector Space (CGVS) method for estimating melting and boiling point Tm, Tb of organic compound has been proposed, and the principle of this method has been elucidated. The models for estimating Tm, Tb have been established and the numerical values of relative parameters have been presented. The average percentage deviations of Tm, Tb estimation are 7.53 and 1.58, respectively, which show that the present method demonstrates significant improvement in applicability to predict the above properties, compared to conventional group methods.

Peer-to-Peer Non-document Content Searching Method Using User Evaluation of Semantic Vectors

With today's advances in peer-to-peer (P2P) techniques, a lot of non-document content has become searchable and usable. In the near future, since a huge amount of content will be distributed over the networks, not only index server searching but also P2P searching will become important because of its scalability and robustness. Typical P2P content searching services have some problems, such as low search precision ratio, significant increase in traffic and inundations of malicious content such as viruses. We propose a P2P content searching method in which a query is effectively forwarded only to peers that have indices of content semantically similar to the wanted content but not forwarded to the same peer repeatedly. It is based on the ideas of content addressable network (CAN) topology and a vector space method where vectors have a variable length. It maps non-document content to a vector space based on users' evaluations and manages the vector space or routes queries using the CAN topology control. The effectiveness of our method is shown by both analytical estimations and simulation experiments. The simulations clarified that our method is effective at improving the precision and recall ratios while reducing the amount of traffic compared with Gnutella flooding, the vector space method in which vector lengths are fixed (similar to the pSearch method), and Chord. In particular, when there was a lot of malicious content, our method exhibited a higher precision ratio than other methods.

P2P Content Searching Method using Semantic Vector which is Managed on CAN Topoogy

With today's advances in Peer-to-Peer (P2P) searching technology, a lot of non-document content has become searchable and usable. In the near future, since a huge amount of content is distributed over the networks, not only index server searching but also P2P searching will become important because of its scalability and robustness. Typical P2P contents sharing services have some problems, such as low search precision ratio, significant increase in traffic and inundations of malicious content such as virus. In this paper, with ideas of the CAN (Content Addressable Network) topology and a vector space method where vectors have a variable length, we propose a new P2P content searching method in which a query is effectively forwarded only to peers that have indices of content semantically similar to the desired one but not forwarded to the same peer repeatedly. The main part of our proposal is to map non-document content to a vector space based on users' evaluation and to manage vector space or to route queries using the CAN topology control. The effectiveness of the proposal is shown both by analytical estimations and simulation experiments. Our simulation experiments clarify that the proposed method is effective in improving the precision and recall ratios while reducing the amount of traffic compared with the Gnutella flooding and the vector space method in which vector lengths are fixed (close to pSearch method). In particular, when there is a lot of malicious content, the proposed method exhibited a higher precision ratio than other methods.

Processing halftone color images by vector space methods

The reproduction of color images by color halftoning can be characterized by the Neugebauer model/equation. However, the Neugebauer equation is not easy to solve because of the highly nonlinear relationship between the underlying Neugebauer primaries and the colorants. We attempt to solve the Neugebauer equation by vector space methods. The proposed method of solution is applicable to any number of colorants, although our experimental results are confined to the CMY and CMYK cases. Among the constraints we consider are those related to a bound on the permissible amount of total ink and a bound on the total cost of applying colorants to achieve a satisfactory level of color reproduction. Our results demonstrate that the vector space method is a feasible approach for solving for the required amounts of colorants in the constrained color halftoning problem.

Estimation of boiling and melting points of light, heavy and complex hydrocarbons by means of a modified group vector space method

A modified group vector space (GVS) method was developed for estimating the normal boiling points and melting points of alkanes, alkenes, alkynes, and cyclic and aromatic hydrocarbons including their isomers. The present method, based on group contributions as well as topological contributions, can represent the normal boiling points of isomeric compounds accurately. The group parameters for the modified GVS method were obtained from the correlation of the boiling and melting points of 1115 hydrocarbons.

Composite group vector space method for estimating critical properties of pure organic compound

Composite Group Vector Space (CGVS) method for estimating critical properties T c, P c, V c of organic compounds has been proposed and principle of this method has been elucidated. Expressions for estimating T c, P c, V c have been established with numerical values of relative parameters obtained. The average percent deviations of T c, P c, V c estimation are 0.67, 2.09 and 1.39, respectively, which show that present method demonstrates significant improvement in applicability to predict the above properties, compared the conventional group methods.

Group Vector Space Method for Estimating Enthalpy of Vaporization of Organic Compounds at the Normal Boiling Point.

Group Vector Space Method for Estimating Enthalpy of Vaporization of Organic Compounds at the Normal Boiling Point.

Group Vector Space Method for Estimating the Normal Boiling Temperature and Enthalpy of Vaporization of Organic Compounds

The specific position of a group in the molecule has been considered, and a group vector space method has been developed to estimate the normal boiling temperature and enthalpy of vaporization of organic compounds. Expressions for the boiling temperature T b and enthalpy of vaporization Δ vap H(T b ) have been established, and numeric values of relative group parameters have been obtained. The average percent deviations of the estimation of T b andΔ va pH(T b ) are 1.5 and 1.0, respectively, which show that the present method demonstrates significant improvement in the ability to predict the above properties, compared to that of conventional group methods.

Group vector space method for estimating enthalpy of vaporization of organic compounds at the normal boiling point.

The specific position of a group in the molecule has been considered, and a group vector space method for estimating enthalpy of vaporization at the normal boiling point of organic compounds has been developed. Expression for enthalpy of vaporization Delta(vap)H(T(b)) has been established and numerical values of relative group parameters obtained. The average percent deviation of estimation of Delta(vap)H(T(b)) is 1.16, which show that the present method demonstrates significant improvement in applicability to predict the enthalpy of vaporization at the normal boiling point, compared the conventional group methods.

A Krylov Subspace Method for Information Retrieval

A new algorithm for information retrieval is described. It is a vector space method with automatic query expansion. The original user query is projected onto a Krylov subspace generated by the query and the term-document matrix. Each dimension of the Krylov space is generated by a simple vector space search, using first the user query and then new queries generated by the algorithm and orthogonal to the previous query vectors. The new algorithm is closely related to latent semantic indexing (LSI), but it is a local algorithm that works on a new subspace of very low dimension for each query. This makes it faster and more flexible than LSI. No preliminary computation of the singular value decomposition (SVD) is needed, and changes in the data base cause no complication. Numerical tests on both small (Cranfield) and larger (Financial Times data from the TREC collection) data sets are reported. The new algorithm gives better precision at given recall levels than simple vector space and LSI in those cases that have been compared.

Group Vector Space Method for Estimating Melting and Boiling Points of Organic Compounds

The specific position of a group in the molecule has been considered and a group vector space method for estimating boiling and melting points of organic compounds developed. Expressions for meltin...