An analysis of image retrieval behavior for metadata type image database

The aim of this paper was to analyze users’ behavior during image retrieval exercises. Results revealed that users tend to follow a set search strategy: firstly they input one or two keyword search terms one after another and view the images generated by their initial search and after they navigate their way around the web by using the ‘back to home’ or ‘previous page’ buttons. These results are consistent with existing Web research. Many of the actions recorded revealed that subjects behavior differed depending on if the task set was presented as a closed or open task. In contrast no differences were found for the time subjects took to perform a single action or their use of the AND operator.     

一种仿真瑞利信道的有效方法

改进了一种基于正弦和的仿真瑞利信道的方法,消除了它生成的复随机过程的同相和正交分量的概率密度函数及该复随机过程的包络平方的自相关函数是广义非平稳的缺点. 运用改进后的方法生成的复随机过程的自相关函数,及其同相和正交分量的自相关函数、互相关函数也与理论参考值相等. 仿真结果表明,改进后的方法生成的复随机过程的幅度、相位的概率密度函数及平均电平通过率、平均衰落持续时间能更好地逼近理论参考值.     

Integration of complex objects and transitive relationships for information retrieval

In this paper we show that in advanced information retrieval (IR) applications capabilities for data aggregation, transitive computation and NF² (non-first normal form) relational computation are often necessary at the same time. We demonstrate that complex objects are naturally modeled as NF² relations, whereas structures like hierarchical thesauri must be modeled for transitive computation. Transitive processing cannot be supported by structurally static structures like NF² relations. We present a truly declarative query interface, which integrates data aggregation, transitive computation and NF² relational computation. Thus the interface supports the retrieval and structural manipulation of complex objects (e.g. documents and bibliographic references), their retrieval through transitive relationships (e.g. thesauri, citations) and data aggregation based on their components (e.g. citation counts, author productivity). Most importantly, users can formulate queries on a high abstraction level without mastering actual programming or database techniques.     

An efficient substring search method by using delayed keyword extraction

In the information retrieval systems, one of the most important and difficult operations is to extract appropriate keywords from documents. This paper proposes an effective substring search method by extending a pattern matching machine for multi-keyword based on Aho and Corasick (AC) called AC machine. The proposed method enables us to extract keyword candidates as much as possible and to select the suitable keywords for users' purpose at a retrieval stage. This method contains four types of substring search methods (exact, prefix, suffix and proper substring search). This paper also proposes a construction algorithm of the retrieval structure for speeding up the substring search. From the simulation results, it is shown that the retrieval time of the presented method is as fast as the key retrieval method based on the trie.     

An information retrieval model based on vector space method by supervised learning

This paper proposes a method to improve retrieval performance of the vector space model (VSM) in part by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the user's relevance feedback information, information such as original document similarities is incorporated into the retrieval model, which is built by using a sequence of linear transformations. High-dimensional and sparse vectors are then reduced by singular value decomposition (SVD) and transformed into a low-dimensional vector space, namely the space representing the latent semantic meanings of words. The method has been tested with two test collections, the Medline collection and the Cranfield collection. In order to train the model, multiple partitions are created for each collection. Improvement of average precision of the averages over all partitions, compared with the latent semantic indexing (LSI) model, are 20.57% (Medline) and 22.23% (Cranfield) for the two training data sets, and 0.47% (Medline) and 4.78% (Cranfield) for the test data, respectively. The proposed method provides an approach that makes it possible to preserve user-supplied relevance information for the long term in the system in order to use it later.     

An empirical testing of user stereotypes of information retrieval systems

Stereotyping is a technique used in many information systems to represent user groups and/or to generate initial individual user models. However, there has been a lack of evidence on the accuracy of their use in representing users. We propose a formal evaluation method to test the accuracy or homogeneity of the stereotypes that are based on users' explicit characteristics. Using the method, the results of an empirical testing on 11 common user stereotypes of information retrieval (IR) systems are reported. The participants' memberships in the stereotypes were predicted using discriminant analysis, based on their IR knowledge. The actual membership and the predicted membership of each stereotype were compared. The data show that “librarians/IR professionals” is an accurate stereotype in representing its members, while some others, such as “undergraduate students” and “social sciences/humanities” users, are not accurate stereotypes. The data also demonstrate that based on the user's IR knowledge a stereotype can be made more accurate or homogeneous. The results show the promise that our method can help better detect the differences among stereotype members, and help with better stereotype design and user modeling. We assume that accurate stereotypes have better performance in user modeling and thus the system performance.Limitations and future directions of the study are discussed.     

An efficient method for solving Emden–Fowler equations

In this paper, He's variational iteration method (VIM) is applied to solve the Emden–Fowler type equations in the second-order ordinary differential equations (ODEs). In this method, general Lagrange multipliers are introduced to construct correction functionals for the problems. The Lagrange multipliers in the functionals can be identified optimally via variational theory. This technique provides a sequence of functions which convergence to the exact solutions of the Emden–Fowler equations. Comparison with the exact solutions and the solutions by the Adomian decomposition method (ADM) show efficiency of VIM in solving equations with singularity.     

对计算水平分层介质响应正交归一化法的改进

汪荣江提出一个简单的正交归一化技术来克服经典的Thomson-Haskell传播矩阵方法中存在的数值不稳定问题.为了进一步提高计算效率,给出该方法的2种改进.一种改进方法是将传播矩阵中与频率无关的部分分离出来,对于某一固定的水平慢度,这些矩阵只需计算一次;另一个改进是利用Langer块对角化的技术,将传播矩阵分解为几个稀疏矩阵的乘积.我们将改进之后的算法应用于计算水平分层模型中的广义反射系数.较之原有方案,提出的改进能节省一半计算时间.     

An evaluation of Goffman's indirect retrieval method

The indirect retrieval method proposed by Goffman is outlined and some similarities to other retrieval methods are indicated. The method is then evaluated and the results are compared with those obtained on the same document collection with cluster-based retrieval using single-link clustering.The comparisons show that although the effectiveness of the indirect retrieval method can be comparable to cluster-based retrieval, the efficiency is lower.     

An analysis of ill-formed input in natural language queries to document retrieval systems

We analyzed natural language document retrieval queries from the Thomas Cooper Library at the University of South Carolina in order to investigate the frequency of various types of ill-formed input, such as spelling errors, co-occurrence violations, conjunctions, ellipsis and missing or incorrect punctuation. The primary reason for analyzing ill-formed inputs was to determine whether there is a significant need to study ill-formed inputs in detail. After analyzing the queries, we found that most of the queries were sentence fragments and that many of them contained some type of ill-formed input. Conjunctions caused the most problems. The next most serious problem was caused by punctuation errors. Spelling errors occurred in a small number of the queries. The remaining types of ill-formed input considered, ellipsis and co-occurrence violations, were not found in the queries.     

A common interface for accessing document retrieval systems and DBMS for retrieval of bibliographic data

Due to their ready availability, database management systems are being applied to bibliographic databases with increasing frequency. This is being done in spite of the fact that although DBMS query languages tend to be very powerful, they are far too complex for the casual user. It is proposed that PSI, an existing virtual-system intermediary for document retrieval systems, be extended to include access to DBMS containing bibliographic data in order to circumvent the complexity problem or the casual user. PSI currently provides a common command language for access to multiple document retrieval systems. It is shown that PSI could be extended to provide this same command language to access DBMS, whether the DBMS are relational or network.     

Explore instance similarity: An instance correlation based hashing method for multi-label cross-model retrieval

With the rapid growth of multimedia data, cross-media hashing has gained more and more attention. However, most existing cross-modal hashing methods ignore the multi-label correlation and only apply binary similarity to measure the correlation between two instances. Most existing methods perform poorly in capturing the relevance between retrieval results and queries since binary similarity measurement has limited abilities to discriminate minor differences among different instances. In order to overcome the mentioned shortcoming, we introduce a novel notion of instance similarity method, which is used to evaluate the semantic correlation between two specific instances in training data. Base on the instance similarity, we also propose a novel deep instance hashing network, which utilizes instance similarity and binary similarity simultaneously for multi-label cross-model retrieval. The experiment results on two real datasets show the superiority of our proposed method, compared with a series of state-of-the-art cross-modal hashing methods in terms of several metric evaluations.     

基于数据量度模型的信息系统整合效益评价

信息系统整合的关键是数据整合。传统的基于业务的信息系统效益评价方式用于评价数据整合的效益略显不足。本文提出基于数据量度模型的系统整合效益评价体系,并介绍其应用案例。     

The role of digital identifier systems in the theory of digital objects

Theory of Digital Objects (TDO) can be considered as one of the important theories for explaining distinctive attributes and social practices of digital objects. The main aim of this study is to determine the role and position of digital identifiers and digital identifier systems in social practices of TDO. Results of applying comparative evaluation method demonstrated that the digital identifier systems can be considered as a third approach called identifiable Web, among memorable and navigable web in TDO. Moreover functions, technologically-induced tensions, challenges, counter mechanisms and emerging practices of the proposed approach had been investigated in contrast with the two existing social practices approaches. Results of this study could provide new ideas to administrators and researchers of these systems for promoting them from the sociotechnical aspect.     

Assigning appropriate weights for the linear combination data fusion method in information retrieval

In data fusion, the linear combination method is a very flexible method since different weights can be assigned to different systems. However, it remains an open question which weighting schema should be used. In some previous investigations and experiments, a simple weighting schema was used: for a system, its weight is assigned as its average performance over a group of training queries. However, it is not clear if this weighting schema is good or not. In some other investigations, different numerical optimisation methods were used to search for appropriate weights for the component systems. One major problem with those numerical optimisation methods is their low efficiency. It might not be feasible to use them in some situations, for example in some dynamic environments, system weights need to be updated from time to time for reasonably good performance. In this paper, we investigate the weighting issue by extensive experiments. The key point is to try to find the relation between performances of component systems and their corresponding weights which can lead to good fusion performance. We demonstrate that a series of power functions of average performance, which can be implemented as efficiently as the simple weighting schema, is more effective than the simple weighting schema for the linear data fusion method. Some other features of the power function weighting schema and the linear combination method are also investigated. The observations obtained from this study can be used directly in fusion applications of component retrieval results. The observations are also very useful for optimisation methods to choose better starting points and therefore to obtain more effective weights more quickly.     

A decision-theoretic approach to the evaluation of information retrieval systems

Evaluation research on information retrieval (IR) systems has thus far been narrowly focused and disjointed. This research attempts to narrow the gap by providing a comprehensive and integrated multiple criteria decision-theoretic approach for the evaluation of IR systems. The approach, which is based on the Analytic Hierarchy Process (AHP), is illustrated in the context of a domain-specific IR system. The novelty of this approach lies in the focus on the user aspect and the application of decision-making theories in the IR field.