Different Degrees Of Relevance Research Articles

An Academic Concept ‘University’ in a Linguistic Perception of German and Russian Students

The relevance of the research problem is conditioned by a significant position, that take up the academic values based on their linguocultural concepts in the modern world, the intensification of Russian-German communication in the field of education generating the need to create a comparative academic conceptuary. The article is aimed at a determination of the comparative characteristics of the academic concept of ‘university’ in the German and Russian linguistic cultures. The leading method in carrying out of this study is linguistic experiment (free association experiment). As a result of the experiment, it was found that the list of associated attributes that make up the core of the concept of ‘university’ in the associative-verbal network of German-speaking and Russian-speaking subjects of academic activities, similar to, but different degree of relevance of these features. The only significant difference fixed in the association experiment is the presence in the German linguoculture associative relationship with the freedom of the student community and the absence of such a mutual relation in the perceptions of Russian students. The materials of the article may be useful in lexicology of the German and Russian languages, training courses on geography and linguocultural studies of Russia and Germany, in the practice of Russian-German academic communication.

Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm

In this paper we introduce a new hierarchical clustering algorithm called Wardp. Unlike the original Ward, Wardp generates feature weights, which can be seen as feature rescaling factors thanks to the use of the Lp norm. The feature weights are cluster dependent, allowing a feature to have different degrees of relevance at different clusters. We validate our method by performing experiments on a total of 75 real-world and synthetic datasets, with and without added features made of uniformly random noise. Our experiments show that: (i) the use of our feature weighting method produces results that are superior to those produced by the original Ward method on datasets containing noise features; (ii) it is indeed possible to estimate a good exponent p under a totally unsupervised framework. The clusterings produced by Wardp are dependent on p. This makes the estimation of a good value for this exponent a requirement for this algorithm, and indeed for any other also based on the Lp norm.

Assessment of relevance of different effects in energy infrastructure revitalization in non-residential buildings

This paper analyses the effects of energy infrastructure revitalization in public non-residential buildings in the contexts of energy planning support and energy policy creation. The approach created acknowledges the different degree of relevance of the specific aspects, the specific interests of different stakeholders in energy activity and the variable degree of influence of each effect on the overall development and sustainability of energy solutions. It is based on the mathematical concept of the analytic hierarchy process (AHP) as a multi-criteria optimization procedure. AHP conclusions provide significant contributions in strategic development and transitional path planning and energy policy instruments implementation, while taking all local society and economic specificities into account.Given the fact that the effects of energy infrastructure revitalization are diverse and multidimensional, this method provides an analytical framework suitable for a comprehensive understanding and relative relevance evaluation of each of the effects with the use of structured organization of thinking, facts, new knowledge insights, intuition, experience and performance indicators. The method establishes a comprehensive and systematic algorithm for reasoning and revealing all aspects of energy infrastructure revitalization, and results in an arguable alternative choice in correlation with quantitative (economic, technical) and qualitative (environmental, social) criteria.

An approach to rank reviews by fusing and mining opinions based on review pertinence

Fusing and mining opinions from reviews posted in webs or social networks is becoming a popular research topic in recent years in order to analyze public opinions on a specific topic or product. Existing research has been focused on extraction, classification and summarization of opinions from reviews in news websites, forums and blogs. An important issue that has not been well studied is the degree of relevance between a review and its corresponding article. Prior work simply divides reviews into two classes: spam and non-spam, neglecting that the non-spam reviews could have different degrees of relevance to the article. In this paper, we propose a notion of “Review Pertinence” to study the degree of this relevance. Unlike usual methods, we measure the pertinence of review by considering not only the similarity between a review and its corresponding article, but also the correlation among reviews. Experiment results based on real data sets collected from a number of popular portal sites show the obvious effectiveness of our method in ranking reviews based on their pertinence, compared with three baseline methods. Thus, our method can be applied to efficiently retrieve reviews for opinion fusion and mining and filter review spam in practice.

Sentence relevance and memory retrieval in discourse comprehension: N400 and LrNERP effects

The present study explored the effect of different degrees of relevance in discourse comprehension by using ERPs analysis. A principle of pragmatic relevance is supposed to guide inferential mechanism underlying discourse processing. Discourse level comprehension needs a system of predictions about which information is more relevant in order to process the ongoing sentence meaning. This system should construct a specific mental model, where inferences related to the present sentence are stored and maintained. Three degrees of relevance of a new information (new sentence) with respect to an old information (target sentence) were manipulated: directly relevant; indirectly relevant; not relevant. Twenty-one subjects participated to the experiment and they were asked to try to comprehend a set of two paired sentences (old-new paired sentences) based on their conceptual relevance. Two negative deflections, peaking respectively at about 410 msec post-stimulus (N400), more right anterior-centrally distributed, and at about 550 msec (late negativity, LrN), more right central localized, were found. Repeated measures ANOVA found that the amplitude of both the N400 and LrNis modulated by the degree of relevance and by the strength of the underlying associations between the two sentences. Indirect relevance resulted in increased negativities in comparison with direct relevance. Contrarily, non-relevant condition did not produce an increasing in N400 and LrNamplitude. Unrelevance of the knowledge related to the actual mental model of sentences may induce a rapid and costless discarding of non pertinent information. The conclusive inference is that a subset of neural processes responding to degree of relevance of information is separable and cortically more frontally and centrally localized. Functional differences between N400 and LrN for relevance were discussed.

Quantifying and Transferring Contextual Information in Object Detection

Context is critical for reducing the uncertainty in object detection. However, context modeling is challenging because there are often many different types of contextual information coexisting with different degrees of relevance to the detection of target object(s) in different images. It is therefore crucial to devise a context model to automatically quantify and select the most effective contextual information for assisting in detecting the target object. Nevertheless, the diversity of contextual information means that learning a robust context model requires a larger training set than learning the target object appearance model, which may not be available in practice. In this work, a novel context modeling framework is proposed without the need for any prior scene segmentation or context annotation. We formulate a polar geometric context descriptor for representing multiple types of contextual information. In order to quantify context, we propose a new maximum margin context (MMC) model to evaluate and measure the usefulness of contextual information directly and explicitly through a discriminant context inference method. Furthermore, to address the problem of context learning with limited data, we exploit the idea of transfer learning based on the observation that although two categories of objects can have very different visual appearance, there can be similarity in their context and/or the way contextual information helps to distinguish target objects from nontarget objects. To that end, two novel context transfer learning models are proposed which utilize training samples from source object classes to improve the learning of the context model for a target object class based on a joint maximum margin learning framework. Experiments are carried out on PASCAL VOC2005 and VOC2007 data sets, a luggage detection data set extracted from the i-LIDS data set, and a vehicle detection data set extracted from outdoor surveillance footage. Our results validate the effectiveness of the proposed models for quantifying and transferring contextual information, and demonstrate that they outperform related alternative context models.

Comparison of weighted grey relational analysis for software effort estimation

In recent years, grey relational analysis (GRA), a similarity-based method, has been proposed and used in many applications. However, we found that most traditional GRA methods only consider nonweighted similarity for predicting software development effort. In fact, nonweighted similarity may cause biased predictions, because each feature of a project may have a different degree of relevance to the development effort. Therefore, this paper proposes six weighted methods, including nonweighted, distance-based, correlative, linear, nonlinear, and maximal weights, to be integrated into GRA for software effort estimation. Numerical examples and sensitivity analyses based on four public datasets are used to show the performance of the proposed methods. The experimental results indicate that the weighted GRA can improve estimation accuracy and reliability from the nonweighted GRA. The results also demonstrate that the weighted GRA performs better than other estimation techniques and published results. In summary, we can conclude that weighted GRA can be a viable and alternative method for predicting software development effort.

Response projected clustering for direct association with physiological and clinical response data

BackgroundMicroarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients' residual tumor sizes after chemotherapy or glucose levels at various stages of diabetic patients. Current clustering analysis cannot directly incorporate such quantitative metadata into the clustering heatmap of gene expression. It will be quite useful if these clinical response data can be effectively summarized in the high-dimensional clustering display so that important groups of genes can be intuitively discovered with different degrees of relevance to target disease phenotypes.ResultsWe introduced a novel clustering analysis approach, response projected clustering (RPC), which uses a high-dimensional geometrical projection of response data to the gene expression space. The projected response vector, which becomes the origin in the projected space, is then clustered together with the projected gene vectors based on their different degrees of association with the response vector. A bootstrap-counting based RPC analysis is also performed to evaluate statistical tightness of identified gene clusters. Our RPC analysis was applied to the in vitro growth-inhibition and microarray profiling data on the NCI-60 cancer cell lines and the microarray gene expression study of macrophage differentiation in atherogenesis. These RPC applications enabled us to identify many known and novel gene factors and their potential pathway associations which are highly relevant to the drug's chemosensitivity activities and atherogenesis.ConclusionWe have shown that RPC can effectively discover gene networks with different degrees of association with clinical metadata. Performed on each gene's response projected vector based on its degree of association with the response data, RPC effectively summarizes individual genes' association with metadata as well as their own expression patterns. Thus, RPC greatly enhances the utility of clustering analysis on investigating high-dimensional microarray gene expression data with quantitative metadata.

On tachyons, gauged linear sigma models, and flip transitions

We study systems of multiple localized closed string tachyons and the phenomena associated with their condensation, in C3/ZN nonsupersymmetric noncompact orbifold singularities using gauged linear sigma model constructions, following hep-th/0406039. Our study reveals close connections between the combinatorics of nonsupersymmetric flip transitions (between topologically distinct resolutions of the original singularity), the physics of tachyons of different degrees of relevance and the singularity structure of the corresponding residual endpoint geometries. This in turn can be used to study the stability of the phases of gauged linear sigma models and gain qualitative insight into the closed string tachyon potential.

Information clustering based on fuzzy multisets

A fuzzy multiset model for information clustering is proposed with application to information retrieval on the World Wide Web. Noting that a search engine retrieves multiple occurrences of the same subjects with possibly different degrees of relevance, we observe that fuzzy multisets provide an appropriate model of information retrieval on the WWW. Information clustering which means both term clustering and document clustering is considered. Three methods of the hard c-means, fuzzy c-means, and an agglomerative method using cluster centers are proposed. Two distances between fuzzy multisets and algorithms for calculating cluster centers are defined. Theoretical properties concerning the clustering algorithms are studied. Illustrative examples are given to show how the algorithms work.

A proof-irrelevant model of Martin-Löf's logical framework

We extend the proof-irrelevant model defined in Smith (1988) to the whole of Martin-Löf's logical framework. The main difference here is the existence of a type whose objects themselves represent types rather than proof-objects. This means that the model must now be able to distinguish between objects with different degree of relevance: those that denote proofs are irrelevant whereas those that denote types are not. In fact a whole hierarchy of relevance exists.Another difference is the higher level of detail in the formulation of the formal theory, such as the explicit manipulation of contexts and substitutions. This demands an equally detailed definition of the model, including interpreting contexts and substitutions.We are thus led to a whole reformulation of the proof-irrelevant model. We present a model that is built up from an arbitrary model of the untyped lambda calculus. We also show how to extend it when the logical framework itself is enlarged with inductive definitions. In doing so, a variant of Church numerals is introduced.As in Smith (1988), the model can only be defined in the absence of universes, and it is useful to obtain an elementary proof of consistency and to prove the independence of Peano's fourth axiom.

Using graded relevance assessments in IR evaluation

AbstractThis article proposes evaluation methods based on the use of nondichotomous relevance judgements in IR experiments. It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents. This is desirable from the user point of view in modern large IR environments. The proposed methods are (1) a novel application of P‐R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) generalized recall and precision based directly on multiple grade relevance assessments (i.e., not dichotomizing the assessments). We demonstrate the use of the traditional and the novel evaluation measures in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system (InQuery1) in a text database consisting of newspaper articles. To gain insight into the retrieval process, one should use both graded relevance assessments and effectiveness measures that enable one to observe the differences, if any, between retrieval methods in retrieving documents of different levels of relevance. In modern times of information overload, one should pay attention, in particular, to the capability of retrieval methods retrieving highly relevant documents.

THE SIGNIFICANCE OF INDUCED FORESTOMACH TUMORS

Scientific interest in forestomach carcinogenesis has been greatly heightened by the observation that butylated hydroxyanisole (BHA) is carcinogenic to rats at this site (1). BHA is one of only three synthetic phenolic antioxidants that are permitted to be added to food in Canada. These chemicals inhibit oxidative spoilage of food (rancidity) during transportation and storage and are thus of importance to the functioning of the modern centralized food industry. The demonstration that high levels of BHA incorporated into the food of male and female rats resulted in papillomas and carcinomas of the forestomach led to an international research effort focused on the forestomach and designed to outline the way in which these tumors arose and determine whether they are relevant to humans (2). This report addresses the development of cancerous lesions in the rodent forestomach, the nature of the chemicals that induce these lesions, and their interactions with the animal body. Presently available evidence indicates there are at least two different types of chemical carcinogen that act on the forestomach and that these may have very different degrees of relevance to the human population.

Status Characteristics: Degrees of Task Relevance and Decision Processes

Status characteristics theories attempt to explain the emergence of observable power and prestige hierarchies in informal task groups based on the perceived performance expectations connoted by differential ranks on status characteristics. Generally, these theories have assumed that the task performance expectation-formation processes operate similarly regardless of whether the status characteristic has been made explicitly relevant to the task or not. An alternative theoretical model is presented that predicts the corresponding decision processes to differ. Using discrete-time, discrete-state event-history techniques to reanalyze Moore's (1968) data, the prediction that the processes are not the same is supported, with characteristics made explicitly relevant being more relied on than characteristics n,ot made explicitly relevant. The implications regarding the vulnerability of the effects of characteristics of different degrees of relevance to being overridden or eliminated are discussed.