Explanation of relevance judgement discrepancy with quantum interference

A key concept in many Information Retrieval (IR) tasks, e.g. document indexing, query language modelling, aspect and diversity retrieval, is the relevance measurement of topics, i.e. to what extent an information object (e.g. a document or a query) is about the topics. This paper investigates the interference of relevance measurement of a topic caused by another topic. For example, consider that two user groups are required to judge whether a topic q is relevant to a document d, and q is presented together with another topic (referred to as a companion topic). If different companion topics are used for different groups, interestingly different relevance probabilities of q given d can be reached. In this paper, we present empirical results showing that the relevance of a topic to a document is greatly affected by the companion topic’s relevance to the same document, and the extent of the impact differs with respect to different companion topics. We further analyse the phenomenon from classical and quantum-like interference perspectives, and connect the phenomenon to nonreality and contextuality in quantum mechanics. We demonstrate that quantum like model fits in the empirical data, could be potentially used for predicting the relevance when interference exists.

Learning-based Relevance Feedback for Web-based Relation Completion

In a pilot application based on web search engine calledWeb-based Relation Completion (WebRC), we propose to join two columns of entities linked by a predefined relation by mining knowledge from the web through a web search engine. To achieve this, a novel retrieval task Relation Query Expansion (RelQE) is modelled: given an entity (query), the task is to retrieve documents containing entities in predefined relation to the given one. Solving this problem entails expanding the query before submitting it to a web search engine to ensure that mostly documents containing the linked entity are returned in the top K search results. In this paper, we propose a novel Learning-based Relevance Feedback (LRF) approach to solve this retrieval task. Expansion terms are learned from training pairs of entities linked by the predefined relation and applied to new entity-queries to find entities linked by the same relation. After describing the approach, we present experimental results on real-world web data collections, which show that the LRF approach always improves the precision of top-ranked search results to up to 8.6 times the baseline. Using LRF, WebRC also shows performances way above the baseline.

Stochastic ranking method for thermostatically controllable appliances to provide regulation services

Demand response can be used for providing regulation services in the electricity markets. The retailers can bid in a day-ahead market and respond to real-time regulation signal by load control. This paper proposes a new stochastic ranking method to provide regulation services via demand response. A pool of thermostatically controllable appliances (TCAs) such as air conditioners and water heaters are adjusted using direct load control method. The selection of appliances is based on a probabilistic ranking technique utilizing attributes such as temperature variation and statuses of TCAs. These attributes are stochastically forecasted for the next time step using day-ahead information. System performance is analyzed with a sample regulation signal. Network capability to provide regulation services under various seasons is analyzed. The effect of network size on the regulation services is also investigated.

Ranking based clustering for social event detection

The problem of clustering a large document collection is not only challenged by the number of documents and the number of dimensions, but it is also affected by the number and sizes of the clusters. Traditional clustering methods fail to scale when they need to generate a large number of clusters. Furthermore, when the clusters size in the solution is heterogeneous, i.e. some of the clusters are large in size, the similarity measures tend to degrade. A ranking based clustering method is proposed to deal with these issues in the context of the Social Event Detection task. Ranking scores are used to select a small number of most relevant clusters in order to compare and place a document. Additionally,instead of conventional cluster centroids, cluster patches are proposed to represent clusters, that are hubs-like set of documents. Text, temporal, spatial and visual content information collected from the social event images is utilized in calculating similarity. Results show that these strategies allow us to have a balance between performance and accuracy of the clustering solution gained by the clustering method.

Towards universal search design

For people with cognitive disabilities, technology is more often thought of as a support mechanism, rather than a source of division that may require intervention to equalize access across the cognitive spectrum. This paper presents a first attempt at formalizing the digital gap created by the generalization of search engines. This was achieved through the development of a mapping of cognitive abilities required by users to execute low- level tasks during a standard Web search task. The mapping demonstrates how critical these abilities are to successfully use search engines with an adequate level of independence. It will lead to a set of design guidelines for search engine interfaces that will allow for the engagement of users of all abilities, and also, more importantly, in search algorithms such as query suggestion and measure of relevance (i.e. ranking).

Relevance feature discovery for text mining

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.

Investigative relevance

Criminal profiling is one tool available to investigative agencies that may assist in narrowing suspect pools, linking crimes, providing relevant leads and new investigative strategies, and keeping the overall investigation on track (Turvey, 2008). However, like a flashlight in a darkened room, profiling may not always provide valuable assistance if it shines in the wrong direction or fails to shine at all. In a perfect world, profiles are intended to provide investigators with a set of refined characteristics of the offender for a crime or a crime series that will assist their efforts. In contrast, it could be argued that profiles are not intended to provide information that may be irrelevant, unclear, confusing, or distracting to these efforts. Any information provided within the profile that does not assist in narrowing suspect pools or providing new avenues of inquiry is left open to misinterpretation and is therefore potentially damaging (Turvey, 2008). The degree to which information provided in a profile can actually be utilized by investigators to meet their goals is known as investigative relevance...

On the relevance of travel journalism : An introduction

Not many people will be instantly familiar with British woman Dale Sheppard-Floyd, but – at least symbolically – she represents a significant milestone in the development of travel and tourism. In fact, the milestone was so significant that the United Nations World Tourism Organization booked Madrid’s venerable Museo del Prado to announce to the world’s media her visit to Spain on 13 December 2012. For Ms Sheppard-Floyd’s arrival for a three-day trip meant that more than one billion times in that year, someone had crossed a border as a tourist. An astounding number, considering that, in 1950, there had been only 25 million tourist arrivals worldwide, and even only two decades previously – in 1990 – the number had been less than half at 435 million arrivals (World Tourism Organization, 2012a, 2012b). While people have traveled for pleasure for millennia (Towner, 1995), tourism really came into its own with the expansion of the middle classes in the 19th and 20th century, and today it is considered the world’s largest business sector, with unprecedented numbers of people venturing outside of their immediate environments to explore the world around them. In 2012, travel and tourism’s total contribution to the world economy amounted to a staggering $6.6 trillion, or 9 per cent of GDP (World Travel & Tourism Council, 2013). More than 260 million jobs were generated by it worldwide, which equates to one in every 11 jobs across the globe. While there were some hiccups during the Global Financial Crisis, growth in 2012 was stronger than in other industries, such as manufacturing, financial services and retail (World Travel & Tourism Council, 2013)...

Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems

Discounted Cumulative Gain (DCG) is a well-known ranking evaluation measure for models built with multiple relevance graded data. By handling tagging data used in recommendation systems as an ordinal relevance set of {negative,null,positive}, we propose to build a DCG based recommendation model. We present an efficient and novel learning-to-rank method by optimizing DCG for a recommendation model using the tagging data interpretation scheme. Evaluating the proposed method on real-world datasets, we demonstrate that the method is scalable and outperforms the benchmarking methods by generating a quality top-N item recommendation list.

The repertoire of nitrogen assimilation in Rhodococcus: Catalysis, pathways and relevance in biotechnology and bioremediation

The Rhodococcus genus exhibits diverse enzymatic activity that can be exploited in the conversion of natural and anthropogenic nitrogenous compounds. This catalytic response provides a selective advantage in terms of available nutrients while also serving to remove otherwise harmful xenobiotics. This review provides a critical assessment of the literature on bioconversion of organo-nitrogen compounds with a consideration of applications in bioremediation and commercial biotechnology. By examining the major nitro-organic compounds (amino acids, amines, nitriles, amides and nitroaromatics) in turn, the considerable repertoire of Rhodococcus spp. is established. The available published enzyme reaction data is coupled with genomic characterisation to provide a molecular basis for Rhodococcus enzyme activity with an assessment of the cellular properties that aid substrate accessibility and ensure stability. The metabolic gene clusters associated with the observed reaction pathways are identified and future directions in enzyme optimisation and metabolic engineering are assessed. © 2014 Society of Chemical Industry.

A multi-criteria analysis approach for ranking and selection of microorganisms for the production of oils for biodiesel production

Oleaginous microorganisms have potential to be used to produce oils as alternative feedstock for biodiesel production. Microalgae (Chlorella protothecoides and Chlorella zofingiensis), yeasts (Cryptococcus albidus and Rhodotorula mucilaginosa), and fungi (Aspergillus oryzae and Mucor plumbeus) were investigated for their ability to produce oil from glucose, xylose and glycerol. Multi-criteria analysis (MCA) using analytic hierarchy process (AHP) and preference ranking organization method for the enrichment of evaluations (PROMETHEE) with graphical analysis for interactive aid (GAIA), was used to rank and select the preferred microorganisms for oil production for biodiesel application. This was based on a number of criteria viz., oil concentration, content, production rate and yield, substrate consumption rate, fatty acids composition, biomass harvesting and nutrient costs. PROMETHEE selected A. oryzae, M. plumbeus and R. mucilaginosa as the most prospective species for oil production. However, further analysis by GAIA Webs identified A. oryzae and M. plumbeus as the best performing microorganisms.

The constraints of relevance on prevocational curriculum

This paper reflects on how relevance has been invoked as a curricular principle, both by students and teachers, in curriculum documents and in curriculum theory, to explore its variously conceived parameters and conditions. By posing the questions ‘relevant to whom?’, ‘relevant to what?’, ‘relevant how?’ and ‘relevant when?’ this paper exposes relevance as both a curricular virtue and a curricular constraint. It draws on an empirical project undertaken in the prevocational curriculum offered in Australia’s recently extended compulsory schooling for students in non-academic pathways. Data vignettes offer windows into two settings to exemplify the different ways relevance can be interpreted, stretched or contested. Using Bernstein’s distinction between vertical and horizontal discourses and knowledge structures, the analysis identifies what is gained and what is lost when relevance, variously defined, serves as a principle for curricular selection.