Users' perception of relevance of spoken documents

We present the results of a study of user's perception of relevance of documents. The aim is to study experimentally how users' perception varies depending on the form that retrieved documents are presented. Documents retrieved in response to a query are presented to users in a variety of ways, from full text to a machine spoken query-biased automatically-generated summary, and the difference in users' perception of relevance is studied. The experimental results suggest that the effectiveness of advanced multimedia information retrieval applications may be affected by the low level of users' perception of relevance of retrieved documents

Mixing and merging for spoken document retrieval

This paper describes a number of experiments that explo- red the issues surrounding the retrieval of spoken documents. Two such issues were examined. First, attempting to find the best use of speech recogniser output to produce the highest retrieval effectiveness. Second, investigating the potential problems of retrieving from a so-called "mi- xed collection", i.e. one that contains documents from both a speech recognition system (producing many errors) and from hand transcription (producing presumably near perfect documents). The result of the first part of the work found that merging the transcripts of multiple recognisers showed most promise. The investigation in the second part showed how the term weighting scheme used in a retrieval system was important in determining whether the system was affected detrimentally when retrieving from a mixed collection

A Network Model for Adaptive Information Retrieval

This thesis presents a network model which can be used to represent Associative Information Retrieval applications at a conceptual level. The model presents interesting characteristics of adaptability and it has been used to model both traditional and knowledge based Information Retrieval applications. Moreover, three different processing frameworks which can be used to implement the conceptual model are presented. They provide three different ways of using domain knowledge to adapt the user formulated query to the characteristics of a specific application domain using the domain knowledge stored in a sub-network. The advantages and drawbacks of these three adaptive retrieval strategies are pointed out and discussed. The thesis also reports the results of an experimental investigation into the effectiveness of the adaptive retrieval given by a processing framework based on Neural Networks. This processing framework makes use of the learning and generalisation capabilities of the Backpropagation learning procedure for Neural Networks to build up and use application domain knowledge in the form of a sub-symbolic knowledge representation. The knowledge is acquired from examples of queries and relevant documents of the collection in use. In the tests reported in this thesis the Cranfield document collection has been used. Three different learning strategies are introduced and analysed. Their results in terms of learning and generalisation of the application domain knowledge are studied from an Information Retrieval point of view. Their retrieval results are studied and compared with those obtained by a traditional retrieval approach. The thesis concludes with a critical analysis of the results obtained in the experimental investigation and with a critical view of the operational effectiveness of such an approach

Exposing Multi-Relational Networks to Single-Relational Network Analysis Algorithms

Many, if not most network analysis algorithms have been designed specifically for single-relational networks; that is, networks in which all edges are of the same type. For example, edges may either represent "friendship," "kinship," or "collaboration," but not all of them together. In contrast, a multi-relational network is a network with a heterogeneous set of edge labels which can represent relationships of various types in a single data structure. While multi-relational networks are more expressive in terms of the variety of relationships they can capture, there is a need for a general framework for transferring the many single-relational network analysis algorithms to the multi-relational domain. It is not sufficient to execute a single-relational network analysis algorithm on a multi-relational network by simply ignoring edge labels. This article presents an algebra for mapping multi-relational networks to single-relational networks, thereby exposing them to single-relational network analysis algorithms.Comment: ISSN:1751-157

Grammar-Based Random Walkers in Semantic Networks

Semantic networks qualify the meaning of an edge relating any two vertices. Determining which vertices are most "central" in a semantic network is difficult because one relationship type may be deemed subjectively more important than another. For this reason, research into semantic network metrics has focused primarily on context-based rankings (i.e. user prescribed contexts). Moreover, many of the current semantic network metrics rank semantic associations (i.e. directed paths between two vertices) and not the vertices themselves. This article presents a framework for calculating semantically meaningful primary eigenvector-based metrics such as eigenvector centrality and PageRank in semantic networks using a modified version of the random walker model of Markov chain analysis. Random walkers, in the context of this article, are constrained by a grammar, where the grammar is a user defined data structure that determines the meaning of the final vertex ranking. The ideas in this article are presented within the context of the Resource Description Framework (RDF) of the Semantic Web initiative.Comment: First draft of manuscript originally written in November 200

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.Comment: To be appear in ECIR 202

Methane activation and exchange by titanium-carbon multiple bonds

We demonstrate that a titanium-carbon multiple bond, specifically an alkylidyne ligand in the transient complex, (PNP)Ti≡C^(t)Bu (A) (PNP^− = N[2-P(CHMe_2)_(2)-4-methylphenyl]_2), can cleanly activate methane at room temperature with moderately elevated pressures to form (PNP)Ti=CHtBu(CH_3). Isotopic labeling and theoretical studies suggest that the alkylidene and methyl hydrogens exchange, either via tautomerization invoking a methylidene complex, (PNP)Ti=CH_(2)(CH_(2)^(t)Bu), or by forming the methane adduct (PNP)Ti≡C^(t)Bu(CH_4). The thermal, fluxional and chemical behavior of (PNP)Ti=CH^(t)Bu(CH_3) is also presented in this study

A study of the kinematics of probabilities in information retrieval

In Information Retrieval (IR), probabilistic modelling is related to the use of a model that ranks documents in decreasing order of their estimated probability of relevance to a user's information need expressed by a query. In an IR system based on a probabilistic model, the user is guided to examine first the documents that are the most likely to be relevant to his need. If the system performed well, these documents should be at the top of the retrieved list. In mathematical terms the problem consists of estimating the probability P(R | q,d), that is the probability of relevance given a query q and a document d. This estimate should be performed for every document in the collection, and documents should then be ranked according to this measure. For this evaluation the system should make use of all the information available in the indexing term space. This thesis contains a study of the kinematics of probabilities in probabilistic IR. The aim is to get a better insight of the behaviour of the probabilistic models of IR currently in use and to propose new and more effective models by exploiting different kinematics of probabilities. The study is performed both from a theoretical and an experimental point of view. Theoretically, the thesis explores the use of the probability of a conditional, namely P(d → q), to estimate the conditional probability P(R | q,d). This is achieved by interpreting the term space in the context of the "possible worlds semantics". Previous approaches in this direction had as their basic assumption the consideration that "a document is a possible world". In this thesis a different approach is adopted, based on the assumption that "a term is a possible world". This approach enables the exploitation of term-term semantic relationships in the term space, estimated using an information theoretic measure. This form of information is rarely used in IR at retrieval time. Two new models of IR are proposed, based on two different way of estimating P(d → q) using a logical technique called Imaging. The first model is called Retrieval by Logical Imaging; the second is called Retrieval by General Logical Imaging, being a generalisation of the first model. The probability kinematics of these two models is compared with that of two other proposed models: the Retrieval by Joint Probability model and the Retrieval by Conditional Probability model. These last two models mimic the probability kinematics of the Vector Space model and of the Probabilistic Retrieval model. Experimentally, the retrieval effectiveness of the above four models is analysed and compared using five test collections of different sizes and characteristics. The results of this experimentation depend heavily on the choice of term weight and term similarity measures adopted. The most important conclusion of this thesis is that theoretically a probability transfer that takes into account the semantic similarity between the probability-donor and the probability-recipient is more effective than a probability transfer that does not take that into account. In the context of IR this is equivalent to saying that models that exploit the semantic similarity between terms in the term space at retrieval time are more effective that models that do not do that. Unfortunately, while the experimental investigation carried out using small test collections provide evidence supporting this conclusion, experiments performed using larger test collections do not provide as much supporting evidence (although they do not provide contrasting evidence either). The peculiar characteristics of the term space of different collections play an important role in shaping the effects that different probability kinematics have on the effectiveness of the retrieval process. The above result suggests the necessity and the usefulness of further investigations into more complex and optimised models of probabilistic IR, where probability kinematics follows non-classical approaches. The models proposed in this thesis are just two such approaches; other ones can be developed using recent results achieved in other fields, such as non-classical logics and belief revision theory

A New Combination Method Based on Adaptive Genetic Algorithm for Medical Image Retrieval

Medical image retrieval could be based on the text describing the image as the caption or the title. The use of text terms to retrieve images have several disadvantages such as term-disambiguation. Recent studies prove that representing text into semantic units (concepts) can improve the semantic representation of textual information. However, the use of conceptual representation has other problems as the miss or erroneous semantic relation between two concepts. Other studies show that combining textual and conceptual text representations leads to better accuracy. Popularly, a score for textual representation and a score for conceptual representation are computed and then a combination function is used to have one score. Although the existing of many combination methods of two scores, we propose in this paper a new combination method based on adaptive version of the genetic algorithm. Experiments are carried out on Medical Information Retrieval Task of the ImageCLEF 2009 and 2010. The results confirm that the combination of both textual and conceptual scores allows best accuracy. In addition, our approach outperforms the other combination methods

When the Earth trembles in the americas: the experience of haiti and chile 2010.

The response of the nephrological community to the Haiti and Chile earthquakes which occurred in the first months of 2010 is described. In Haiti, renal support was organized by the Renal Disaster Relief Task Force (RDRTF) of the International Society of Nephrology (ISN) in close collaboration with Médecins Sans Frontières (MSF), and covered both patients with acute kidney injury (AKI) and patients with chronic kidney disease (CKD). The majority of AKI patients (19/27) suffered from crush syndrome and recovered their kidney function. The remaining 8 patients with AKI showed acute-to-chronic renal failure with very low recovery rates. The intervention of the RDRTF-ISN involved 25 volunteers of 9 nationalities, lasted exactly 2 months, and was characterized by major organizational difficulties and problems to create awareness among other rescue teams regarding the availability of dialysis possibilities. Part of the Haitian patients with AKI reached the Dominican Republic (DR) and received their therapy there. The nephrological community in the DR was able to cope with this extra patient load. In both Haiti and the DR, dialysis treatment was able to be prevented in at least 40 patients by screening and adequate fluid administration. Since laboratory facilities were destroyed in Port-au-Prince and were thus lacking during the first weeks of the intervention, the use from the very beginning on of a point-of-care device (i-STAT®) was very efficient for the detection of aberrant kidney function and electrolyte parameters. In Chile, nephrological problems were essentially related to difficulties delivering dialysis treatment to CKD patients, due to the damage to several units. This necessitated the reallocation of patients and the adaptation of their schedules. The problems could be handled by the local nephrologists. These observations illustrate that local and international preparedness might be life-saving if renal problems occur in earthquake circumstances