A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model.

Perceptual user interface framework for immersive information retrieval environments (An experimental framework for testing and rapid iteration)

The use of perceptual inputs is an emerging area within HCI that suggests a developing Perceptual User Interface (PUI) that may prove advantageous for those involved in mobile serious games and immersive social network environments. Since there are a large variety of input devices, software platforms, possible interactions, and myriad ways to combine all of the above elements in pursuit of a PUI, we propose in this paper a basic experimental framework that will be able to standardize study of the wide range of interactive applications for testing efficacy in learning or information retrieval and also suggest improvements to emerging PUIs by enabling quick iteration. This rapid iteration will start to define a targeted range of interactions that will be intuitive and comfortable as perceptual inputs, and enhance learning and information retention in comparison to traditional GUI systems. The work focuses on the planning of the technical development of two scenarios, and the first steps in developing a framework to evaluate these and other PUIs for efficacy and pedagogy.

Self-similarity principle: the reduced description of randomness

A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented. The proposed analytical function helps to fit the response of many complex systems when their recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of economical, meteorological and other complex data when the mathematical model is absent but the reduced description in terms of some universal set of the fitting parameters is necessary. This fitting function is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of initial data set. The general conditions of application of this fitting method describing the response of many complex systems and the forecast possibilities are discussed.

Double power laws, fractals and self-similarity

Power law (PL) distributions have been largely reported in the modeling of distinct real phenomena and have been associated with fractal structures and self-similar systems. In this paper, we analyze real data that follows a PL and a double PL behavior and verify the relation between the PL coefficient and the capacity dimension of known fractals. It is to be proved a method that translates PLs coefficients into capacity dimension of fractals of any real data.

Double power laws, fractals and self-similarity

Power law (PL) distributions have been largely reported in the modeling of distinct real phenomena and have been associated with fractal structures and self-similar systems. In this paper, we analyze real data that follows a PL and a double PL behavior and verify the relation between the PL coefficient and the capacity dimension of known fractals. It is to be proved a method that translates PLs coefficients into capacity dimension of fractals of any real data.

Power Law Behavior and Self-similarity in Modern Industrial Accidents

Advances in technology have produced more and more intricate industrial systems, such as nuclear power plants, chemical centers and petroleum platforms. Such complex plants exhibit multiple interactions among smaller units and human operators, rising potentially disastrous failure, which can propagate across subsystem boundaries. This paper analyzes industrial accident data-series in the perspective of statistical physics and dynamical systems. Global data is collected from the Emergency Events Database (EM-DAT) during the time period from year 1903 up to 2012. The statistical distributions of the number of fatalities caused by industrial accidents reveal Power Law (PL) behavior. We analyze the evolution of the PL parameters over time and observe a remarkable increment in the PL exponent during the last years. PL behavior allows prediction by extrapolation over a wide range of scales. In a complementary line of thought, we compare the data using appropriate indices and use different visualization techniques to correlate and to extract relationships among industrial accident events. This study contributes to better understand the complexity of modern industrial accidents and their ruling principles.

Mouse alarm pheromone shares structural similarity with predator scents.

Sensing the chemical warnings present in the environment is essential for species survival. In mammals, this form of danger communication occurs via the release of natural predator scents that can involuntarily warn the prey or by the production of alarm pheromones by the stressed prey alerting its conspecifics. Although we previously identified the olfactory Grueneberg ganglion as the sensory organ through which mammalian alarm pheromones signal a threatening situation, the chemical nature of these cues remains elusive. We here identify, through chemical analysis in combination with a series of physiological and behavioral tests, the chemical structure of a mouse alarm pheromone. To successfully recognize the volatile cues that signal danger, we based our selection on their activation of the mouse olfactory Grueneberg ganglion and the concomitant display of innate fear reactions. Interestingly, we found that the chemical structure of the identified mouse alarm pheromone has similar features as the sulfur-containing volatiles that are released by predating carnivores. Our findings thus not only reveal a chemical Leitmotiv that underlies signaling of fear, but also point to a double role for the olfactory Grueneberg ganglion in intraspecies as well as interspecies communication of danger.

The influence on children of the purpose of experiencing reading vocabulary: encoding-retrieval interactions in word perception

This research looked at conditions which result in the development of integrated letter code information in the acquisition of reading vocabulary. Thirty grade three children of normal reading ability acquired new reading words in a Meaning Assigned task and a Letter Comparison task, and worked to increase skill for known reading words in a Copy task. The children were then assessed on their ability to identify the letters in these words. During the test each stimulus word for each child was exposed for 100 msec., after which each child reported as many of his or her letters as he or she could. Familiar words, new words, and a single letter identification task served as within subject controls. Following this, subjects were assessed for word meaning recall of the Meaning Assigned words and word reading times for words in all condi tions • The resul ts supported an episodic model of word recognition in which the overlap between the processing operations employed in encoding a word and those required when decoding it affected decoding performance. In particular, the Meaning Assigned and Copy tasks. appeared to facilitate letter code accessibility and integration in new and familiar words respectively. Performance in the Letter Comparison task, on the other hand, suggested that subjects can process the elements of a new word without integrating them into its lexical structure. It was concluded that these results favour an episodic model of word recognition.

"Re-huminization": the role of human-animal similarity in predicting prejudice towards immigrants and non-human animals

Research implies that there ~ay be an association between attitudes toward margil1alized human outgroups and non-human animals. Very few studies, however, have specifically tested this relation empirically. The general purpose of the present research was to determine if such a relation exists and if perceptions of human-animal similarity avail as a common predictor of both types of attitudes. Ideological orientations associated with prejudiced attitudes (Social Dominance Orientation, Right-Wing Authoritarianism, and Universal Orientation) were also examined as individual differences in predicting perceptions of human-animal similarity. As predicted, people who endorsed prejudiced attitudes toward human outgroups (Study 1) and immigrants in particular (Studies 2 and 3), were more likely to endorse prejudiced attitudes toward non-human animals. In Study 2, perceptions that humans are superior (versus similar) to other animals directly predicted higher levels of prejudice toward non-human animals, whereas the effect of human superiority beliefs on immigrant prejudice was mediated by dehumanization. In other words, greater perceptions of humans as superior (versus similar) to other animals "allowed for" greater dehumanization of immigrants, which in turn resulted in heightened immigrant prejudice. Furthermore, people higher in Social Dominance Orientation or Right-Wing Authoritarianism were particularly likely to perceive humans as superior (versus similar) to other animals, whereas people characterized by a greater Universal Orientation were more likely to perceive humans and non-human animals as similar. Study 3 examined whether inducing perceptions of human-animal similarity through experimental manipulation would lead to more favourable attitudes toward non-human animals and immigrants. Participants were randomly assigned to read one of four 11 editorials designed to highlight either the similarities or differences between humans and other animals (i.e., animals are similar to humans; humans are similar to animals;~~nimals are inferior to humans; humans are superior to animals) or to a neutral control condition. Encouragingly, when animals were described as similar to humans, prejudice towards non-human animals and immigrants was significantly lower, and to some extent this finding was also true for people naturally high in prejudice (i.e., high in Social Dominance Orientation or Right-Wing Authoritarianism). Inducing perceptions that nonhuman animals are similar to humans was particularly effective at reducing the tendency to dehumanize immigrants ("re-humanization"), lowering feelings of personal threat regarding one's animal-nature, and at increasing inclusive intergroup representations and empathy, all of which uniquely accounted for the significant decreases in prejudiced attitudes. Implications for research, theory and prejudice interventions are considered.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Similarity of Options and the Measurement of Diversity

This paper analyzes the measurement of the diversity of sets based on the dissimilarity of the objects contained in the set. We discuss axiomatic approaches to diversity measurement and examine the considerations underlying the application of specific measures. Our focus is on descriptive issues: rather than assuming a specific ethical position or restricting attention to properties that are appealing in specific applications, we address the foundations of the measurement issue as such in the context of diversity.

Algorithmes de recommandation musicale

Ce mémoire est composé de trois articles qui s’unissent sous le thème de la recommandation musicale à grande échelle. Nous présentons d’abord une méthode pour effectuer des recommandations musicales en récoltant des étiquettes (tags) décrivant les items et en utilisant cette aura textuelle pour déterminer leur similarité. En plus d’effectuer des recommandations qui sont transparentes et personnalisables, notre méthode, basée sur le contenu, n’est pas victime des problèmes dont souffrent les systèmes de filtrage collaboratif, comme le problème du démarrage à froid (cold start problem). Nous présentons ensuite un algorithme d’apprentissage automatique qui applique des étiquettes à des chansons à partir d’attributs extraits de leur fichier audio. L’ensemble de données que nous utilisons est construit à partir d’une très grande quantité de données sociales provenant du site Last.fm. Nous présentons finalement un algorithme de génération automatique de liste d’écoute personnalisable qui apprend un espace de similarité musical à partir d’attributs audio extraits de chansons jouées dans des listes d’écoute de stations de radio commerciale. En plus d’utiliser cet espace de similarité, notre système prend aussi en compte un nuage d’étiquettes que l’utilisateur est en mesure de manipuler, ce qui lui permet de décrire de manière abstraite la sorte de musique qu’il désire écouter.

Extracting and exploiting word relationships for information retrieval

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Objective neuropsychological deficits in post-traumatic stress disorder and mild traumatic brain injury: what remains beyond symptom similarity?

This exploratory study intends to characterize the neuropsychological profile in persons with post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) using objective measures of cognitive performance. A neuropsychological battery of tests for attention, memory and executive functions was administered to four groups: PTSD (n = 25), mTBI (n = 19), subjects with two formal diagnoses: Post-traumatic Stress Disorder and Mild Traumatic Brain Injury (mTBI/PTSD) (n = 6) and controls (n = 25). Confounding variables, such as medical, developmental or neurological antecedents, were controlled and measures of co-morbid conditions, such as depression and anxiety, were considered. The PTSD and mTBI/PTSD groups reported more anxiety and depressive symptoms. They also presented more cognitive deficits than the mTBI group. Since the two PTSD groups differ in severity of PTSD symptoms but not in severity of depression and anxiety symptoms, the PTSD condition could not be considered as the unique factor affecting the results. The findings underline the importance of controlling for confounding medical and psychological co-morbidities in the evaluation and treatment of PTSD populations, especially when a concomitant mTBI is also suspected.