Svariati contributi comparsi negli ultimi decenni hanno avviato la parziale rilettura del pensiero linguistico di Aristotele. Su queste premesse, lo studio si propone, a partire dall’analisi dei testi più significativi, di ricostruire una teoria della metafora coerente con il resto del sistema filosofico aristotelico. Aristotele concepisce la metafora come un fatto di lingua, e ne delinea le principali caratteristiche e funzioni all’interno della comunicazione. Per Aristotele, tuttavia, la metafora è anche il segno del processo mentale che l’ha prodotta. Il pensiero metaforico, che soggiace alla metafora intesa semplicemente come tropo, è un’attività cognitiva che si fonda sulla capacità umana di cogliere la somiglianza. A sua volta, il vedere ciò che è simile è una capacità che precede il linguaggio, ma tuttavia si connette inevitabilmente ad esso sul piano sia analogico che logico, nel momento del concepimento di un giudizio. Il processo metaforico è dunque uno strumento di conoscenza che, procedendo dal pensiero al linguaggio, permette all’uomo di cogliere le relazioni tra gli enti, mettendolo a sua volta in relazione con il mondo. / Several studies have recently started a partial reinterpretation of Aristotle’s linguistics. Moving from these premises, this work tries to rebuild Aristotle’s theory of metaphor, in conformity with his philosophy and the analysis of his most relevant papers. Aristotle conceives metaphor a fact of language, and defines metaphor most important features and functions in relationship with communication. Nevertheless Aristotle means metaphor as a sign of the psychical process that produces it. Metaphorical thought, which is in our mind and which we can understand looking through the trope of metaphor, is a cognitive process, based on the human capability of catching similarity. Seeing what is similar is a capability that precedes utterance, but nevertheless it is connected to the language in an analogical and logical way every time we make an assertion. Metaphorical action is a cognitive appliance that, proceeding from thought to utterance, makes man capable of understanding relationships between things, and brings man himself in relationship with the world.

<p>One way of modeling human knowledge is by using multidimensional spaces, in which an object is represented as a point in the space, and the distances among the points reflect the similarities among the represented objects. The distances are measured with some metric, commonly some instance of the Minkowski metric. The instances differ with the magnitude of the so-called r-parameter. The instances most commonly mentioned in the literature are the ones where r equals 1, 2 and infinity.</p><p>Cognitive scientists have found out that different metrics are suited to describe different dimensional combinations. From these findings an important distinction between integral and separable dimensions has been stated (Garner, 1974). Separable dimensions, e.g. size and form, are best described by the city-block metric, where r equals 1, and integral dimensions, such as the color dimensions, are best described by the Euclidean metric, where r equals 2. Developmental psychologists have formulated a hypothesis saying that small children perceive many dimensional combinations as integral whereas adults perceive the same combinations as separable. Thus, there seems to be a shift towards increasing separability with age or maturity.</p><p>Earlier experiments show the same phenomenon in adult short-term learning with novel stimuli. In these experiments, the stimuli were first perceived as rather integral and were then turning more separable, indicated by the Minkowski-r. This indicates a shift towards increasing separability with familiarity or skill.</p><p>This dissertation aims at investigating the generality of this phenomenon. Five similarity-rating experiments are conducted, for which the best fitting metric for the first half of the session is compared to the last half of the session. If the Minkowski-r is lower for the last half compared to the first half, it is considered to indicate increasing separability.</p><p>The conclusion is that the phenomenon of increasing separability during short-term learning cannot be found in these experiments, at least not given the operational definition of increasing separability as a function of a decreasing Minkowski-r. An alternative definition of increasing separability is suggested, where an r-value ‘retreating’ 2.0 indicates increasing separability, i.e. when the r-value of the best fitting metric for the last half of a similarity-rating session is further away from 2.0 compared to the first half of the session.</p>

<p>Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. Therefore, determination of protein-protein interaction is fundamental for the understanding of the cell’s lifecycle and functions. The function of a protein is also largely determined by its interactions with other proteins. The amount of protein-protein interaction data available has multiplied by the emergence of large-scale technologies for detecting them, but the drawback of such measures is the relatively high amount of noise present in the data. It is time consuming to experimentally determine protein-protein interactions and therefore the aim of this project is to create a computational method that predicts interactions with high sensitivity and specificity. Semantic similarity measures were applied across the Gene Ontology terms assigned to proteins in S. cerevisiae to predict protein-protein interactions. Three semantic similarity measures were tested to see which one performs best in predicting such interactions. Based on the results, a method that predicts function of proteins in connection with connectivity was devised. The results show that semantic similarity is a useful measure for predicting protein-protein interactions.</p>

<p>In this masters’ thesis report we present a graph theoretical method used for automatic bilingual lexicon acquisition with parallel texts. We analyze the concept of graph similarity and give an interpretation, of the parallel texts, connected to the vector space model. We represent the parallel texts by a directed, tripartite graph and from here use the corresponding adjacency matrix, A, to compute the similarity of the graph. By solving the eigenvalue problem ρS = ASAT + ATSA we obtain the self-similarity matrix S and the Perron root ρ. A rank k approximation of the self-similarity matrix is computed by implementations of the singular value decomposition and the non-negative matrix factorization algorithm GD-CLS. We construct an algorithm in order to extract the bilingual lexicon from the self-similarity matrix and apply a statistical model to estimate the precision, the correctness, of the translations in the bilingual lexicon. The best result is achieved with an application of the vector space model with a precision of about 80 %. This is a good result and can be compared with the precision of about 60 % found in the literature.</p>

Includes vita. / Title from PDF of title page. / Document formatted into pages; contains 145 pages. / Thesis (Ph.D.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: In this dissertation we introduce several novel techniques for performing data mining on web documents which utilize graph representations of document content. Graphs are more robust than typical vector representations as they can model structural information that is usually lost when converting the original web document content to a vector representation. For example, we can capture information such as the location, order and proximity of term occurrence, which is discarded under the standard document vector representation models. Many machine learning methods rely on distance computations, centroid calculations, and other numerical techniques. Thus many of these methods have not been applied to data represented by graphs since no suitable graph-theoretical concepts were previously available. We introduce the novel Graph Hierarchy Construction Algorithm (GHCA), which performs topic-oriented hierarchical clustering of web search results modeled using graphs. / ABSTRACT: The system we created around this new algorithm and its prior version is compared with similar web search clustering systems to gauge its usefulness. An important advantage of this approach over conventional web search systems is that the results are better organized and more easily browsed by users. Next we present extensions to classical machine learning algorithms, such as the k-means clustering algorithm and the k-Nearest Neighbors classification algorithm, which allows the use of graphs as fundamental data items instead of vectors. We perform experiments comparing the performance of the new graph-based methods to the traditional vector-based methods for three web document collections. Our experimental results show an improvement for the graph approaches over the vector approaches for both clustering and classification of web documents. / ABSTRACT: An important advantage of the graph representations we propose is that they allow the computation of graph similarity in polynomial time; usually the determination of graph similarity with the techniques we use is an NP-Complete problem. In fact, there are some cases where the execution time of the graph-oriented approach was faster than the vector approaches. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Thesis (M. Sc.)--Acadia University, 2001. / Includes bibliographical references (leaves 87-91). Also available on the Internet via the World Wide Web.

Purpose: This thesis examines the effect of listener characteristics (i.e., cognition and vocabulary) and language-based factors (i.e., lexical frequency and phonological similarity) on speech recognition accuracy in adverse listening conditions. Method: Fifty listeners (40 females and 10 males) aged 18-33 years and with normal hearing (puretone thresholds ≤ 20 dB HL, 0.25-8 kHz) participated. They completed a speech perception experiment, which required listeners to repeat back non-sensical English phrases presented at a variety of signal-to-noise ratios (-5, -2, +1, and +4 dB SNRs). In addition, all listeners undertook assessments of vocabulary knowledge (PPVT-IV) and cognition (WAIS -IV). The primary dependent variable was individual content word recognition accuracy, and results were analysed using binomial mixed effects modelling. Results: Listeners demonstrated variability in their speech recognition abilities, and their vocabulary and cognitive scores. Statistical analysis revealed that listener-based factors affected word recognition. Listeners with faster processing speed and larger working memories exhibited higher word recognition accuracy. Surprisingly, listeners with higher non-verbal intelligence scores exhibited lower word recognition accuracy. Vocabulary knowledge interacted with SNR, such that as the listening conditions became more favourable, listeners with larger receptive vocabularies identified more words correctly. Similarly, main effects were also present for language-based factors. The more phonologically distinct a word was, the more likely it was to be correctly identified; higher frequency words were more likely to be accurately recognised. In addition, higher frequency words were identified more accurately at higher SNR levels. Finally, listener- and language-based factors interacted. The positive effect of working memory on word recognition was reversed as word frequency increased; on the other hand non-verbal intelligence’s negative influence on word recognition was reversed as word frequency increased. Conclusion: In the current cohort, listener and language-based factors interacted in the process of word recognition in noise. These results provide an insight into the underlying speech recognition mechanisms in adverse conditions. Further understanding of how these listener differences affect an individual’s speech processing may lead to the development of improved signal processing techniques and rehabilitation strategies.

Siesta Key Beach, located on the Gulf Coast of Florida, is frequently mentioned among the top ten beaches in the US. In summer 2004, high levels of indicator bacteria caused health warnings to be posted, and a storm drainage system was implicated as a possible source of microbial contamination. A study was initiated to determine whether indicator bacteria that persisted in the stormwater system could contribute to high microbial loads in receiving waters. Two sampling events, one within 48 hours of a rain event and the other during dry conditions, were conducted. Water and sediment samples were taken at various sites from the storm drainage system to the beach. Fecal coliforms and Enterococcus spp. were enumerated, and genotypic fingerprints of E. coli and Enterococcus spp. were generated by BOX-PCR. Diversity of E. coli and Enterococcus populations was calculated with the Shannon-Weiner diversity index. Similarity of E. coli and Enterococcus populations was calculated with the population similarity coefficient. After the rain event, levels of fecal coliforms and Enterococcus spp. were high in sediments and exceeded the regulatory standard for all water samples. In dry conditions, levels were lower in water samples, but still high in sediment samples. Significantly greater population diversity was observed in the rain event compared to the dry event for both E. coli and Enterococcus populations, and greater population similarity was vi observed in dry conditions. Enterococcus population diversity was significantly higher in untreated sewage and the Siesta Key rain event when compared to dry conditions, and to a site on the Myakka River (no known human input or urban stormwater runoff). Siesta Key populations in dry conditions were most similar to Myakka, and sewage was the least similar to all other populations. Increased population similarity for E. coli and Enterococcus spp. during dry conditions suggests that a portion of the population is composed of “survivor” isolates. Persistence of survivor isolates in the storm drainage system, where urban runoff can sit for days, suggests a reservoir for indicator bacteria that can be flushed through the system to the Gulf, causing high levels of indicator bacteria in receiving waters.

The purpose of this dissertation is to explore whether rehabilitation counselor/consumer racial similarity is related to rehabilitation services provided and employment outcomes achieved. This research contributes additional information to the research base on how to educate and train practicing vocational rehabilitation counselors to support culturally and linguistically diverse (CLD) consumers more effectively. Information regarding racial dyads may help to articulate whether CLD counselors are more effective with CLD consumers. Utilizing the RSA-911 data file submitted by California for fiscal year 2006 as well as 189 Multicultural Counseling Inventory (MCI) surveys collected on practicing vocational rehabilitation counselors, ANOVA, MANOVA, ANCOVA and chi-square analysis were used to measure relationships among counselor and consumer dyads that were either similar or dissimilar in ethnicity and case service variables and outcome variables. Additional analysis focused on the influence of high versus low cultural competency of rehabilitation counselors and the same consumer case service and outcome variables. Study results revealed statistically significant differences on MCI total scores between counselor ethnic groups, with Hispanic counselors demonstrating the highest mean scores overall on the MCI. In addition, significant differences existed between counselor ethnic groups on MCI total scores, as well as all four subscale scores. Chi-square was used to investigate the effect of counselor ethnicity and cultural competency scores on the case service variables of job search, rehabilitation technology, maintenance and college training, as well as outcome variables of competitive employment. MANOVA and ANCOVA were used for the continuous outcome variables of cost of services, wages at closure and weekly earnings at closure. Significant chi-square results were found for some of the service and outcome variables for both the matched counselor/consumer dyads and the counselor competency score groups. More significant findings existed in the ANCOVA analysis for cultural competency scores than for matched ethnicity groups. Implications of the findings and suggestions for future research are discussed. / text

Recent image quality assessment (IQA) metrics achieve high correlation with human perception of image quality. Naturally, it is of interest to produce even better results. One promising method is to weight image quality measurements by visual importance. To this end, we describe three strategies - visual fixation-based weighting, quality-based weighting and weighting based on distribution of local quality scores about the mean. By contrast with some prior studies we find that these strategies can improve the correlations with subjective judgment significantly. We demonstrate improvements on the SSIM index in both its multi-scale and single-scale versions, using the LIVE database as a test-bed. / text