751 resultados para Mutual recognition


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Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

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The problem of automatic recognition of the fish from the video sequences is discussed in this Master’s Thesis. This is a very urgent issue for many organizations engaged in fish farming in Finland and Russia because the process of automation control and counting of individual species is turning point in the industry. The difficulties and the specific features of the problem have been identified in order to find a solution and propose some recommendations for the components of the automated fish recognition system. Methods such as background subtraction, Kalman filtering and Viola-Jones method were implemented during this work for detection, tracking and estimation of fish parameters. Both the results of the experiments and the choice of the appropriate methods strongly depend on the quality and the type of a video which is used as an input data. Practical experiments have demonstrated that not all methods can produce good results for real data, whereas on synthetic data they operate satisfactorily.

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In the present review, we describe a systematic study of the sulfated polysaccharides from marine invertebrates, which led to the discovery of a carbohydrate-based mechanism of sperm-egg recognition during sea urchin fertilization. We have described unique polymers present in these organisms, especially sulfated fucose-rich compounds found in the egg jelly coat of sea urchins. The polysaccharides have simple, linear structures consisting of repeating units of oligosaccharides. They differ among the various species of sea urchins in specific patterns of sulfation and/or position of the glycosidic linkage within their repeating units. These polysaccharides show species specificity in inducing the acrosome reaction in sea urchin sperm, providing a clear-cut example of a signal transduction event regulated by sulfated polysaccharides. This distinct carbohydrate-mediated mechanism of sperm-egg recognition coexists with the bindin-protein system. Possibly, the genes involved in the biosynthesis of these sulfated fucans did not evolve in concordance with evolutionary distance but underwent a dramatic change near the tip of the Strongylocentrotid tree. Overall, we established a direct causal link between the molecular structure of a sulfated polysaccharide and a cellular physiological event - the induction of the sperm acrosome reaction in sea urchins. Small structural changes modulate an entire system of sperm-egg recognition and species-specific fertilization in sea urchins. We demonstrated that sulfated polysaccharides - in addition to their known function in cell proliferation, development, coagulation, and viral infection - mediate fertilization, and respond to evolutionary mechanisms that lead to species diversity.

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Facial expressions of basic emotions have been widely used to investigate the neural substrates of emotion processing, but little is known about the exact meaning of subjective changes provoked by perceiving facial expressions. Our assumption was that fearful faces would be related to the processing of potential threats, whereas angry faces would be related to the processing of proximal threats. Experimental studies have suggested that serotonin modulates the brain processes underlying defensive responses to environmental threats, facilitating risk assessment behavior elicited by potential threats and inhibiting fight or flight responses to proximal threats. In order to test these predictions about the relationship between fearful and angry faces and defensive behaviors, we carried out a review of the literature about the effects of pharmacological probes that affect 5-HT-mediated neurotransmission on the perception of emotional faces. The hypothesis that angry faces would be processed as a proximal threat and that, as a consequence, their recognition would be impaired by an increase in 5-HT function was not supported by the results reviewed. In contrast, most of the studies that evaluated the behavioral effects of serotonin challenges showed that increased 5-HT neurotransmission facilitates the recognition of fearful faces, whereas its decrease impairs the same performance. These results agree with the hypothesis that fearful faces are processed as potential threats and that 5-HT enhances this brain processing.

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Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing.

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A modified version of the intruder-resident paradigm was used to investigate if social recognition memory lasts at least 24 h. One hundred and forty-six adult male Wistar rats were used. Independent groups of rats were exposed to an intruder for 0.083, 0.5, 2, 24, or 168 h and tested 24 h after the first encounter with the familiar or a different conspecific. Factor analysis was employed to identify associations between behaviors and treatments. Resident rats exhibited a 24-h social recognition memory, as indicated by a 3- to 5-fold decrease in social behaviors in the second encounter with the same conspecific compared to those observed for a different conspecific, when the duration of the first encounter was 2 h or longer. It was possible to distinguish between two different categories of social behaviors and their expression depended on the duration of the first encounter. Sniffing the anogenital area (49.9% of the social behaviors), sniffing the body (17.9%), sniffing the head (3%), and following the conspecific (3.1%), exhibited mostly by resident rats, characterized social investigation and revealed long-term social recognition memory. However, dominance (23.8%) and mild aggression (2.3%), exhibited by both resident and intruders, characterized social agonistic behaviors and were not affected by memory. Differently, sniffing the environment (76.8% of the non-social behaviors) and rearing (14.3%), both exhibited mostly by adult intruder rats, characterized non-social behaviors. Together, these results show that social recognition memory in rats may last at least 24 h after a 2-h or longer exposure to the conspecific.

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The visualization of tools and manipulable objects activates motor-related areas in the cortex, facilitating possible actions toward them. This pattern of activity may underlie the phenomenon of object affordance. Some cortical motor neurons are also covertly activated during the recognition of body parts such as hands. One hypothesis is that different subpopulations of motor neurons in the frontal cortex are activated in each motor program; for example, canonical neurons in the premotor cortex are responsible for the affordance of visual objects, while mirror neurons support motor imagery triggered during handedness recognition. However, the question remains whether these subpopulations work independently. This hypothesis can be tested with a manual reaction time (MRT) task with a priming paradigm to evaluate whether the view of a manipulable object interferes with the motor imagery of the subject's hand. The MRT provides a measure of the course of information processing in the brain and allows indirect evaluation of cognitive processes. Our results suggest that canonical and mirror neurons work together to create a motor plan involving hand movements to facilitate successful object manipulation.

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Metal-ion-mediated base-pairing of nucleic acids has attracted considerable attention during the past decade, since it offers means to expand the genetic code by artificial base-pairs, to create predesigned molecular architecture by metal-ion-mediated inter- or intra-strand cross-links, or to convert double stranded DNA to a nano-scale wire. Such applications largely depend on the presence of a modified nucleobase in both strands engaged in the duplex formation. Hybridization of metal-ion-binding oligonucleotide analogs with natural nucleic acid sequences has received much less attention in spite of obvious applications. While the natural oligonucleotides hybridize with high selectivity, their affinity for complementary sequences is inadequate for a number of applications. In the case of DNA, for example, more than 10 consecutive Watson-Crick base pairs are required for a stable duplex at room temperature, making targeting of sequences shorter than this challenging. For example, many types of cancer exhibit distinctive profiles of oncogenic miRNA, the diagnostics of which is, however, difficult owing to the presence of only short single stranded loop structures. Metallo-oligonucleotides, with their superior affinity towards their natural complements, would offer a way to overcome the low stability of short duplexes. In this study a number of metal-ion-binding surrogate nucleosides were prepared and their interaction with nucleoside 5´-monophosphates (NMPs) has been investigated by 1H NMR spectroscopy. To find metal ion complexes that could discriminate between natural nucleobases upon double helix formation, glycol nucleic acid (GNA) sequences carrying a PdII ion with vacant coordination sites at a predetermined position were synthesized and their affinity to complementary as well as mismatched counterparts quantified by UV-melting measurements.

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Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.

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One group of 12 non learning disabled students and two groups of 12 learning disabled students between the ges of 10 and 12 were measured on implicit and explicit knowledge cquisition. Students in each group implicitly cquired knowledge bout I of 2 vocabulary rules. The vocabulary rules governed the pronunciation of 2 types of pseudowords. After completing the implicit acquisition phase, all groups were administered a test of implicit knowledge. The non learning disabled group and I learning disabled group were then asked to verbalize the knowledge acquired during the initial phase. This was a test of explicit knowledge. All 3 groups were then given a postlest of implicit knowledge. This tcst was a measure of the effectiveness of the employment of the verbalization technique. Results indicate that implicit knowledge capabilities for both the learning disabled and non learning disabled groups were intact. However. there were significant differences between groups on explicit knowledge capabilities. This led to the conclusion that implicit functions show little individual differences, and that explicit functions are affected by ability difference. Furthermore, the employment of the verbalization technique significantly increased POStlest scores for learning disabled students. This suggested that the use of metacognitive techniques was a beneficial learning tool for learning disabled students.

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This thesis poses two fundamental issues regarding Hegel's philosophy of intersubjectivity. Firstly, it examines Kojeve's problematic interpretation of Hegelian intersubjectivity as being solely rooted in the dialectic of lordship and bondage. It is my contention that Kojeve conflates the concepts of recognition {Anerkennung) with that of desire (Begierde), thereby reducing Hegel's philosophy of intersubjectivity to a violent reduction of the other to the same. This is so despite the plenary of examples Hegel uses to define intersubjectivity as the mutual (reciprocal) recognition between the self and the other. Secondly, it examines Hegel's use of Sophocles' Antigone to demonstrate the notion of the individual par excellence. I contend that Hegel's use of Antigone opens a new methodological framework through which to view his philosophy of intersubjectivity. It is Antigone that demonstrates the upheaval of an economy of exchange between the self and the other, whereby the alterity of the other transcends the self Ultimately, Hegel's philosophy of intersubjectivity must be reexamined, not only to dismiss Kojeve's problematic interpretation, but also to pose the possibility that Hegel's philosophy of intersubjectivity can viably account for a philosophy of the other that has a voice in contemporary philosophical debate.

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Adults' expert face recognition is limited to the kinds of faces they encounter on a daily basis (typically upright human faces of the same race). Adults process own-race faces holistically (Le., as a gestalt) and are exquisitely sensitive to small differences among faces in the spacing of features, the shape of individual features and the outline or contour of the face (Maurer, Le Grand, & Mondloch, 2002), however this expertise does not seem to extend to faces from other races. The goal of the current study was to investigate the extent to which the mechanisms that underlie expert face processing of own-race faces extend to other-race faces. Participants from rural Pennsylvania that had minimal exposure to other-race faces were tested on a battery of tasks. They were tested on a memory task, two measures of holistic processing (the composite task and the part/whole task), two measures of spatial and featural processing (the JanelLing task and the scrambledlblurred faces task) and a test of contour processing (JanelLing task) for both own-and other-race faces. No study to date has tested the same participants on all of these tasks. Participants had minimal experience with other-race faces; they had no Chinese family members, friends or had ever traveled to an Asian country. Results from the memory task did not reveal an other-race effect. In the present study, participants also demonstrated holistic processing of both own- and other-race faces on both the composite task and the part/whole task. These findings contradict previous findings that Caucasian adults process own-race faces more holistically than other-race faces. However participants did demonstrate an own-race advantage for processing the spacing among features, consistent with two recent studies that used different manipulations of spacing cues (Hayward et al. 2007; Rhodes et al. 2006). They also demonstrated an other-race effect for the processing of individual features for the Jane/Ling task (a direct measure of featural processing) consistent with previous findings (Rhodes, Hayward, & Winkler, 2006), but not for the scrambled faces task (an indirect measure offeatural processing). There was no own-race advantage for contour processing. Thus, these results lead to the conclusion that individuals may show less sensitivity to the appearance of individual features and the spacing among them in other-race faces, despite processing other-race faces holistically.

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In this thesis, three main questions were addressed using event-related potentials (ERPs): (1) the timing of lexical semantic access, (2) the influence of "top-down" processes on visual word processing, and (3) the influence of "bottom-up" factors on visual word processing. The timing of lexical semantic access was investigated in two studies using different designs. In Study 1,14 participants completed two tasks: a standard lexical decision (LD) task which required a word/nonword decision to each target stimulus, and a semantically primed version (LS) of it using the same category of words (e.g., animal) within each block following which participants made a category judgment. In Study 2, another 12 participants performed a standard semantic priming task, where target stimulus words (e.g., nurse) could be either semantically related or unrelated to their primes (e.g., doctor, tree) but the order of presentation was randomized. We found evidence in both ERP studies that lexical semantic access might occur early within the first 200 ms (at about 170 ms for Study 1 and at about 160 ms for Study 2). Our results were consistent with more recent ERP and eye-tracking studies and are in contrast with the traditional research focus on the N400 component. "Top-down" processes, such as a person's expectation and strategic decisions, were possible in Study 1 because of the blocked design, but they were not for Study 2 with a randomized design. Comparing results from two studies, we found that visual word processing could be affected by a person's expectation and the effect occurred early at a sensory/perceptual stage: a semantic task effect in the PI component at about 100 ms in the ERP was found in Study 1 , but not in Study 2. Furthermore, we found that such "top-down" influence on visual word processing might be mediated through separate mechanisms depending on whether the stimulus was a word or a nonword. "Bottom-up" factors involve inherent characteristics of particular words, such as bigram frequency (the total frequency of two-letter combinations of a word), word frequency (the frequency of the written form of a word), and neighborhood density (the number of words that can be generated by changing one letter of an original word or nonword). A bigram frequency effect was found when comparing the results from Studies 1 and 2, but it was examined more closely in Study 3. Fourteen participants performed a similar standard lexical decision task but the words and nonwords were selected systematically to provide a greater range in the aforementioned factors. As a result, a total of 18 word conditions were created with 18 nonword conditions matched on neighborhood density and neighborhood frequency. Using multiple regression analyses, we foimd that the PI amplitude was significantly related to bigram frequency for both words and nonwords, consistent with results from Studies 1 and 2. In addition, word frequency and neighborhood frequency were also able to influence the PI amplitude separately for words and for nonwords and there appeared to be a spatial dissociation between the two effects: for words, the word frequency effect in PI was found at the left electrode site; for nonwords, the neighborhood frequency effect in PI was fovind at the right elecfrode site. The implications of otir findings are discussed.