Diagnóstico psicopatológico y "analitica existencial": una mirada crítica a la clinica psiquiátrica desde la propuesta de L. Bisnwanger

A partir del siglo XVIII la psiquiatría focaliza su interés por constituirse como discurso científico, como discurso de la verdad acerca de la locura. Hacia 1845, en Alemania, Wilhelm Griesinger, sostiene que las enfermedades mentales son enfermedades del cerebro y propulsa un programa de investigación sobre patología cerebral que busca localizar las regiones asociadas con esta clase de trastornos. "Para Griesinger resultaba de crucial importancia que el estudio de la enfermedad mental no se aislara de la medicina general sino que se mantuviera como una parte integral de ella" (Porter, 2003).Theodor Meynert, propone algo después una clasificación de la enfermedad mental basada en criterios histopatológícos y Carl Wemike, a partir de sus descubrimientos de la localización cerebral de las regiones responsables de la afasia, reauima un intento por relacionar los síntomas psiquiátricos con anormalidades en el funcionamiento cerebral. Esta psiquiatría temprana puso el acento en el cerebro, sus afecciones y funciones básicas, recurriendo en toda instancia a explicaciones nervo-fisiológícas y biológicas y a taxonomías basadas en la etiología, la semíología y la prognosis de la enfermedad . Conviven en todo este período dos discursos, uno anátomo-patológico y otro nosográfico que buscaba caracterizar los síntomas psiquiátricos ordenándolos a partir del estudio de las funciones psicológicas básicas y sus anormalidades; desde ambas perspectivas se crearon lenguajes clasificatorios que intentaban describir a la locura como un conjunto de enfermedades que presentaba, cada una, una sintomatología y una evolución, aspectos diagnósticos, etiológicos y de prognosis (Foucault, 2003)

“Hasta el color me cambia, hasta el pelo fino voy a tener”: La nación argentina en la experiencia de migrantes dominicanos en Río Grande, Tierra del Fuego.

This  paper tackles the way in which the assumptions about the Argentine nation being white and European are expressed in the lived experiences of Dominican migrants in the city of Río Grande, Tierra del Fuego. Firstly,I propose to make a review through the invention of the Argentine nation  during the  late 19's  century. It is focused on aspects related to whiteness and European identity and its consequent denial of Afro-descendants as contemporary subjects. Secondly, I address the concept of the nation and its classifying mechanisms, in which I argue that the nation involves the construction of a fictitious ethnicity. Secondly, I address the concept nation and its classifying mechanisms, in which I argue that the nation involves the construction of a fictitious ethnicity. To delve into the classifying mechanisms, I draw upon two specific cases: the tension between nationalist and racist forms framed within an educational ritual context, and the role that the National Identity Document assumes in its capacity for social control through restricted citizenship. In conclusion, I outline  the explanatory response regarding both,  the incorporation and denial of Dominican migrants into the intended Argentine nation, which is envisioned as white and European, intertwined with the constitutive tension between nationalism and racism.El presente trabajo aborda la forma en que los supuestos de la nación argentina, blanca y europea se expresan en las experiencias vividas de los migrantes dominicanos en la ciudad de Río Grande, Tierra del Fuego. En primer lugar, propongo realizar un breve recorrido sobre la invención de la nación argentina a fines del S. XIX. Me centro en los aspectos referidos a la blanquitud y europeidad como los valores normales de la nación argentina y su consecuente negación de los afrodescendientes como sujetos contemporáneos. En segundo lugar, abordo el concepto de nación y sus dispositivos clasificatorios, en el que expongo que la nación supone la construcción de una etnicidad ficticia. Para desarrollar los dispositivos clasificatorios recurro a dos casos particulares: la tensión entre formas nacionalistas y racistas enmarcadas en un contexto de ritual-escolar; y, el rol que asume el Documento Nacional de Identidad en su capacidad de control social desde la ciudadanía restringida. Como reflexión final esbozo una respuesta exploratoria en torno a la incorporación y negación de los migrantes dominicanos a la pretendida nación argentina, blanca y europea relacionada a la tensión constitutiva entre nacionalismo y racismo

Handshape recognition for Argentinian Sign Language using ProbSom

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearing-impaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA handshapes, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%

Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition

Human action recognition from skeletal data is an important and active area of research in which the state of the art has not yet achieved near-perfect accuracy on many well-known datasets. In this paper, we introduce the Distribution of Action Movements Descriptor, a novel action descriptor based on the distribution of the directions of the motions of the joints between frames, over the set of all possible motions in the dataset. The descriptor is computed as a normalized histogram over a set of representative directions of the joints, which are in turn obtained via clustering. While the descriptor is global in the sense that it represents the overall distribution of movement directions of an action, it is able to partially retain its temporal structure by applying a windowing scheme. The descriptor, together with a standard classifier, outperforms several state-of-the-art techniques on many well-known datasets

Handshape recognition for Argentinian Sign Language using ProbSom

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic

Handshape recognition for Argentinian Sign Language using ProbSom

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic

Sign Languague Recognition without frame-sequencing constraints: A proof of concept on the Argentinian Sign Language

Automatic sign language recognition (SLR) is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language for the hearing population. SLR systems usually employ Hidden Markov Models, Dynamic Time Warping or similar models to recognize signs. Such techniques exploit the sequential ordering of frames to reduce the number of hypothesis. This paper presents a general probabilistic model for sign classification that combines sub-classifiers based on different types of features such as position, movement and handshape. The model employs a bag-of-words approach in all classification steps, to explore the hypothesis that ordering is not essential for recognition. The proposed model achieved an accuracy rate of 97% on an Argentinian Sign Language dataset containing 64 classes of signs and 3200 samples, providing some evidence that indeed recognition without ordering is possible.Comment: IBERAMIA 201

LSA64: An Argentinian Sign Language Dataset

Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population. Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language. This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs. We also present a pre-processed version of the dataset, from which we computed statistics of movement, position and handshape of the signs.Comment: Published in CACIC XXI

LSA64: An Argentinian Sign Language Dataset

Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population. Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language. This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

LSA64: An Argentinian Sign Language Dataset

Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population. Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language. This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI