Facial attributes recognition using computer vision to detect drowsiness and distraction in drivers

Driving is an activity that requires a high degree of concentration on the part of the person who performs it, since the slightest negligence is sufficient to provoke an accident with the consequent material and/or human losses. According to the most recent study published by the World Health Organization (WHO) in 2013, it was estimated that 1.25 million people died as a result of traffic accidents, whereas between 20 and 50 million did not die but consequences resulted in chronic conditions. Many of these accidents are caused by what is known as inattention. This term encloses different conditions such as distraction and drowsiness, which are, precisely, the ones that cause more fatalities. Many publications and research have tried to set figures indicating the consequences of inattention (and its subtypes), but there is no exact number of the accidents caused by inattention since all these studies have been carried out in different places, different time frames and, therefore, under different conditions. Overall, it has been estimated that inattention causes between 25% and 75% of accidents and near-accidents. A study on drowsiness while driving in ten European countries found that fatigue risks increasing reaction time by 86% and it is the fourth leading cause of death on Spanish roads. Distraction is also a major contributor to fatal accidents in Spain. According to the Directorate General of Traffic (DGT), distraction is the first violation found in fatal accidents, 13.15% of the cases. Overall, considering both distraction and drowsiness, the latest statistics on inattentive driving in Spanish drivers are alarming, appearing as the leading cause of fatalities (36%), well above excessive speed (21%) or alcohol consumption (11%).The reason for this PhD thesis is the direct consequences of the abovementioned figures and its purpose is to provide mechanisms to help reduce driver inattention effects using computer vision techniques. The extraction of facial attributes can be used to detect inattention robustly.Specifically, research establishes a frame of reference to characterize distraction in drivers in order to provide solid foundations for future research [1]. Based on this research [1], an architecture based on the analysis of visual characteristics has been proposed, constructed and validated by using techniques of computer vision and automatic learning for the detection of both distraction and drowsiness [2], integrating several innovative elements in order to operate in a completely autonomous way for the robust detection of the main visual indicators characterizing the driver's both distraction and drowsiness: (1) a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction [3]; (2) a face processing algorithm based on Local Binary Patterns (LBP) and Support Vector Machine (SVM) to detect facial attributes [4]; (3) detection unit for the presence/absence of the driver using both a marker and a machine learning algorithm [2]; (4) robust face tracking algorithm based on both the position of the camera and the face detection algorithm [2]; (5) a face alignment and normalization algorithm to improve the eyes state detection [3]; (6) driver drowsiness detection based on the eyes state detection over time [2]; (7) driver distraction detection based on the position of the head over time [2]. This architecture has been validated, firstly, with reference databases testing the different modules that compose it, and, secondly, with users in real environments, obtaining in both cases, excellent results with a suitable computational load for the embedded devices in vehicle environments [2]. In connection with the tests performed in real-world settings, 16 drivers were involved performing several activities imitating different signs of sleepiness and distraction. Overall, an accuracy of 93.11% is obtained considering all activities and all drivers [2].Additionally, other contributions of this thesis have been experimentally validated in controlled settings, but are expected to be included in the abovementioned architecture: (1) glasses detection algorithm prior to the detection of the eyes state [3] (the eyes state can not be accurately obtained if the driver is wearing glasses or sunglasses [1]); (2) face recognition and spoofing detection algorithm to identify the driver [5]; (3) physiological information (Heart Rate, Respiration Rate and Heart Rate Variability) are extracted from the users face [6] (using this information, cognitive load and stress can be obtained [1]); (4) a real-time big data architecture to process a large number of relatively small-sized images [7]. Therefore, future work will include these points to complete the architecture

Co-option of Neutrophil Fates by Tissue Environments.

Classically considered short-lived and purely defensive leukocytes, neutrophils are unique in their fast and moldable response to stimulation. This plastic behavior may underlie variable and even antagonistic functions during inflammation or cancer, yet the full spectrum of neutrophil properties as they enter healthy tissues remains unexplored. Using a new model to track neutrophil fates, we found short but variable lifetimes across multiple tissues. Through analysis of the receptor, transcriptional, and chromatin accessibility landscapes, we identify varying neutrophil states and assign non-canonical functions, including vascular repair and hematopoietic homeostasis. Accordingly, depletion of neutrophils compromised angiogenesis during early age, genotoxic injury, and viral infection, and impaired hematopoietic recovery after irradiation. Neutrophils acquired these properties in target tissues, a process that, in the lungs, occurred in CXCL12-rich areas and relied on CXCR4. Our results reveal that tissues co-opt neutrophils en route for elimination to induce programs that support their physiological demands.This study was supported byIntramural grants from the Severo Ochoa program (IGP-SO), a grant from Fundacio la Marato de TV3 (120/C/2015-20153032), grant SAF2015-65607-R fromMinisterio de Ciencia e Innovacion (MICINN) with co-funding by Fondo Eu-ropeo de Desarrollo Regional (FEDER), RTI2018-095497-B-I00 from MICINN,HR17_00527 from Fundacion La Caixa, and Transatlantic Network of Excel-lence (TNE-18CVD04) from the Leducq Foundation to A.H. I.B. is supportedby fellowship MSCA-IF-EF-748381 and EMBO short-term fellowship 8261.A.R.-P. is supported by a fellowship (BES-2016-076635) and J.A.N.-A. byfellowship SVP-2014-068595 from MICINN. R.O. is supported by ERC startinggrant 759532, Italian Telethon Foundation SR-Tiget grant award F04, ItalianMoH grant GR-201602362156, AIRC MFAG 20247, Cariplo Foundation grant2015-0990, and the EU Infect-ERA 126. C.S. is supported by the SFB 1123,project A07, as well as by the DZHK (German Centre for Cardiovascular Research) and the BMBF (German Ministry of Education and Research) grant81Z0600204. L.G.N. is supported by SIgN core funding from A*STAR. The CNIC is supported by the MICINN and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence (MICINN award SEV-2015-0505). G.F.-C. issupported by the Spanish Ministerio de Ciencia e Innovacio ́n (grantPID2019-110895RB-100) and Junta de Comunidades de Castilla-La Mancha(grant SBPLY/19/180501/000211). C.R. received funding from the BoehingerIngelheim Foundation (consortium grant ‘‘Novel and Neglected CardiovascularRisk Factors’’) and German Federal Ministry of Education and Research(BMBF 01EO1503) and is a Fellow of the Gutenberg Research College (GFK)at the Johannes Gutenberg-University MainzS

Facial attributes recognition using computer vision to detect drowsiness and distraction in drivers

Driving is an activity that requires a high degree of concentration on the part of the person who performs it, since the slightest negligence is sufficient to provoke an accident with the consequent material and/or human losses. According to the most recent study published by the World Health Organization (WHO) in 2013, it was estimated that 1.25 million people died as a result of traffic accidents, whereas between 20 and 50 million did not die but consequences resulted in chronic conditions. Many of these accidents are caused by what is known as inattention. This term encloses different conditions such as distraction and drowsiness, which are, precisely, the ones that cause more fatalities. Many publications and research have tried to set figures indicating the consequences of inattention (and its subtypes), but there is no exact number of the accidents caused by inattention since all these studies have been carried out in different places, different time frames and, therefore, under different conditions. Overall, it has been estimated that inattention causes between 25% and 75% of accidents and near-accidents. A study on drowsiness while driving in ten European countries found that fatigue risks increasing reaction time by 86% and it is the fourth leading cause of death on Spanish roads. Distraction is also a major contributor to fatal accidents in Spain. According to the Directorate General of Traffic (DGT), distraction is the first violation found in fatal accidents, 13.15% of the cases. Overall, considering both distraction and drowsiness, the latest statistics on inattentive driving in Spanish drivers are alarming, appearing as the leading cause of fatalities (36%), well above excessive speed (21%) or alcohol consumption (11%).The reason for this PhD thesis is the direct consequences of the abovementioned figures and its purpose is to provide mechanisms to help reduce driver inattention effects using computer vision techniques. The extraction of facial attributes can be used to detect inattention robustly.Specifically, research establishes a frame of reference to characterize distraction in drivers in order to provide solid foundations for future research [1]. Based on this research [1], an architecture based on the analysis of visual characteristics has been proposed, constructed and validated by using techniques of computer vision and automatic learning for the detection of both distraction and drowsiness [2], integrating several innovative elements in order to operate in a completely autonomous way for the robust detection of the main visual indicators characterizing the driver's both distraction and drowsiness: (1) a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction [3]; (2) a face processing algorithm based on Local Binary Patterns (LBP) and Support Vector Machine (SVM) to detect facial attributes [4]; (3) detection unit for the presence/absence of the driver using both a marker and a machine learning algorithm [2]; (4) robust face tracking algorithm based on both the position of the camera and the face detection algorithm [2]; (5) a face alignment and normalization algorithm to improve the eyes state detection [3]; (6) driver drowsiness detection based on the eyes state detection over time [2]; (7) driver distraction detection based on the position of the head over time [2]. This architecture has been validated, firstly, with reference databases testing the different modules that compose it, and, secondly, with users in real environments, obtaining in both cases, excellent results with a suitable computational load for the embedded devices in vehicle environments [2]. In connection with the tests performed in real-world settings, 16 drivers were involved performing several activities imitating different signs of sleepiness and distraction. Overall, an accuracy of 93.11% is obtained considering all activities and all drivers [2].Additionally, other contributions of this thesis have been experimentally validated in controlled settings, but are expected to be included in the abovementioned architecture: (1) glasses detection algorithm prior to the detection of the eyes state [3] (the eyes state can not be accurately obtained if the driver is wearing glasses or sunglasses [1]); (2) face recognition and spoofing detection algorithm to identify the driver [5]; (3) physiological information (Heart Rate, Respiration Rate and Heart Rate Variability) are extracted from the users face [6] (using this information, cognitive load and stress can be obtained [1]); (4) a real-time big data architecture to process a large number of relatively small-sized images [7]. Therefore, future work will include these points to complete the architecture

Reconocimiento de atributos faciales mediante visión por computador para la detección de distracción y somnolencia en conductores

Tesis doctoral por el sistema de compendio de publicacionesLa conducción es una actividad que requiere un alto grado de concentración por parte de la persona que la realiza, ya que el más pequeño descuido es suficiente para sufrir un accidente con las consiguientes pérdidas materiales y/o humanas. De acuerdo con el estudio más reciente publicado por la Organización Mundial de la Salud (OMS) en 2013, se estimó que 1.25 millones de personas mueren como resultado de accidentes de tráfico y entre 20 y 50 millones más sufren accidentes sin perder la vida pero pudiendo derivar en dolencias crónicas. Todas estas muertes y accidentes no sólo afectan de manera directa a los familiares de las víctimas sino que, además, tienen un alto coste sobre los presupuestos de los gobiernos, que se estima entre un 3 y un 5% del producto interior bruto. De todos estos accidentes, muchos son provocados por lo que se conoce como inatención. Este término engloba diferentes estados del conductor, como pueden ser la distracción y la somnolencia, siendo precisamente éstos los que más fatalidades ocasionan. Existen muchas publicaciones e investigaciones que intentan poner cifras que indiquen las consecuencias producidas por la inatención (y sus subtipos), pero no existe una figura exacta sobre los accidentes causados por la inatención puesto que todos estos estudios están realizados en diferentes lugares, diferentes marcos temporales, y por tanto, en diferentes condiciones. En líneas generales, se calcula que la inatención ocasiona entre el 25% y el 75% de los accidentes y casi-accidentes. En un estudio realizado en 10 países europeos acerca de somnolencia y conducción se determinó que dicha conjunción incrementa el tiempo de reacción en un 86% y es la cuarta causa de muerte en las carreteras españolas. Además, cabe destacar que el 75% de los conductores españoles han sufrido episodios de somnolencia mientras conducían, porcentaje muy superior a la media del 47% que han admitido este hecho. Además, otro factor importante a tener en cuenta es que, aunque los accidentes producidos por la somnolencia suelen ser muy graves (vistas las estadísticas anteriores de mortalidad), muchos conductores infravaloran esta situación y conducen aunque noten la presencia de sus síntomas. Bostezos frecuentes, cabeceos, visión borrosa, caída de párpados y esfuerzos por mantener tanto la atención como los ojos abiertos son signos habituales de somnolencia. Respecto a la distracción, éste es uno de los factores que más fatalidades ocasiona en España. De acuerdo con la Dirección General de Tráfico (DGT), la distracción es la primera causa detectada en los accidentes con víctimas, un 13.15% de los casos. Los últimos datos y estadísticas arrojados por inatención en los conductores españoles son dramáticos, siendo la causa que más víctimas mortales ocasiona (36%), muy por encima de la velocidad inadecuada (21%) o el nivel de alcoholemia (11%). A causa de estas cifras y sus consecuencias, la inatención se ha convertido en un campo ampliamente estudiado por la comunidad investigadora, donde los estudios y soluciones para luchar contra la distracción y la somnolencia, en particular, y la inatención, en general, se pueden clasificar en tres grandes grupos: 1) métodos basados en el análisis del comportamiento del vehículo, 2) métodos basados en el análisis de variables fisiológicas del conductor capturadas por diferentes sensores y 3) métodos basados en el análisis de características visuales del conductor mediante la captura de imágenes aplicando métodos de visión por computador, los cuales, por sus características, se han posicionado como una forma no intrusiva y eficaz para la detección tanto de la distracción como de la somnolencia. La motivación de la presente tesis doctoral es consecuencia directa de las cifras anteriores y radica en ofrecer mecanismos para detectar y reducir los efectos ocasionados por la inatención en los conductores. Es por ello que el objetivo principal del presente trabajo sea alcanzar un mayor grado de conocimiento en todo lo relacionado con la inatención en los conductores, teniendo como fin último la reducción del número de accidentes y víctimas mortales ocasionados por esta causa haciendo uso de herramientas de la información y la comunicación (TIC). A tal efecto, la investigación se ha centrado en el reconocimiento de atributos faciales, que puedan ser empleados a posteriori para una detección robusta de indicadores, que permitan una clasificación de episodios de distracción y somnolencia en conductores. En segundo lugar, la investigación ha intentado establecer un marco de referencia para caracterizar la distracción en los conductores con lo que futuras investigaciones tengan un punto de partida como referente. En tercer lugar, se ha propuesto, construido y validado una arquitectura basada en el análisis de características visuales mediante el empleo de técnicas de visión por computador y aprendizaje automático para la detección tanto de la distracción como de la somnolencia. En concreto, se propone una arquitectura de procesamiento especialmente diseñada para operar en entornos vehiculares, con una carga computacional muy baja y fácilmente integrable en dispositivos con reducidas capacidades de cómputo, capaz de lidiar con imágenes en distintas condiciones muy presentes en este tipo de entornos. El sistema de control propuesto integra varios elementos innovadores permitiendo que pueda operar de forma completamente autónoma para la detección robusta de los principales indicadores visuales, caracterizando tanto la distracción como la somnolencia del conductor. La arquitectura se ha validado, en primer lugar, con bases de datos de referencia validando los diferentes módulos que la componen y, en segundo lugar, con usuarios en entornos reales obteniendo, en ambos casos, unos resultados prometedores con una carga computacional adecuada para los dispositivos embebidos habituales en entornos vehiculares. RESUMEN (en Inglés) Driving is an activity that requires a high degree of concentration on the part of the person who performs it since the slightest negligence is sufficient to provoke an accident with the consequent material and/or human losses. According to the most recent study published by the World Health Organization (WHO) in 2013, it was estimated that 1.25 million people die as a result of traffic accidents, whereas between 20 and 50 million do not die but consequences may result in chronic conditions. All these deaths and accidents not only have a direct impact on victims and families, but they also mean a high cost for government budgets, estimated at between 3% and 5% of their Gross Domestic Product (GDP). Many of these accidents are caused by what is known as inattention. This term encloses a driver's different conditions such as distraction and drowsiness, which are, precisely, the ones that cause more fatalities. Many publications and research have tried to set figures that indicate the consequences of inattention (and its subtypes), but there is no exact number of the accidents caused by inattention since all these studies have been carried out in different places, different time frames and, therefore, under different conditions. Overall, it has been estimated that inattention causes between 25% and 75% of accidents and near-accidents. A study on drowsiness while driving in ten European countries found that fatigue risks increasing reaction time by 86% and it is the fourth leading cause of death on Spanish roads. In addition, it is noted that 75% of Spanish drivers have suffered episodes of sleepiness while driving, a much higher percentage than the average of 47% who admitted this fact. In addition, another important factor to consider is that, although accidents caused by drowsiness are usually very serious (having regard to the abovementioned fatal statistics), many drivers underestimate this situation and drive even if they notice the presence of symptoms. Frequent yawning, pitching movements, blurred vision, drooping upper eyelids and efforts to keep both attention and eyes open are common signs of drowsiness. With respect to distraction, this is a major contributor to fatal accidents in Spain. According to the Directorate General of Traffic (DGT), distraction is the first violation found in fatal accidents, 13.15% of the cases. The latest statistics on inattentive driving in Spanish drivers are alarming, appearing as the leading cause of fatalities (36%), well above excessive speed (21%) or alcohol consumption (11%). Because of these figures and their consequences, inattention has become a widely studied field by the research community, whose studies and solutions to combat distraction and sleepiness, in particular, and inattention, in general, can be divided into three broad groups: 1) methods based on the analysis of the behavior of the vehicle, 2) methods based on the analysis of the driver`s physiological variables captured by different sensors and 3) methods based on the analysis of the driver's visual characteristics by capturing images using computer vision methods, which, for their non-intrusive and effective characteristics, have become a leading way to detect both distraction and drowsiness. The reason for this PhD thesis is the direct consequences of the abovementioned figures and its purpose is to provide mechanisms to help reduce driver inattention effects. That is why the main objective is to reach a better knowledge regarding driving inattention in order to reduce the number of accidents and fatalities. The main focus is that the extraction of facial attributes in a solid way could be used for a robust detection of indicators, which enables a classification of distraction and drowsiness episodes in drivers. Secondly, research has attempted to establish a frame of reference to characterize distraction in drivers in order to provide solid foundations for future research. Thirdly, an architecture based on the analysis of visual characteristics has been proposed, constructed and validated by using techniques of computer vision and automatic learning for the detection of both distraction and drowsiness. In particular, a processing architecture specially designed to operate in vehicular environments is proposed, with a very low computational load and easily embeddable into devices with reduced computational capacities in order to deal with images in the different conditions prevailing in this type of environments. The proposed control system integrates several innovative elements in order to operate in a completely autonomous way for the robust detection of the main visual indicators characterizing the driver`s both distraction and drowsiness. The architecture has been validated, firstly, with reference databases testing the different modules that compose it, and, secondly, with users in real environments, obtaining in both cases, promising results with a suitable computational load for the embedded devices in vehicle environments

Automatic System to Detect Both Distraction and Drowsiness in Drivers Using Robust Visual Features

[ES] De acuerdo con un reciente estudio publicado por la Organización Mundial de la Salud (OMS), se estima que 1.25 millones de personas mueren como resultado de accidentes de tráfico. De todos ellos, muchos son provocados por lo que se conoce como inatención, cuyos principales factores contribuyentes son tanto la distracción como la somnolencia. En líneas generales, se calcula que la inatención ocasiona entre el 25% y el 75% de los accidentes y casi-accidentes. A causa de estas cifras y sus consecuencias se ha convertido en un campo ampliamente estudiado por la comunidad investigadora, donde diferentes estudios y soluciones han sido propuestos, pudiendo destacar los métodos basados en visión por computador como uno de los más prometedores para la detección robusta de estos eventos de inatención. El objetivo del presente artículo es el de proponer, construir y validar una arquitectura especialmente diseñada para operar en entornos vehiculares basada en el análisis de características visuales mediante el empleo de técnicas de visión por computador y aprendizaje automático para la detección tanto de la distracción como de la somnolencia en los conductores. El sistema se ha validado, en primer lugar, con bases de datos de referencia testeando los diferentes módulos que la componen. En concreto, se detecta la presencia o ausencia del conductor con una precisión del 100%, 90.56%, 88.96% por medio de un marcador ubicado en el reposacabezas del conductor, por medio del operador LBP, o por medio del operador CS-LBP, respectivamente. En lo que respecta a la validación mediante la base de datos CEW para la detección del estado de los ojos, se obtiene una precisión de 93.39% y de 91.84% utilizando una nueva aproximación basada en LBP (LBP_RO) y otra basada en el operador CS-LBP (CS-LBP_RO). Tras la realización de varios experimentos para ubicar la cámara en el lugar más adecuado, se posicionó la misma en el salpicadero, pudiendo aumentar la precisión en la detección de la región facial de un 86.88% a un 96.46%. Las pruebas en entornos reales se realizaron durante varios días recogiendo condiciones lumínicas muy diferentes durante las horas diurnas involucrando a 16 conductores, los cuales realizaron diversas actividades para reproducir síntomas de distracción y somnolencia. Dependiendo del tipo de actividad y su duración, se obtuvieron diferentes resultados. De manera general y considerando de forma conjunta todas las actividades se obtiene una tasa media de detección del 93.11%.[EN] According to the most recent studies published by the World Health Organization (WHO) in 2013, it is estimated that 1.25 million people die as a result of traffic crashes. Many of them are caused by what it is known as inattention, whose main contributing factors are both distraction and drowsiness. Overall, it is estimated that inattention causes between 25% and 75% of the crashes and near-crashes. That is why this is a thoroughly studied field by the research community, where solutions to combat distraction and drowsiness, in particular, and inattention, in general, can be classified into three main categories, and, where computer vision has clearly become a non-obtrusive effective tool for the detection of both distraction and drowsiness. The aim of this paper is to propose, build and validate an architecture based on the analysis of visual characteristics by using computer vision techniques and machine learning to detect both distraction and drowsiness in drivers. Firstly, the modules have been tested with all its components independently using several datasets. More specifically, the presence/absence of the driver is detected with an accuracy of 100%, 90.56%, 88.96% by using a marker positioned onto the headrest, the LBP operator and the CS-LBP operator, respectively. Regarding the eye closeness validation with CEW dataset, an accuracy of 93.39% and 91.84% is obtained using a new method using both LBP (LBP_RO) and CS-LBP (CS-LBP_RO). After performing several tests, the camera is positioned on the dashboard, increasing the accuracy of face detection from 86.88% to 96.46%. In connection with the tests performed in real-world settings, 16 drivers were involved performing several activities imitating different sings of sleepiness and distraction. Overall, an accuracy of 93.11%is obtained considering all activities and all drivers.El origen de las actividades del presente trabajo ha sido realizado parcialmente gracias al apoyo tanto de la Fundación para el fomento en Asturias de la investigación científica aplicada y la tecnología (FICYT) y de la empresa SINERCO SL, por medio de la ejecución del proyecto "Creación de algoritmos de visión artificial ", con referencia IE09-511.El presente trabajo se engloba en la tesis doctoral de Alberto Fernández Villán.Fernández Villán, A.; Usamentiaga Fernández, R.; Casado Tejedor, R. (2017). Sistema Automático Para la Detección de Distracción y Somnolencia en Conductores por Medio de Características Visuales Robustas. 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Declaración de Luarca sobre el Derecho Humano a la Paz

Postprint (published version

La pandemia en/desde Jujuy: reflexiones situadas

Acaso no podrí amos afirmar que la pandemia llego a nuestra sociedad mundial causando un pandemo - nium?. En cierto modo, ma s alla del juego de palabras, gran parte de nosotros lo sentimos así . Es claro, una pandemia, es una enfermedad que afecta a la sociedad. Perturba intensamente la cotidianeidad, las ocupaciones, y, en general, lo que en estos dí as an oramos como la “vida normal” de todos. Si contraemos una enfermedad ma s o menos aguda, todas nuestras actividades se ven afectadas, se desordenan. Cuando ello ocurre, pra cticamente debemos concentrarnos, casi con exclusividad, en superar la afeccio n con la ayuda de profesionales de la salud, cualquiera sea el abordaje disciplinario que nos resulte ma s adecuado y confiable. Así , del mismo modo, la pandemia afecta a toda la comunidad, a todas sus actividades. Y, en este caso tambie n la principal preocupacio n es superar la afeccio n. Entonces hay que buscar alternativas para el resto de las tareas, que deben transcurrir entre los estrechos ma rgenes que nos permiten tanto el cuidado personal como el social, ambos imprescindibles.Fil: Aramayo, Anahí. Universidad Nacional de Jujuy; ArgentinaFil: Lopez, Andrea Noelia. Universidad Nacional de Jujuy; ArgentinaFil: Díaz, Rodrigo Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades. Universidad Nacional de Jujuy. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades; ArgentinaFil: Astorga, Farid Diego. Universidad Nacional de Jujuy; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Assad, Lucas Gabriel. Universidad Nacional de Jujuy; ArgentinaFil: Hoyos, Gustavo Daniel. Universidad Nacional de Jujuy; ArgentinaFil: Balut, Jorgelina. No especifíca;Fil: Angulo Villán, Florencia Raquel. No especifíca;Fil: Brailovsky, Sofia Miriam. No especifíca;Fil: Carrizo, María José. No especifíca;Fil: Bustamante, Patricia. No especifíca;Fil: Jaled, Daniela Alejandra. No especifíca;Fil: Castillo, Silvina Ana Lia. No especifíca;Fil: Díaz, Enrique Antonio. No especifíca;Fil: Cieza, Fernanda. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; ArgentinaFil: Cuva, Cecilia Alejandra. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; ArgentinaFil: Rivas, Rosana Verónica. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; ArgentinaFil: Altea, Laura. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; ArgentinaFil: Garzon, Analia Soledad. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mamani, Claudia. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; ArgentinaFil: Villarroel, Viviana Mabel. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; ArgentinaFil: Costas Frison, Celeste. No especifíca;Fil: Montenegro, Erica Maricel. No especifíca;Fil: Guzmán, Vilma Roxana. No especifíca;Fil: Donaire, Claudia. No especifíca;Fil: Herrera, Ana Soledad. No especifíca;Fil: Cardozo, Juana Griselda. No especifíca;Fil: Nieva, Nuria Noelia. No especifíca;Fil: Miranda , Ana Lía. Universidad Nacional de Jujuy; ArgentinaFil: Patagua, Patricia Evangelina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades. Universidad Nacional de Jujuy. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades; ArgentinaFil: Gomez, Carina Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades. Universidad Nacional de Jujuy. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades; ArgentinaFil: Bustamante, Patricia. Universidad Nacional de Jujuy; ArgentinaFil: Navarro Suárez, Camila. Universidad Nacional de Jujuy; ArgentinaFil: Yufra, Laura Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades. Universidad Nacional de Jujuy. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades; ArgentinaFil: Massari, María Justina. Universidad Nacional de Jujuy; ArgentinaFil: Cortez, Carla Melisa. Universidad Nacional de Jujuy; ArgentinaFil: Rovetta, Ana Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Zinger, Sabrina. Universidad Nacional de Jujuy; ArgentinaFil: Alba, Juan Pablo. Universidad Nacional de Jujuy; ArgentinaFil: Arrueta, Patricia Marisel. Universidad Nacional de Jujuy; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Constant, Juan. Universidad Nacional de Jujuy; ArgentinaFil: Gumiel, Silvina. Universidad Nacional de Jujuy; ArgentinaFil: Zazzarini, Susana. Universidad Nacional de Jujuy; ArgentinaFil: Valente, Verónica. Universidad Nacional de Jujuy; ArgentinaFil: Bergesio, Liliana del Carmen. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; ArgentinaFil: González, Natividad María. Universidad Nacional de Jujuy. Facultad de Ciencias Económicas. Instituto de Investigaciones Económicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Nieva, Florencia Antonella. Universidad Nacional de Jujuy; ArgentinaFil: Callieri, Ivanna Gabriela. Universidad Nacional de Jujuy; ArgentinaFil: Montes, Elena Patricia. Universidad Nacional de Jujuy; ArgentinaFil: Civila Orellana, Fabiola Vanesa. Universidad Nacional de Jujuy; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Villarrubia Gómez, Álvaro Patricio. Universidad Nacional de Jujuy; ArgentinaFil: Quispe, Gloria. Universidad Nacional de Jujuy; ArgentinaFil: Cosme, María Cristina. Universidad Nacional de Jujuy; ArgentinaFil: Quispe, Ariadna Vanesa. Universidad Nacional de Jujuy; ArgentinaFil: Galián, Víctor Joel. Universidad Nacional de Jujuy; ArgentinaFil: Vazquez, Omar Eduardo. Universidad Nacional de Jujuy; ArgentinaFil: Cerpa, Daniela Soledad. Universidad Nacional de Jujuy; ArgentinaFil: Martínez, Luis Gustavo. Universidad Nacional de Jujuy; ArgentinaFil: Fernández, Laura Soledad. Universidad Nacional de Jujuy; ArgentinaFil: Tolaba, Gladys Sarai. Universidad Nacional de Jujuy; ArgentinaFil: Escalante, Norberto Oscar. Universidad Nacional de Jujuy; ArgentinaFil: Cazón, Mariela. Universidad Nacional de Jujuy; ArgentinaFil: Ugarte, María Adela. Universidad Nacional de Jujuy; ArgentinaFil: García Vargas, Alejandra. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; ArgentinaFil: Gaona, Melina. Universidad Nacional de Quilmes. Departamento de Ciencias Sociales. Centro de Estudios de Historia, Cultura y Memoria; ArgentinaFil: Zubia, Gonzalo Federico. Universidad Nacional de Quilmes. Departamento de Ciencias Sociales. Centro de Estudios de Historia, Cultura y Memoria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kulemeyer, Jorge Alberto. No especifíca;Fil: Pantoja, Rodrigo. No especifíca;Fil: Paz, María Elisa. No especifíca;Fil: Rivero, Ariel Rodolfo. No especifíca;Fil: Rocabado, Cecilia Inés. Universidad Nacional de Jujuy; ArgentinaFil: Villagra, Gabriela Soledad. Instituto de Ciencia y Tecnología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rodríguez, Ana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Ciencia y Tecnología Regional; ArgentinaFil: Adi Barrionuevo, Ana Carolina. Universidad Nacional de Jujuy; ArgentinaFil: Adi Barrionuevo, Irene. Universidad Nacional de Jujuy; ArgentinaFil: Aramayo, Natalia. Universidad Nacional de Jujuy; ArgentinaFil: Fernández, Gabriel. Universidad Nacional de Jujuy; ArgentinaFil: Morales, Miriam Mariana. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Rios, Natalia Fatima. Facultad Latinoamericana de Ciencias Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rocabado, Zaida Nadia. Universidad Nacional de Jujuy; ArgentinaFil: Sandoval, Cecilia. No especifíca;Fil: Soto, Mercedes. No especifíca;Fil: Osores, Noelia Andrea del Valle. No especifíca;Fil: Revollo, Jimena ;Citterio. No especifíca;Fil: Gutiérrez, Ivone Belén. Universidad Nacional de Jujuy; ArgentinaFil: Juste, Stella Maris. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades. Universidad Nacional de Jujuy. Unidad Ejecutora en Ciencias Sociales Regionales y Humanidades; ArgentinaFil: Vidal, José Fernando. Universidad Nacional de Jujuy; ArgentinaFil: Karasik, Gabriela Alejandra. Universidad Nacional de Jujuy. Facultad de Humanidades y Ciencias Sociales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta; ArgentinaFil: Bruce, Beatriz Maria. Universidad Nacional de Jujuy; Argentin