Canopy fruit location can affect olive oil quality in Arbequina hedgerow orchards

The effect of location of fruit in canopies of hedgerow olive trees (Olea europaea L., cv. ‘Arbequina’) on quality of virgin oil was tested by analyzing oils extracted from different height layers and faces of 9 olive hedgerows (6 North-South oriented and 3 East-West). Although sensory attributes were not different other oil quality parameters may be significantly modified by fruit position. In some hedgerows, oils extracted from fruits harvested from higher layers exhibited significantly higher stability against oxidation, along with higher palmitic acid, linoleic acid and phenol contents, but lower oleic acid content. Oils extracted from fruits harvested from East and North facing hedgerows oriented North-South and East-West, respectively, exhibited higher oleic contents and lower saturated and polyunsaturated fatty acid contents. The mean phenol content of oils extracted from fruits from a North-South oriented hedgerow was significantly greater from one of the East-West oriented hedgerows. These findings may be relevant for the design of future olive hedgerows destined for olive oil production.

A Satisfaction-based Model for Affect Recognition from Conversational Features in Spoken Dialog Systems

Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator?s data, suggesting that user bias does exist in a laboratory-led evaluation.

User-centric need-driven affect modeling for spoken conversational agents: design and evaluation

It is easy to get frustrated at spoken conversational agents (SCAs), perhaps because they seem to be callous. By and large, the quality of human-computer interaction is affected due to the inability of the SCAs to recognise and adapt to user emotional state. Now with the mass appeal of artificially-mediated communication, there has been an increasing need for SCAs to be socially and emotionally intelligent, that is, to infer and adapt to their human interlocutors’ emotions on the fly, in order to ascertain an affective, empathetic and naturalistic interaction. An enhanced quality of interaction would reduce users’ frustrations and consequently increase their satisfactions. These reasons have motivated the development of SCAs towards including socio-emotional elements, turning them into affective and socially-sensitive interfaces. One barrier to the creation of such interfaces has been the lack of methods for modelling emotions in a task-independent environment. Most emotion models for spoken dialog systems are task-dependent and thus cannot be used “as-is” in different applications. This Thesis focuses on improving this, in which it concerns computational modeling of emotion, personality and their interrelationship for task-independent autonomous SCAs. The generation of emotion is driven by needs, inspired by human’s motivational systems. The work in this Thesis is organised in three stages, each one with its own contribution. The first stage involved defining, integrating and quantifying the psychological-based motivational and emotional models sourced from. Later these were transformed into a computational model by implementing them into software entities. The computational model was then incorporated and put to test with an existing SCA host, a HiFi-control agent. The second stage concerned automatic prediction of affect, which has been the main challenge towards the greater aim of infusing social intelligence into the HiFi agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. In this stage, we attempted to address part of this challenge by considering the roles of user satisfaction ratings and conversational/dialog features as the respective target and predictors in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. The final stage concerned the evaluation of the emotional model through the HiFi agent. A series of user studies with 70 subjects were conducted in a real-time environment, each in a different phase and with its own conditions. All the studies involved the comparisons between the baseline non-modified and the modified agent. The findings have gone some way towards enhancing our understanding of the utility of emotion in spoken dialog systems in several ways; first, an SCA should not express its emotions blindly, albeit positive. Rather, it should adapt its emotions to user states. Second, low performance in an SCA may be compensated by the exploitation of emotion. Third, the expression of emotion through the exploitation of prosody could better improve users’ perceptions of an SCA compared to exploiting emotions through just lexical contents. Taken together, these findings not only support the success of the emotional model, but also provide substantial evidences with respect to the benefits of adding emotion in an SCA, especially in mitigating users’ frustrations and ultimately improving their satisfactions. Resumen Es relativamente fácil experimentar cierta frustración al interaccionar con agentes conversacionales (Spoken Conversational Agents, SCA), a menudo porque parecen ser un poco insensibles. En general, la calidad de la interacción persona-agente se ve en cierto modo afectada por la incapacidad de los SCAs para identificar y adaptarse al estado emocional de sus usuarios. Actualmente, y debido al creciente atractivo e interés de dichos agentes, surge la necesidad de hacer de los SCAs unos seres cada vez más sociales y emocionalmente inteligentes, es decir, con capacidad para inferir y adaptarse a las emociones de sus interlocutores humanos sobre la marcha, de modo que la interacción resulte más afectiva, empática y, en definitiva, natural. Una interacción mejorada en este sentido permitiría reducir la posible frustración de los usuarios y, en consecuencia, mejorar el nivel de satisfacción alcanzado por los mismos. Estos argumentos justifican y motivan el desarrollo de nuevos SCAs con capacidades socio-emocionales, dotados de interfaces afectivas y socialmente sensibles. Una de las barreras para la creación de tales interfaces ha sido la falta de métodos de modelado de emociones en entornos independientes de tarea. La mayoría de los modelos emocionales empleados por los sistemas de diálogo hablado actuales son dependientes de tarea y, por tanto, no pueden utilizarse "tal cual" en diferentes dominios o aplicaciones. Esta tesis se centra precisamente en la mejora de este aspecto, la definición de modelos computacionales de las emociones, la personalidad y su interrelación para SCAs autónomos e independientes de tarea. Inspirada en los sistemas motivacionales humanos en el ámbito de la psicología, la tesis propone un modelo de generación/producción de la emoción basado en necesidades. El trabajo realizado en la presente tesis está organizado en tres etapas diferenciadas, cada una con su propia contribución. La primera etapa incluyó la definición, integración y cuantificación de los modelos motivacionales de partida y de los modelos emocionales derivados a partir de éstos. Posteriormente, dichos modelos emocionales fueron plasmados en un modelo computacional mediante su implementación software. Este modelo computacional fue incorporado y probado en un SCA anfitrión ya existente, un agente con capacidad para controlar un equipo HiFi, de alta fidelidad. La segunda etapa se orientó hacia el reconocimiento automático de la emoción, aspecto que ha constituido el principal desafío en relación al objetivo mayor de infundir inteligencia social en el agente HiFi. En los últimos años, los estudios sobre reconocimiento de emociones a partir de la voz han pasado de emplear datos actuados a usar datos reales en los que la presencia u observación de emociones se produce de una manera mucho más sutil. El reconocimiento de emociones bajo estas condiciones resulta mucho más complicado y esta dificultad se pone de manifiesto en tareas tales como el etiquetado y el aprendizaje automático. En esta etapa, se abordó el problema del reconocimiento de las emociones del usuario a partir de características o métricas derivadas del propio diálogo usuario-agente. Gracias a dichas métricas, empleadas como predictores o indicadores del grado o nivel de satisfacción alcanzado por el usuario, fue posible discriminar entre satisfacción y frustración, las dos emociones prevalentes durante la interacción usuario-agente. La etapa final corresponde fundamentalmente a la evaluación del modelo emocional por medio del agente Hifi. Con ese propósito se llevó a cabo una serie de estudios con usuarios reales, 70 sujetos, interaccionando con diferentes versiones del agente Hifi en tiempo real, cada uno en una fase diferente y con sus propias características o capacidades emocionales. En particular, todos los estudios realizados han profundizado en la comparación entre una versión de referencia del agente no dotada de ningún comportamiento o característica emocional, y una versión del agente modificada convenientemente con el modelo emocional propuesto. Los resultados obtenidos nos han permitido comprender y valorar mejor la utilidad de las emociones en los sistemas de diálogo hablado. Dicha utilidad depende de varios aspectos. En primer lugar, un SCA no debe expresar sus emociones a ciegas o arbitrariamente, incluso aunque éstas sean positivas. Más bien, debe adaptar sus emociones a los diferentes estados de los usuarios. En segundo lugar, un funcionamiento relativamente pobre por parte de un SCA podría compensarse, en cierto modo, dotando al SCA de comportamiento y capacidades emocionales. En tercer lugar, aprovechar la prosodia como vehículo para expresar las emociones, de manera complementaria al empleo de mensajes con un contenido emocional específico tanto desde el punto de vista léxico como semántico, ayuda a mejorar la percepción por parte de los usuarios de un SCA. Tomados en conjunto, los resultados alcanzados no sólo confirman el éxito del modelo emocional, sino xv que constituyen además una evidencia decisiva con respecto a los beneficios de incorporar emociones en un SCA, especialmente en cuanto a reducir el nivel de frustración de los usuarios y, en última instancia, mejorar su satisfacción.

Assessing user bias in affect detection within context-based spoken dialog systems

This paper presents an empirical evidence of user bias within a laboratory-oriented evaluation of a Spoken Dialog System. Specifically, we addressed user bias in their satisfaction judgements. We question the reliability of this data for modeling user emotion, focusing on contentment and frustration in a spoken dialog system. This bias is detected through machine learning experiments that were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. The target used was the satisfaction rating and the predictors were conversational/dialog features. Our results indicated that standard classifiers were significantly more successful in discriminating frustration and contentment and the intensities of these emotions (reflected by user satisfaction ratings) from annotator data than from user data. Indirectly, the results showed that conversational features are reliable predictors of the two abovementioned emotions.

I feel you: Towards affect-sensitive domotic Spoken Conversational Agents

We describe the work on infusion of emotion into limitedtask autonomous spoken conversational agents (SCAs) situated in the domestic environment, using a Need-inspired task-independentEmotion model (NEMO). In order to demonstrate the generation of a?ect through the use of the model, we describe the work of integrating it with a naturallanguage mixed-initiative HiFi-control SCA. NEMO and the host system communicates externally, removing the need for the Dialog Manager to be modi?ed as done in most existing dialog systems, in order to be adaptive. We also summarize the work on automatic a?ect prediction, namely frustration and contentment from dialog features, a non-conventional source, in the attempt of moving towards a more user-centric approach.

Canopy fruit location can affect olive oil quality in Arbequina hedgerow orchards.

En este trabajo se recogieron muestras de aceituna procedente de distintas alturas de setos cultivados en diferentes condiciones para evaluar el efecto en la calidad del aceite.

Canopy fruit location can affect olive oil quality in Arbequina hedgerow orchards.

The effect of location of fruit in canopies of hedgerow olive trees (Olea europaea L., cv. ‘Arbequina’) on quality of virgin oil was tested by analyzing oils extracted from different height layers and faces of nine olive hedgerows (6 North–South oriented and 3 East– West). Although sensory attributes were not different, other oil quality parameters may be significantly modified by fruit position. Oils extracted from fruits harvested from higher layers exhibited significantly higher stability against oxidation, along with higher palmitic acid, linoleic acid and phenol contents, but lower oleic acid content. Oils extracted from fruits harvested from East and North facing hedgerows oriented North–South and East–West, respectively, exhibited higher oleic contents and lower saturated and polyunsaturated fatty acid contents. The mean phenol content of oils extracted from fruits from a North–South oriented hedgerow was significantly greater from one of the East–West oriented hedgerows. These findings may be relevant for the design of future olive hedgerows destined for olive oil production.

I feel you: the design and evaluation of a domotic affect-sensitive spoken conversational agent

We describe the work on infusion of emotion into a limited-task autonomous spoken conversational agent situated in the domestic environment, using a need-inspired task-independent emotion model (NEMO). In order to demonstrate the generation of affect through the use of the model, we describe the work of integrating it with a natural-language mixed-initiative HiFi-control spoken conversational agent (SCA). NEMO and the host system communicate externally, removing the need for the Dialog Manager to be modified, as is done in most existing dialog systems, in order to be adaptive. The first part of the paper concerns the integration between NEMO and the host agent. The second part summarizes the work on automatic affect prediction, namely, frustration and contentment, from dialog features, a non-conventional source, in the attempt of moving towards a more user-centric approach. The final part reports the evaluation results obtained from a user study, in which both versions of the agent (non-adaptive and emotionally-adaptive) were compared. The results provide substantial evidences with respect to the benefits of adding emotion in a spoken conversational agent, especially in mitigating users' frustrations and, ultimately, improving their satisfaction.

Does the natural "microcosm" created by Tuber aestivum affect soil microarthropods? A new hypothesis based on Collembola in truffle culture

microarthropods play an important role in fungi dispersion, but little is still known about the interaction between truffle and soil microarthropods. The aim of this study was to investigate the ability of the truffle Tuber aestivum to modify soil biogeochemistry (i.e. create a zone of scarce vegetation around the host plant, called a burn or brûlé) and to highlight the effects of the brûlé on the soil fauna community. We compared soil microarthropod communities found in the soil inside versus outside the T. aestivum brûlé with the chemistry of soil collected inside versus outside the brûlé. The study was carried out in three Mediterranean areas, two in Italy and one in Spain. The results confirmed the ability of T. aestivum to modify soil biogeochemistry in the brûlé: pH was higher and total organic carbon tended to be lower inside the brûlé compared to outside. Soil fauna communities showed some interesting differences. Some groups, such as Symphyla and Pauropoda, adapted well to the soil; some Collembolan families, and biodiversity and soil quality indices were generally higher outside the brûlé. Folsomia sp. showed higher abundance in the soil of the brûlé compared to outside. The results suggest that some Collembola groups may be attracted by the fungal metabolites produced by T. aestivum, while other Collembola and other microarthropods may find an unfavourable environment in the soil of the brûlé. The next steps will be to confirm this hypothesis and to extend the study to other keys groups such as nematodes and earthworms and to link fluctuations of soil communities with the biological phases of truffle growth.

Feed form and energy concentration of the diet affect growth performance and digestive tract traits of brown-egg laying pullets from hatching to 17 weeks of age

The influence of feed form and energy concentration of the diet on growth performance and the development of the gastrointestinal tract (GIT) was studied in brown-egg laying pullets. Diets formed a 2 x 5 factorial with 2 feed forms (mash vs. crumbles) and 5 levels of energy differing in 50 kcal AMEn/kg. For the entire study (0 to 17 wk of age) feeding crumbles increased ADFI (52.9 vs. 49.7 g; P < 0.001) and ADG (12.7 vs. 11.6 g; P < 0.001) and improved feed conversion ratio (FCR; 4.18 vs. 4.27; P < 0.001). An increase in the energy content of the diet decreased ADFI linearly (P < 0.001) and improved FCR quadratically (P < 0.01) but energy intake (kcal AMEn/d) was not affected. BW uniformity was higher (P < 0.05) in pullets fed crumbles than in those fed mash but was not affected (P > 0.05) by energy content of the diet. At 5, 10, and 17 wk of age, the relative weight (RW, % BW) of the GIT and the gizzard, and gizzard digesta content were lower (P < 0.05 to P < 0.001) and gizzard pH was higher (P < 0.05 to P < 0.001) in pullets fed crumbles than in pullets fed mash. Energy concentration of the diet did not affect any of the GIT variables studied. In summary, feeding crumbles improved pullet performance and reduced the RW of the GIT and gizzard, and increased gizzard pH at all ages. An increase in the energy content of the diet improved FCR from 0 to 17 wk of age. The use of crumbles and the increase in the AMEn content of the diet might be used adventageously when the objetive is to increase the BW of the pullets. However, crumbles affected the development and weight of the organs of the GIT, which might have negative effects on feed intake and egg production at the beginning of the egg laying cycle.

How future trends may affect international cooperation in planning education : the case of Spain and Latin America

Despite the benefits for exc hanging experiences among planners at the global scale, the strong context dependency of urban planning creates in many instances significant difficulties to extrapolate experiences from one geographical context to the other. If progress is to be achieved in international cooperation programmes, differences and commonalities should be assessed before la unching any academic initiative. In that respect, this p aper makes a brief foresight exercise on how future trends and challenges, which may affect the urban pl anning field, should be taken into consideration according to two different contexts: Spain and Latin America. A segmentation matrix is used to expose a nd discuss the different effects of future trends on both contexts. Some tentative conclusions are drawn for the development of international educational programmes