Towards a contextual model for data quality in precision agriculture

Precision agriculture is a farming management concept, based on the crop variability in the field; it comprises several stages: data collection, information processing and decision-making. After an extensive review of the literature, it appears that data quality control is an important process in precision agriculture and can be considered in the data collection process. This paper makes an approach to data architecture quality control by applying the contextual information of the acquisition system (sad) and environment context information. This approach can provide the sad the capability to understand the situations of their environment in order to improve the quality of data for decision-making.La agricultura de precisión es un concepto agronómico de gestión de parcelas agrícolas, basado en la existencia de variabilidad en campo; comprende varias etapas: recolección de datos, procesamiento de información y toma de decisiones. Después de una extensa revisión de la literatura, se observa que el control de calidad de los datos es un proceso muy importante para agricultura de precisión que puede ser considerado en la recolección de datos. En este artículo se da una aproximación a una arquitectura de control de calidad de datos utilizando la información de contexto del sistema de adquisición (SAD) y el medio ambiente. Este enfoque puede proporcionar a los SAD la capacidad de comprender las situaciones de su entorno con el fin de mejorar la calidad de datos para la toma de decisiones

Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)

Recommender systems have been based on context and content, and now the technological challenge of making personalized recommendations based on the user emotional state arises through physiological signals that are obtained from devices or sensors. This paper applies the deep learning approach using a deep convolutional neural network on a dataset of physiological signals (electrocardiogram and galvanic skin response), in this case, the AMIGOS dataset. The detection of emotions is done by correlating these physiological signals with the data of arousal and valence of this dataset, to classify the affective state of a person. In addition, an application for emotion recognition based on classic machine learning algorithms is proposed to extract the features of physiological signals in the domain of time, frequency, and non-linear. This application uses a convolutional neural network for the automatic feature extraction of the physiological signals, and through fully connected network layers, the emotion prediction is made. The experimental results on the AMIGOS dataset show that the method proposed in this paper achieves a better precision of the classification of the emotional states, in comparison with the originally obtained by the authors of this dataset.This research project is financed by theGovernment of Colombia, Colciencias and the Governorateof Boyac

Modelo conceptual para el despliegue de publicidad ubicua soportado en un esquema de cooperación Smart TV- SmartPhone.

Advertising has been one of the most valuable marketing tools for years by means of a massive, wide-ranging and vertical approach between customers and advertisers. However, a new tendency known as pervasive advertising suggests an evolution of the classical concept towards a more interactive, customized, and horizontal environment that seeks to improve the impact and efficiency of conventional advertising. As a result of the support of emerging technologies related to the development of smartphones and smart TVs, there are no doubts about pervasive advertising potential and its value as a rich research field. This article introduces a conceptual model, which compiles the most relevant research areas related to pervasive computing applied to advertising supported on a smart TV – smartphone cooperation framework.La publicidad ha sido durante años una de las herramientas más valiosas del mercadeo a través de un enfoque principalmente masivo, generalizado y vertical entre clientes y anunciantes. No obstante, una nueva corriente conocida como publicidad ubicua marca una evolución en el concepto clásico hacia entornos más interactivos, personalizados y horizontales que busca mejorar la eficiencia y el impacto de la publicidad convencional. Gracias al apoyo de tecnologías emergentes que se sustentan en la evolución de los smartphones y los smart TV, el potencial de la publicidad ubicua es indudable, lo cual la ha convertido en un terreno fértil de investigación. El presente artículo presenta un modelo conceptual que condensa las áreas de investigación más relevantes relacionadas con el despliegue de publicidad en entornos de computación ubicua soportados en esquemas de cooperación smart TV – smartphone

Tourist experiences recommender system based on emotion recognition with wearable data

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.This research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja. We thank the members of the GICAC group (Research Group in Administrative and Accounting Sciences) of the Universidad Santo Tomás Seccional Tunja for their participation in the experimental phase of this investigation

Algorithm for the comparison of human periodic movements using wearable devices

In the context of teaching-learning of motor skills in a virtual environment, videos are generally used. The person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely thought of. This article proposes an algorithm in which two periodic movements are compared, the one carried out by an expert and the one carried out by the person who is learning, in order to determine how closely these two movements are performed and to provide feedback from them. The algorithm starts from the capture of data through a wearable device that yields data from an accelerometer; in this case, the data of the expert and the data of the person who is learning are captured in a dataset of salsa dance steps. Adjustments are made to the data in terms of Pearson iterations, synchronization, filtering, and normalization, and DTW, linear regression, and error analysis are used to make the corresponding comparison of the two datasets. With the above, it is possible to determine if the cycles of the two signals coincide and how closely the learner’s movements resemble those of the expert

Solução ubíqua baseada em NFC para a análise de dados turísticos em cidades inteligentes.

El registro y el análisis detallado de las trayectorias del visitante y los movimientos individuales en tiempo real de las decenas de miles de visitantes es una de las áreas más importantes de la investigación en turismo. Para observar los movimientos turísticos, está disponible una variedad de técnicas. Nuevas técnicas de seguimiento se están explorando y gracias al avance de la tecnología es posible disponer en cualquier momento y desde cualquier lugar (computación ubicua) de la de información que se ha utilizado para registrar el movimiento de turistas, con alta resolución. En estos entornos (ambientes etiquetados) donde el usuario interactúa con su medio ambiente, una tecnología emergente conocida como Near Field Communication [NFC] ofrece una manera natural para la interacción entre los usuarios y su entorno. Este artículo elabora una propuesta ubicua, basada en NFC, que permite obtener datos turísticos en tiempo real que son analizados con el método de cadenas de Markov por medio de pruebas experimentales y estadísticas, gracias a que se demuestra que el movimiento de un turista está influenciado por el estado o sitio turístico donde se encuentre antes de pasar a otro, corroborando la hipótesis que indica que es posible capturar información dejada por los turistas por medio de herramientas tecnológicas, y que gracias al procesamiento de esa información se puede obtener una traza que muestre la actividad realizada, la misma que, por medio de su visualización permitirá la toma de decisiones que favorezcan la actividad turística como parte de la economía regional y nacional. Detailed recording and analysis of visitor paths and individual mouse movements in real time of tens-of-thousands of visitors is one of the most important areas of tourism research, and to observe tourist movements a variety of techniques are available. New tracking techniques are explored and due to the advance of technology we can have information at any time and from anywhere (pervasive computing). This has been used to record movement information of tourists with high resolution. In these environments (tags environments) where the user interacts with the environment, an emerging technology called NFC (Near Field Communication) is providing a natural means of interaction between the users and their environment. This paper shows the implementation of an NFC-based pervasive solution that allows tourist tracking data to be obtained in real time; it is simplified and analyzed with the Markov chains method by experimental and statistical testing. It is also demonstrated that the movement of a tourist is influenced by the state or tourist site where he or she is to move to another, corroborating the hypothesis "that if you can capture the information left by tourists through technological tools, thanks to the processing of such information you can obtain a trace that is a sample of the activity which, through its display, allows decisions that promote tourism as part of the regional and national economy".O registro e a análise pormenorizados dos percursos do visitante bem como os movimentos individuais em tempo real das dezenas de milhares de visitantes pertencem a uma das mais importantes áreas de pesquisa em turismo. Para observar os movimentos turísticos, encontra-se disponível uma variedade de técnicas. Novas técnicas de monitorização estão sendo exploradas e graças aos avanços da tecnologia é possível ter em qualquer momento e desde qualquer lugar (computação ubíqua) a informação que foi usada para registrar o movimento de turistas, com alta resolução. Nesses ambientes (ambientes etiquetados) onde o usuário interage com o seu ambiente, uma tecnologia emergente conhecida como Near Field Communication [NFC] fornece uma maneira natural para a interação entre os usuários e seu ambiente. Este artigo desenvolve uma proposta ubíqua, baseada em NFC, que permite obter dados turísticos em tempo real que são analisados com o método de cadeias de Markov através de testes experimentais e estatísticas, graças a que se demonstra que o movimento de um turista é influenciado pelo estado ou local turístico onde se encontra antes de passar para outro, confirmando a hipótese que é possível capturar informação deixada pelos turistas através de ferramentas tecnológicas, e que graças ao processamento dessa informação pode obter-se um traço que mostre a atividade realizada, o mesmo que, através da sua visualização permitirá tomar decisões que promovam o turismo como parte da economia regional e nacional.

Forest Health in the Southern Cone of America: State of the Art and Perspectives on Regional Efforts

The plantation and natural forests of South America have been highly impacted by native and exotic pests in recent decades. The interaction of emerging invasive pests, climate change, and timber markets will define the region’s forests, with significant but uncertain ecological changes and economic losses expected. The Southern Cone Forest Health Group (SCFHG), a joint ad hoc initiative run by forest health professionals from Argentina, Brazil, Chile, and Uruguay, aims to strengthen relationships between the forestry industry, stakeholders, academia, and government agencies across the region. Here, we highlight regional strengths, weaknesses, threats, and opportunities to address forest health issues in the region. A regional approach with a strong communication network is relevant for future actions. In the current global scenario of invasive species and climate change, the implementation of practices that incorporate the resilience of forest ecosystems and sustainable management needs to be prioritized in forest policy across the region. Understanding that pests and pathogens do not recognize borders, we call on governments and organizations to support joint actions with agreements and adequate resources to enhance our regional capabilities.Estación Experimental Agropecuaria BarilocheFil: Villacide, Jose Maria. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estacion Experimental Agropecuaria Bariloche. Area de Recursos Forestales. Grupo de Ecologia de Poblaciones de Insectos; ArgentinaFil: Villacide, Jose Maria. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Gomez, Demian F. Texas A&M Forest Service; Estados UnidosFil: Perez, Carlos Alberto. Universidad de la República Paysandú. Facultad de Agronomia; UruguayFil: Corley, Juan Carlos. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estacion Experimental Agropecuaria Bariloche. Area de Recursos Forestales. Grupo de Ecologia de Poblaciones de Insectos; ArgentinaFil: Corley, Juan Carlos. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Corley, Juan Carlos. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Departamento de Ecologia; ArgentinaFil: Ahumada, Rodrigo. Bioforest S.A. División de Silvicultura y Sanidad; ChileFil: Rodrigues Barbosa, Leonardo. Embrapa Florestas. Empresa Brasileira de Pesquisa Agropecuária; BrasilFil: Furtado, Edson Luiz. Universidade Estadual Paulista. Faculdade de Ciências Agronômicas Botucatu. Departamento de Proteção Vegetal; BrasilFil: Gonzalez, Andres. Universidad de la Republica. Facultad de Quimica; UruguayFil: Ramirez, Nazaret. Área Productividad de las Plantaciones. I&D.Montes del Plata; UruguayFil: Balmelli, Gustavo. Instituto Nacional de Investigacion Agropecuaria. Sistema Forestal; UruguayFil: Dias de Souza, Caroline. Instituto de Pesquisas e Estudos Florestais. Programa Cooperativo Sobre Proteção Florestal; BrasilFil: Martinez, Gonzalo. Instituto Nacional de Investigacion Agropecuaria. Sistema Forestal; Urugua

A Medical Records Managing and Securing Blockchain Based System Supported by a Genetic Algorithm and Discrete Wavelet Transform

The privacy of patients is jeopardised when medical records and data are spread or shared beyond the protected cloud of institutions. This is because breaches force them to the brink that they start abstaining from full disclosure of their condition. This type of condition has a negative effect on scientific research, patients and all stakeholders. A blockchain-based data sharing system is proposed to tackle this issue, which employs immutability and autonomy properties of the blockchain to sufficiently resolve challenges associated with access control and handle sensitive data. Our proposed system is supported by a Discrete Wavelet Transform to enhance the overall security, and a Genetic Algorithm technique to optimise the queuing optimization technique as well. Introducing this cryptographic key generator enhances the immunity and system access control, which allows verifying users securely in a fast way. This design allows further accountability since all users involved are already known and the blockchain records a log of their actions. Only when the users' cryptographic keys and identities are confirmed, the system allows requesting data from the shared queuing requests. The achieved execution time per node, confirmation time per node and robust index for block number of 0.19 s, 0.17 s and 20 respectively that based on system evaluation illustrates that our system is robust, efficient, immune and scalable

Focal and non-focal epilepsy localization: a review

The focal and non-focal epilepsy is seen to be a chronic neurological brain disorder, which has affected ≈ 60 million people in the world. Hence, an early detection of the focal epileptic seizures can be carried out using the EEG signals, which act as a helpful tool for early diagnosis of epilepsy. Several EEG-based approaches have been proposed and developed to understand the underlying characteristics of the epileptic seizures. Despite the fact that the early results were positive, the proposed techniques cannot generate reproducible results and lack a statistical validation, which has led to doubts regarding the presence of the pre-ictal state. Various methodical and algorithmic studies have indicated that the transition to an ictal state is not a random process, and the build-up can lead to epileptic seizures. This study reviews many recently-proposed algorithms for detecting the focal epileptic seizures. Generally, the techniques developed for detecting the epileptic seizures were based on tensors, entropy, empirical mode decomposition, wavelet transform and dynamic analysis. The existing algorithms were compared and the need for implementing a practical and reliable new algorithm is highlighted. The research regarding the epileptic seizure detection research is more focused on the development of precise and non-invasive techniques for rapid and reliable diagnosis. Finally, the researchers noted that all the methods that were developed for epileptic seizure detection lacks standardization, which hinders the homogeneous comparison of the detector performance