A Systematic Mapping Study on Approaches for AI-Supported Security Risk Assessment

Effective assessment of cyber risks in the increasingly dynamic threat landscape must be supported by artificial intelligence techniques due to their ability to dynamically scale and adapt. This article provides the state of the art of AI-supported security risk assessment approaches in terms of a systematic mapping study. The overall goal is to obtain an overview of security risk assessment approaches that use AI techniques to identify, estimate, and/or evaluate cyber risks. We carried out the systematic mapping study following standard processes and identified in total 33 relevant primary studies that we included in our mapping study. The results of our study show that on average, the number of papers about AI-supported security risk assessment has been increasing since 2010 with the growth rate of 133% between 2010 and 2020. The risk assessment approaches reported have mainly been used to assess cyber risks related to intrusion detection, malware detection, and industrial systems. The approaches focus mostly on identifying and/or estimating security risks, and primarily make use of Bayesian networks and neural networks as supporting AI methods/techniques.acceptedVersio

HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features

In this paper, we present HTAD: A Home Tasks Activities Dataset. The dataset contains wrist-accelerometer and audio data from people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such as elderly care and mental health monitoring. Preliminary results show that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this dataset public, researchers can test different machine learning algorithms for activity recognition, especially, sensor data fusion methodsacceptedVersio

Astrocitoma subependimario de células gigantes asociado a complejo de esclerosis tuberosa: recomendaciones para el diagnóstico oportuno y tratamiento

El complejo de esclerosis tuberosa es una enfermedad genética poco frecuente, autosómica dominante con fenotipo y expresión clínica muy variables. Se caracteriza por alteraciones en la migración, diferenciación y proliferación celulares con formación de múltiples tumores benignos llamados hamartomas, las cuales afectan principalmente piel, encéfalo, riñón, ojo, corazón y pulmón. Los astrocitomas subependimarios de células gigantes son tumores benignos de crecimiento lento y son los más frecuentes en el sistema nervioso central de los pacientes con complejo de esclerosis tuberosa. Actualmente existen medicamentos indicados en pacientes con astrocitomas subependimarios de células gigantes asociados con complejo de esclerosis tuberosa y son una alternativa al tratamiento quirúrgico. Su mecanismo consiste en la inhibición el complejo 1 mTOR, acción que modula el defecto molecular que ocasiona al complejo de esclerosis tuberosa. Con su uso se han reportado disminución y estabilización de angiomiolipomas renales, linfangioleiomiomatosis, angiofibromas y de astrocitomas subependimarios de células gigantes asociados con complejo de esclerosis tuberos

A Systematic Mapping Study on Approaches for AI-Supported Security Risk Assessment

Effective assessment of cyber risks in the increasingly dynamic threat landscape must be supported by artificial intelligence techniques due to their ability to dynamically scale and adapt. This article provides the state of the art of AI-supported security risk assessment approaches in terms of a systematic mapping study. The overall goal is to obtain an overview of security risk assessment approaches that use AI techniques to identify, estimate, and/or evaluate cyber risks. We carried out the systematic mapping study following standard processes and identified in total 33 relevant primary studies that we included in our mapping study. The results of our study show that on average, the number of papers about AI-supported security risk assessment has been increasing since 2010 with the growth rate of 133% between 2010 and 2020. The risk assessment approaches reported have mainly been used to assess cyber risks related to intrusion detection, malware detection, and industrial systems. The approaches focus mostly on identifying and/or estimating security risks, and primarily make use of Bayesian networks and neural networks as supporting AI methods/techniques

Towards the Automation of a Chemical Sulphonation Process with Machine Learning

Nowadays, the continuous improvement and automation of industrial processes has become a key factor in many fields, and in the chemical industry, it is no exception. This translates into a more efficient use of resources, reduced production time, output of higher quality and reduced waste. Given the complexity of today's industrial processes, it becomes infeasible to monitor and optimize them without the use of information technologies and analytics. In recent years, machine learning methods have been used to automate processes and provide decision support. All of this, based on analyzing large amounts of data generated in a continuous manner. In this paper, we present the results of applying machine learning methods during a chemical sulphonation process with the objective of automating the product quality analysis which currently is performed manually. We used data from process parameters to train different models including Random Forest, Neural Network and linear regression in order to predict product quality values. Our experiments showed that it is possible to predict those product quality values with good accuracy, thus, having the potential to reduce time. Specifically, the best results were obtained with Random Forest with a mean absolute error of 0.089 and a correlation of 0.978.ISBN för värdpublikation: 978-1-7281-3787-2Productive4.

A Feature Importance Analysis for Soft-Sensing-Based Predictions in a Chemical Sulphonation Process

In this paper we present the results of a feature importance analysis of a chemical sulphonation process. The task consists of predicting the neutralization number (NT), which is a metric that characterizes the product quality of active detergents. The prediction is based on a dataset of environmental measurements, sampled from an industrial chemical process. We used a soft-sensing approach, that is, predicting a variable of interest based on other process variables, instead of directly sensing the variable of interest. Reasons for doing so range from expensive sensory hardware to harsh environments, e.g., inside a chemical reactor. The aim of this study was to explore and detect which variables are the most relevant for predicting product quality, and to what degree of precision. We trained regression models based on linear regression, regression tree and random forest. A random forest model was used to rank the predictor variables by importance. Then, we trained the models in a forward-selection style by adding one feature at a time, starting with the most important one. Our results show that it is sufficient to use the top 3 important variables, out of the 8 variables, to achieve satisfactory prediction results. On the other hand, Random Forest obtained the best result when trained with all variables.ISBN för värdpublikation: 978-1-7281-6389-5Productive4.

HTAD: A Home-Tasks Activities Dataset with Wrist-Accelerometer and Audio Features

In this paper, we present HTAD: A Home Tasks Activities Dataset. The dataset contains wrist-accelerometer and audio data from people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such as elderly care and mental health monitoring. Preliminary results show that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this dataset public, researchers can test different machine learning algorithms for activity recognition, especially, sensor data fusion method

Complejidad e inclusividad del comportamiento intrasituacional: Análisis empírico

Ribes and López's (1985) taxonomy proposed that psychological behavior is progressively complex and inclusive. In that respect, there is little research and data are not robust. A study was conducted with the purpose to increase data related to the complexity and inclusivity of the three less complex behaviors of the taxonomy, with three training sequences, namely: 1) ascending (contextual-supplementary-selector), 2) descending-ascending (supplementary -contextual-selector) and 3) descending (selector-supplementary-contextual). The objective was to evaluate the effect of the interaction history (related to training sequences) on differential (contextual), effective (supplementary) and precise (selector) behavior adjustment process.Results showed that a greater number of training sessions were required to improve the performance in functional organizations with greater complexity when participants lacked an interaction history related to programmed contingencies. But, when the interaction history participated in the functional organization, as a previous interaction with the contingencies, a facilitating effect was found in the behavioral adjustment, regardless of whether the transition was ascending or descending. It is discussed whether the increase in the number of sessions is related to the complexity of each level of functional organization. Regarding the functional training transitions, ascending and descending, its effects on facilitation in learning are discussed in relation to the assumption of functional inclusivity.En la taxonomía de Ribes y López (1985) se propone que el comportamiento psicológico es progresivamente complejo e inclusivo; sin embargo, en la literatura sobre el tema se encuentran pocas investigaciones y los datos no son robustos. Teniendo esto en cuenta, y con el propósito de aumentar la evidencia de la complejidad e inclusividad de los tres primeros niveles de complejidad conductual de la taxonomía en tres secuencias de entrenamiento ascendente (contextual-suplementario-selector), descendente-ascendente (suplementario-contextual-selector) y descendente (selectorsuplementario-contextual), el presente estudio tuvo como objetivo evaluar el efecto de la historia de interacción por medio de secuencias de entrenamiento- sobre el proceso de ajuste diferencial (contextual), efectivo (suplementario) y preciso (selector). En general, los resultados muestran que cuando los participantes carecían de historia de interacción ante las contingencias programadas se requirió de un mayor número de sesiones de entrenamiento para mejorar el desempeño en organizaciones funcionales de mayor complejidad; y que cuando la historia de interacción estaba presente en la organización funcional en tanto interacción previa con las contingencias se encontró un efecto de facilitación en el ajuste conductual, independientemente de si la transición fue ascendente o descendente. Al final se indaga sobre si el incremento en el número de sesiones se relaciona con la complejidad de cada nivel de organización funcional, y se discute, respecto a las transiciones de entrenamiento funcional, ascendente y descendente, sus efectos en la facilitación en el aprendizaje y surelación con el supuesto de inclusividad funciona

Complejidad e inclusividad del comportamiento intrasituacional: Análisis empírico

Abstract Ribes and López's (1985) taxonomy proposed that psychological behavior is progressively complex and inclusive. In that respect, there is little research and data are not robust. A study was conducted with the purpose to increase data related to the complexity and inclusivity of the three less complex behaviors of the taxonomy, with three training sequences, namely: 1) ascending (contextual-supplementary-selector), 2) descending-ascending (supplementary -contextual-selector) and 3) descending (selector-supplementary-contextual). The objective was to evaluate the effect of the interaction history (related to training sequences) on differential (contextual), effective (supplementary) and precise (selector) behavior adjustment process. Results showed that a greater number of training sessions were required to improve the performance in functional organizations with greater complexity when participants lacked an interaction history related to programmed contingencies. But, when the interaction history participated in the functional organization, as a previous interaction with the contingencies, a facilitating effect was found in the behavioral adjustment, regardless of whether the transition was ascending or descending. It is discussed whether the increase in the number of sessions is related to the complexity of each level of functional organization. Regarding the functional training transitions, ascending and descending, its effects on facilitation in learning are discussed in relation to the assumption of functional inclusivity.Resumen En la taxonomía de Ribes y López (1985) se propone que el comportamiento psicológico es progresivamente complejo e inclusivo; sin embargo, en la literatura sobre el tema se encuentran pocas investigaciones y los datos no son robustos. Teniendo esto en cuenta, y con el propósito de aumentar la evidencia de la complejidad e inclusividad de los tres primeros niveles de complejidad conductual de la taxonomía en tres secuencias de entrenamiento -ascendente (contextual-suplementario-selector), descendente-ascendente (suplementario-contextual-selector) y descendente (selector-suplementario-contextual)-, el presente estudio tuvo como objetivo evaluar el efecto de la historia de interacción -por medio de secuencias de entrenamiento- sobre el proceso de ajuste diferencial (contextual), efectivo (suplementario) y preciso (selector). En general, los resultados muestran que cuando los participantes carecían de historia de interacción ante las contingencias programadas se requirió de un mayor número de sesiones de entrenamiento para mejorar el desempeño en organizaciones funcionales de mayor complejidad; y que cuando la historia de interacción estaba presente en la organización funcional -en tanto interacción previa con las contingencias- se encontró un efecto de facilitación en el ajuste conductual, independientemente de si la transición fue ascendente o descendente. Al final se indaga sobre si el incremento en el número de sesiones se relaciona con la complejidad de cada nivel de organización funcional, y se discute, respecto a las transiciones de entrenamiento funcional, ascendente y descendente, sus efectos en la facilitación en el aprendizaje y su relación con el supuesto de inclusividad funcional

Astrocitoma subependimario de células gigantes asociado a complejo de esclerosis tuberosa: recomendaciones para el diagnóstico oportuno y tratamiento.

RESUMEN El complejo de esclerosis tuberosa es una enfermedad genética poco frecuente, autosómica dominante con fenotipo y expresión clínica muy variables. Se caracteriza por alteraciones en la migración, diferenciación y proliferación celulares con formación de múltiples tumores benignos llamados hamartomas, las cuales afectan principalmente piel, encéfalo, riñón, ojo, corazón y pulmón. Los astrocitomas subependimarios de células gigantes son tumores benignos de crecimiento lento y son los más frecuentes en el sistema nervioso central de los pacientes con complejo de esclerosis tuberosa. Actualmente existen medicamentos indicados en pacientes con astrocitomas subependimarios de células gigantes asociados con complejo de esclerosis tuberosa y son una alternativa al tratamiento quirúrgico. Su mecanismo consiste en la inhibición el complejo 1 mTOR, acción que modula el defecto molecular que ocasiona al complejo de esclerosis tuberosa. Con su uso se han reportado disminución y estabilización de angiomiolipomas renales, linfangioleiomiomatosis, angiofibromas y de astrocitomas subependimarios de células gigantes asociados con complejo de esclerosis tuberosa