Guiding the retraining of convolutional neural networks against adversarial inputs

Background: When using deep learning models, there are many possible vulnerabilities and some of the most worrying are the adversarial inputs, which can cause wrong decisions with minor perturbations. Therefore, it becomes necessary to retrain these models against adversarial inputs, as part of the software testing process addressing the vulnerability to these inputs. Furthermore, for an energy efficient testing and retraining, data scientists need support on which are the best guidance metrics and optimal dataset configurations. Aims: We examined four guidance metrics for retraining convolutional neural networks and three retraining configurations. Our goal is to improve the models against adversarial inputs regarding accuracy, resource utilization and time from the point of view of a data scientist in the context of image classification. Method: We conducted an empirical study in two datasets for image classification. We explore: (a) the accuracy, resource utilization and time of retraining convolutional neural networks by ordering new training set by four different guidance metrics (neuron coverage, likelihood-based surprise adequacy, distance-based surprise adequacy and random), (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations (from scratch and augmented dataset, using weights and augmented dataset, and using weights and only adversarial inputs). Results: We reveal that retraining with adversarial inputs from original weights and by ordering with surprise adequacy metrics gives the best model w.r.t. the used metrics. Conclusions: Although more studies are necessary, we recommend data scientists to use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs

Citizen Action as a Driving Force of Change. The Meninas of Canido, Art in the Street as an Urban Dynamizer

The austerity policies imposed by the government in the wake of the 2007 crisis have deteriorated the welfare state and limited neighborhood recovery. Considering the inability and inefficiency on the part of administrations to carry out improvement actions in neighborhoods, it is the neighborhood action itself that has carried out a series of resilient social innovations to reverse the dynamics. In this article, we will analyze the Canido neighborhood in Ferrol, a city in north-western Spain. Canido is traditional neighborhood that was experiencing a high degree of physical and social deterioration, until a cultural initiative called “Meninas of Canido,” promoted by one of its artist neighbors, recovered its identity and revitalized it from a physical, social, and economic point of view. Currently, the Meninas of Canido has become one of the most important urban art events in Spain and has receives international recognition. The aim of this article is to evaluate the impact that this action has had in the neighborhood. For this, we conducted a series of semi-structured interviews with the local administration, neighborhood association, the precursors of this idea, merchants, and some residents in general, in order to perceive the reception and evolution of this action.This research was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) New Models for Governing Cities and Intervention in Urban Spaces in the Post-Crisis Period (CSO2016-75236-C2-1-R).S

The Lessons of Public–Private Collaboration for Energy Regeneration in a Spanish City. The Case of Txantrea Neighbourhood (Pamplona)

Although the transformation of the energy model is a global problem, cities take on a leading role in the process as they are important consumers of energy resources. For years, local authorities have been implementing various energy saving initiatives. The transport and equipment renovation sectors, as well as the residential renovation sector, are the focus of the objectives of local strategies to reduce greenhouse gas (GHG) emissions. In this article we analyse the role of local government in the energy transition, its relationship with other public–private territorial agents, and the involvement of citizens in the design and implementation of their initiatives. To this end, we will focus on the case of Pamplona, a city in the north of Spain with a policy aimed at low-energy, renewable, decentralised, and sustainable restructuring. We will analyse the heating districts of its Txantrea neighbourhood. By means of qualitative information obtained through interviews, we will see how the project has been carried out, which actors participated, the problems encountered, and how it has impacted savings, the improvement of quality of life of the residents, and urban and energetic regeneration processesThis research was funded by the State Research Agency, Ministry of Science and Innovation Reference PID2019-108120RB-C31S

Geranium's response to compost based substrates

8 páginas, 2 figuras, 3 tablas, 10 referencias.-- International Symposium on Composting and use of Composted Materials for Horticulture, celebrado del 5-11 de abril 1997, en Ayr, Scotland, United Kingdom.The effects of compost based substrates on growth and nutrition of geranium (Pelargonium zonale cv Lucky Break F2) were investigated. Substrates of manure compost, cotton gin trash compost, municipal solid waste compost and pine bark utilized as potting media for domestic use, produced an underdevelopment of geranium plants with respect to the control. This behaviour is related to the inferior physical properties of the compost-based potting media, nitro gen immobilization due to the high C/N ratio of pine bark, and probably lack of available phosphorus originated by high calcium and high pH of the compost-based media. Nitrogen fertilization and a longer period of cultivation diminished the differences between plants grown in the control and in compost-based media. Plants grown in compost mixtures which were rich in K showed K and Ca leaf contents closer to the optimum range than did control plants.This work was supported by the Agencia de Medio Ambiente of the Junta de Andalucía and by Fertilizantes Orgánicos Melguizo, S. L.Peer reviewe

An Automated Fall Detection System Using Recurrent Neural Networks

Falls are the most common cause of fatal injuries in elderly people, causing even death if there is no immediate assistance. Fall detection systems can be used to alert and request help when this type of accident happens. Certain types of these systems include wearable devices that analyze bio-medical signals from the person carrying it in real time. In this way, Deep Learning algorithms could automate and improve the detection of unintentional falls by analyzing these signals. These algorithms have proven to achieve high effectiveness with competitive performances in many classification problems. This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection based on accelerometer data, reducing computational requirements of previous research. The architectures have been tested on a labeled version of the publicly available SisFall dataset, achieving a mean F1-score above 0.73 and improving state-of-the-art solutions in terms of network complexity.Ministerio de Economía y Competitivida TEC2016-77785-

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-

Multi-dataset Training for Medical Image Segmentation as a Service

Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.Ministerio de Economía y Competitividad TEC2016-77785-

Guiding the retraining of convolutional neural networks against adversarial inputs

Background: When using deep learning models, one of the most critical vulnerabilities is their exposure to adversarial inputs, which can cause wrong decisions (e.g., incorrect classification of an image) with minor perturbations. To address this vulnerability, it becomes necessary to retrain the affected model against adversarial inputs as part of the software testing process. In order to make this process energy efficient, data scientists need support on which are the best guidance metrics for reducing the adversarial inputs to create and use during testing, as well as optimal dataset configurations. Aim: We examined six guidance metrics for retraining deep learning models, specifically with convolutional neural network architecture, and three retraining configurations. Our goal is to improve the convolutional neural networks against the attack of adversarial inputs with regard to the accuracy, resource utilization and execution time from the point of view of a data scientist in the context of image classification. Method: We conducted an empirical study using five datasets for image classification. We explore: (a) the accuracy, resource utilization, and execution time of retraining convolutional neural networks with the guidance of six different guidance metrics (neuron coverage, likelihood-based surprise adequacy, distance-based surprise adequacy, DeepGini, softmax entropy and random), (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations (one-step adversarial retraining, adversarial retraining and adversarial fine-tuning). Results: We reveal that adversarial retraining from original model weights, and by ordering with uncertainty metrics, gives the best model w.r.t. accuracy, resource utilization, and execution time. Conclusions: Although more studies are necessary, we recommend data scientists use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs and without creating numerous adversarial inputs. We also show that dataset size has an important impact on the results.This work was supported by the GAISSA Spanish research project (ref. TED2021-130923B-I00; MCIN/AEI/10.13039/501100011033), the “UNAM-DGECI: Iniciación a la Investigación (verano otoño 2021)” scholarship provided by Universidad Nacional Autónoma de México (UNAM), the “Beatriz Galindo” Spanish Program BEAGAL18/00064, the Austrian Science Fund (FWF): I 4701-N and the project Continuous Testing in Production (ConTest) funded by the Austrian Research Promotion Agency (FFG): 888127.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaPostprint (published version

Use of a D-optimal design with constrains to quantify the effects of the mixture of sodium, potassium, calcium and magnesium chloride salts on the growth parameters of Saccharomyces cerevisiae.

The combined effect of NaCl, KCl, CaCl(2), and MgCl(2) on the water activity (a (w)) and the growth parameters of Saccharomyces cerevisiae was studied by means of a D-optimal mixture design with constrains (total salt concentrationsor = 9.0%, w/v). The a (w) was linearly related to the concentrations of the diverse salts; its decrease, by similar concentrations of salts, followed the order NaClCaCl(2)KClMgCl(2), regardless of the reference concentrations used (total absence of salts or 5% NaCl). The equations that expressed the maximum specific growth (mu (max)), lag phase duration (lambda), and maximum population reached (N (max)) showed that the values of these parameters depended on linear effects and two-way interactions of the studied chloride salts. The mu (max) decreased as NaCl and CaCl(2) increased (regardless of the presence or not of previous NaCl); however, in the presence of a 5% NaCl, a further addition of KCl and MgCl(2) markedly increased mu (max). The lambda was mainly affected by MgCl(2) and the interactions NaCl x CaCl(2) and CaCl(2) x MgCl(2). The further addition of NaCl and CaCl(2) to a 5% NaCl medium increased the lag phase while KCl and MgCl(2) had negligible or slightly negative effect, respectively. N (max) was mainly affected by MgCl(2) and its interactions with NaCl, KCl, and CaCl(2); MgCl(2) stimulated N (max) in the presence of 5% NaCl while KCl, NaCl, and CaCl(2) had a progressive decreasing effect. These results can be of interest for the fermentation and preservation of vegetable products, and foods in general, in which this yeast could be present