Biometric identity systems in law enforcement and the politics of (voice)recognition: the case of SiiP

Biometric identity systems are now a prominent feature of contemporary law enforcement, including in Europe. Often advanced on the premise of efficiency and accuracy, they have also been the subject of significant controversy. Much attention has focussed on longer-standing biometric data collection, such as finger-printing and facial recognition, foregrounding concerns with the impact such technologies can have on the nature of policing and fundamental human rights. Less researched is the growing use of voice recognition in law enforcement. This paper examines the case of the recent Speaker Identification Integrated Project, a European wide initiative to create the first international and interoperable database of voice biometrics, now the third largest biometric database at Interpol. Drawing on Freedom of Information requests, interviews and public documentation, we outline the emergence and features of SiiP and explore how voice is recognised and attributed meaning. We understand Speaker Identification Integrated Project as constituting a particular 'regime of recognition' premised on the use of soft biometrics (age, language, accent and gender) to disembed voice in order to optimise for difference. This, in turn, has implications for the nature and scope of law enforcement, people's position in society, and justice concerns more broadly

How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees

Fair machine learning works have been focusing on the development of equitable algorithms that address discrimination of certain groups. Yet, many of these fairness-aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions. We present the first methodology that allows to explore those limits within a multi-objective framework that seeks to optimize any measure of accuracy and fairness and provides a Pareto front with the best feasible solutions. In this work, we focus our study on decision tree classifiers since they are widely accepted in machine learning, are easy to interpret and can deal with non-numerical information naturally. We conclude experimentally that our method can optimize decision tree models by being fairer with a small cost of the classification error. We believe that our contribution will help stakeholders of sociotechnical systems to assess how far they can go being fair and accurate, thus serving in the support of enhanced decision making where machine learning is used

The water-soluble organic fraction and its relationships to the degree of maturity of organic matter during composting

The evolution of different components of the water-soluble organic matter, water soluble carbon (COW), carbohydrates and phenols were studied during the composting of six different mixtures of organic wastes prepared with sweet sorghum bagasse, cotton waste, sewage sludge, municipal solid waste, urea, pine bark and brewery sludge. The COW, carbohydrate and phenol concentrations decreased in the six composting mixtures as a consequence of the organic matter degradation carried out by the microbial activity. The intensity of this organic matter degradation and the evolution of the water-soluble compounds depended on the kind of material used in the starting mixtures. Therefore, these parameters were not considered suitable to be used as the basis for a general organic matter stabilisation index. The changes in the water-soluble organic carbon to water-soluble organic nitrogen ratio (COW/NOW) and the water-soluble organic carbon to total organic nitrogen ratio (COW/NOT) were considered to be suitable as general stabilisation indices since these ratios did not depend on the material used. The evolution of these two ratios showed a similar pattern during the composting of the six mixtures studied. All mature composts reached values for these ratios which were in agreement with the ranges proposed by other authors with other type of materials. These maturity indices were also compared with Lepidium Sativum L. germination assays and no phytotoxic effects were found in materials with COW/NOW and COW/NOT ratios values between the limits established for mature composts (COW/OW between the range 5-6, and COW/NOT < 0.40).The authors wish to thank the Spanish CICYT for the support of the PETRI project N ref: 95-0234-OP-02-02 under which has financed this work.Peer reviewe

Design of wide-beam leaky-wave antenna arrays based on the bilinear transformation of IIR digital filters and the Z transform

In the pioneering work, the radiation diagrams of leaky wave antenna arrays can achieve attenuation nulls and gains at specific angles by manually placing the zeros and the poles in the Z domain of the corresponding discrete linear time-invariant (LTI) system. This handcrafted design procedure does not allow radiation diagrams with wide beams since the interaction between poles involved in the wide beam and their corresponding leaky modes cannot be easily handled. To overcome this limitation, this paper describes a novel method for designing radiation diagrams of leaky-wave antenna arrays based on the theory of IIR discrete filters. The proposed method relies on the design of discrete filters with the prototypes of analog low-pass filters defined by Butterworth and Chebyshev type I polynomials, whose roots along with the bilinear transformation provide the location of the poles and the zeros of the discrete LTI system and, therefore, the parameters of the leaky-wave antenna array. Results with different designs and a comparison with other approaches show the utility and effectiveness of this novel method to design wide-beam leaky-wave antenna arrays.This work was supported by the Spanish National project PID2019-103982RB-C42/AEI/10.13039/501100011033

Improving glaucoma diagnosis assembling deep networks and voting schemes

Glaucoma is a group of eye conditions that damage the optic nerve, the health of which is vital for good eyesight. This damage is often caused by higher-than-normal pressure in the eye. In the past few years, the applications of artificial intelligence and data science have increased rapidly in medicine especially in imaging applications. In particular, deep learning tools have been successfully applied obtaining, in some cases, results superior to those obtained by humans. In this article, we present a soft novel ensemble model based on the K-NN algorithm, that combines the probability of class membership obtained by several deep learning models. In this research, three models of different nature (CNN, CapsNets and Convolutional Autoencoders) have been selected searching for diversity. The latent space of these models are combined using the local information provided by the true sample labels and the K-NN algorithm is applied to determine the final decision. The results obtained on two different datasets of retinal images show that the proposed ensemble model improves the diagnosis capabilities for both the individual models and the state-of-the-art results.This research was funded by Instituto de Salud Carlos III grant number AES2017-PI17/007 and Fundación Séneca grant number 20901/PI/18. The APC was funded by Fundación Séneca grant number 20901/PI/18

A guided data projection technique for classi cation of sovereign ratings: the case of European Union 27

Sovereign rating has had an increasing importance since the beginning of the nancial crisis. However, credit rating agencies opacity has been criticised by several authors highlighting the suitability of designing more objective alternative methods. This paper tackles the sovereign credit rating classi cation problem within an ordinal classi cation perspective by employing a pairwise class distances projection to build a classi cation model based on standard regression techniques. In this work the -SVR is selected as the regressor tool. The quality of the projection is validated through the classi cation results obtained for four performance metrics when applied to Standard & Poors, Moody's and Fitch sovereign rating data of U27 countries during the period 2007-2010. This validated projection is later used for ranking visualization which might be suitable to build a decision support syste

Overcoming biochar limitations to remediate pentachlorophenol in soil by modifying its electrochemical properties

In this study, we produced modified biochars with enhanced electrochemical properties to increase PCP remediation in soil. Although all biochars enhanced PCP remediation in aerobic conditions, only a few did in anaerobic soil. The most successful modifications were (i) the preloading of biomass with 10% w/w FeCl3, to obtain a biochar rich in redox-active metals (B-Fe); (ii) the oxidation of a conductive biochar pyrolyzed at 1000 ºC with 0.025 M KMnO4, to produce a biochar with both moderate conductivity and redox capacity (B-1000-KMnO4); and (iii) KMnO4 oxidation of an amorphous biochar pyrolyzed at 400 ºC to obtain a biochar with very high redox capacity (B-KMnO4). B-Fe reduced extractable PCP to almost zero after 50 days in both incubations, but showed slow kinetics of remediation in aerobic soil. B-1000-KMnO4 had the highest rate of remediation under aerobic conditions, but no significant effect under anaerobic conditions. B-KMnO4, however, presented high rates of remediation and high removal of extractable PCP under both conditions, which made it the recommended modification strategy for increased PCP remediation. We found that the degree of remediation primarily depends on the redox capacity, while the rate of remediation was determined by both the conductivity and redox capacity of biochar

A hybrid framework for efficient and accurate orientation estimation based on single and multiple orientation vector fields

This article presents a hybrid framework for efficient and accurate orientation estimation. The proposed scheme combines the single orientation information given by a novel method and the multiple orientation information provided by a bank of linear orientated morphological openings. The single orientations are estimated by means of an energy-minimization Gaussian filtering which solves the drawback related to phase changes of other methods. After describing the formulation of these two approaches for estimating the existing orientations in the pixels of an image, several strategies have been analyzed to fuse and discriminate the information of both orientation vector fields in the resulting hybrid orientation vector field. The objective of the proposed hybrid method is to reduce the computational cost involved in calculating multiple orientations only in those pixels where they exist while maintaining the accuracy provided by the single orientation method in the remaining pixels. To this end, strategies ranging from a threshold in the multiple orientation vector field to a convolutional neural network trained with a set of patterns specifically designed to detect pixels with multiple orientations, passing through the Harris corner detector, have been tested to identify those pixels where multiple orientations exist. Results on natural and synthetic images show the accuracy and the computational efficiency achieved by the proposed hybrid framework to provide the vector field with single and multiple orientations

Electrochemical performance of activated screen printed carbon electrodes for hydrogen peroxide and phenol derivatives sensing

Screen-printed carbon electrodes (SPCEs) are widely used for the electroanalysis of a plethora of organic and inorganic compounds. These devices offer unique properties to address electroanalytical chemistry challenges and can successfully compete in numerous aspects with conventional carbon-based electrodes. However, heterogeneous kinetics on SPCEs surfaces is comparatively sluggish, which is why the electrochemical activation of inks is sometimes required to improve electron transfer rates and to enhance sensing performance. In this work, SPCEs were subjected to different electrochemical activation methods and the response to H2O2 electroanalysis was used as a testing probe. Changes in topology, surface chemistry and electrochemical behavior to H2O2 oxidation were performed by SEM, XPS, cyclic voltammetry, chronoamperometry and electrochemical impedance spectroscopy. The combination of electrochemical activation methods using H2SO4 and H2O2 proved particularly effective. A reduction in charge transfer resistance, together with functionalization with some carbon‑oxygen groups on carbon ink surfaces, were likely responsible for such electrochemical improvement. The use of a two-step protocol with 0.5 M H2SO4 and 10 mM H2O2 under potential cycling conditions was the most effective activation procedure investigated herein, and gave rise to 518-fold higher sensitivity than that obtained for the untreated SPCEs upon H2O2 electrooxidation. The electrochemical behavior of acetaminophen, hydroquinone and dopamine is also shown, as a proof of concept upon the optimum activated SPCEs.This work was funded by the Spanish Ministry of Economy and Competitiveness (MINECO, http://www.mineco.gob.es/portal/site/mineco/idi), Projects No. BFU2016-75609-P (AEI/FEDER, UE) and CTQ2016-76231-C2-2-R, and by the Junta de Comunidades de Castilla-La Mancha (Spain), Project No. SBPLY/17/180501/000276/2 (cofunded with FEDER funds, EU). BGM is a post-doctoral research fellow of the Youth Employment Initiative (JCCM, Spain, cofunded with ESF funds, EU)