Deep Generative Variational Autoencoding for Replay Spoof Detection in Automatic Speaker Verification

Automatic speaker verification (ASV) systems are highly vulnerable to presentation attacks, also called spoofing attacks. Replay is among the simplest attacks to mount - yet difficult to detect reliably. The generalization failure of spoofing countermeasures (CMs) has driven the community to study various alternative deep learning CMs. The majority of them are supervised approaches that learn a human-spoof discriminator. In this paper, we advocate a different, deep generative approach that leverages from powerful unsupervised manifold learning in classification. The potential benefits include the possibility to sample new data, and to obtain insights to the latent features of genuine and spoofed speech. To this end, we propose to use variational autoencoders (VAEs) as an alternative backend for replay attack detection, via three alternative models that differ in their class-conditioning. The first one, similar to the use of Gaussian mixture models (GMMs) in spoof detection, is to train independently two VAEs - one for each class. The second one is to train a single conditional model (C-VAE) by injecting a one-hot class label vector to the encoder and decoder networks. Our final proposal integrates an auxiliary classifier to guide the learning of the latent space. Our experimental results using constant-Q cepstral coefficient (CQCC) features on the ASVspoof 2017 and 2019 physical access subtask datasets indicate that the C-VAE offers substantial improvement in comparison to training two separate VAEs for each class. On the 2019 dataset, the C-VAE outperforms the VAE and the baseline GMM by an absolute 9-10% in both equal error rate (EER) and tandem detection cost function (t-DCF) metrics. Finally, we propose VAE residuals --- the absolute difference of the original input and the reconstruction as features for spoofing detection. The proposed frontend approach augmented with a convolutional neural network classifier demonstrated substantial improvement over the VAE backend use case

Embedded Based Smart ICU-For Intelligent Patient Monitoring

Smart ICUs are networks of audio-visual communication and computer systems that link critical care doctors and nurses (intensivists) to intensive care units (ICUs) in other, remote hospitals. The intensivists in the “command center” can communicate by voice with the remote ICU personnel and can receive video communication and clinical data about the patients. Direct patient care is provided by the doctors and nurses in the remote ICU who do not have to be intensivists themselves. In recent years there has been an increase in the number of patients needing ICU care without a corresponding increase in the supply of intensivists. Smart ICUs can be a valuable resource for hospitals faced with the need to expand capacity and improve care for a growing elderly population. Evidence from some early-adopter hospitals indicates that it can leverage management of patient care by intensivists, reduce mortality rates, and reduce LOS. However, positive outcomes appear to depend on the organizational environment into which the Smart ICU is introduced. The dramatic improvements in mortality and LOS reported by some early-adopter hospitals have not been matched in most. The limited research available suggests that the best outcomes may occur in ICUs that: Can make organizational arrangements to support the management of patient care by intensivists using Smart ICU; Have little or no intensivist staff available to them in the absence of Smart ICU; Have relatively high severity-adjusted mortality and LOS rates; Are located in remote or rural areas where safe and efficient transfer of patients to regional centers for advanced critical care presents difficulties. Smart ICU connects a central command center staffed by intensivists with patients in distant ICUs. Continuous, real-time audio, video, and electronic reports of vital signs connect the command center to the patients’ bedsides. Computer-managed decision support systems track each patient’s status and give alerts when negative trends are detected and when changes in treatment patterns are scheduled. The patient data include physiological status (e.g., ECG and blood oxygenation), treatment (e.g., the infusion rate for a specific medicine or the settings on a respirator), and medical records.

Intracellular Photophysics of an Osmium Complex bearing an Oligothiophene Extended Ligand

This contribution describes the excited-state properties of an Osmium-complex when taken up into human cells. The complex 1 [Os(bpy)2(IP-4T)](PF6)2 with bpy=2,2′-bipyridine and IP-4T=2-{5′-[3′,4′-diethyl-(2,2′-bithien-5-yl)]-3,4-diethyl-2,2′-bithiophene}imidazo[4,5-f][1,10]phenanthroline) can be discussed as a candidate for photodynamic therapy in the biological red/NIR window. The complex is taken up by MCF7 cells and localizes rather homogeneously within in the cytoplasm. To detail the sub-ns photophysics of 1, comparative transient absorption measurements were carried out in different solvents to derive a model of the photoinduced processes. Key to rationalize the excited-state relaxation is a long-lived 3ILCT state associated with the oligothiophene chain. This model was then tested with the complex internalized into MCF7 cells, since the intracellular environment has long been suspected to take big influence on the excited state properties. In our study of 1 in cells, we were able to show that, though the overall model remained the same, the excited-state dynamics are affected strongly by the intracellular environment. Our study represents the first in depth correlation towards ex-vivo and in vivo ultrafast spectroscopy for a possible photodrug. © 2020 The Authors. Published by Wiley-VCH Gmb

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modelling approach. For robustness, we use both deep neural networks and traditional machine learning models and combine them as ensemble models through logistic regression. They are trained to detect logical access (LA) and physical access (PA) attacks on the dataset released as part of the ASV Spoofing and Countermeasures Challenge 2019. We propose dataset partitions that ensure different attack types are present during training and validation to improve system robustness. Our ensemble model outperforms all our single models and the baselines from the challenge for both attack types. We investigate why some models on the PA dataset strongly outperform others and find that spoofed recordings in the dataset tend to have longer silences at the end than genuine ones. By removing them, the PA task becomes much more challenging, with the tandem detection cost function (t-DCF) of our best single model rising from 0.1672 to 0.5018 and equal error rate (EER) increasing from 5.98% to 19.8% on the development set

Can working with the private for-profit sector improve utilization of quality health services by the poor? A systematic review of the literature

BACKGROUND: There has been a growing interest in the role of the private for-profit sector in health service provision in low- and middle-income countries. The private sector represents an important source of care for all socioeconomic groups, including the poorest and substantial concerns have been raised about the quality of care it provides. Interventions have been developed to address these technical failures and simultaneously take advantage of the potential for involving private providers to achieve public health goals. Limited information is available on the extent to which these interventions have successfully expanded access to quality health services for poor and disadvantaged populations. This paper addresses this knowledge gap by presenting the results of a systematic literature review on the effectiveness of working with private for-profit providers to reach the poor. METHODS: The search topic of the systematic literature review was the effectiveness of interventions working with the private for-profit sector to improve utilization of quality health services by the poor. Interventions included social marketing, use of vouchers, pre-packaging of drugs, franchising, training, regulation, accreditation and contracting-out. The search for published literature used a series of electronic databases including PubMed, Popline, HMIC and CabHealth Global Health. The search for grey and unpublished literature used documents available on the World Wide Web. We focused on studies which evaluated the impact of interventions on utilization and/or quality of services and which provided information on the socioeconomic status of the beneficiary populations. RESULTS: A total of 2483 references were retrieved, of which 52 qualified as impact evaluations. Data were available on the average socioeconomic status of recipient communities for 5 interventions, and on the distribution of benefits across socioeconomic groups for 5 interventions. CONCLUSION: Few studies provided evidence on the impact of private sector interventions on quality and/or utilization of care by the poor. It was, however, evident that many interventions have worked successfully in poor communities and positive equity impacts can be inferred from interventions that work with types of providers predominantly used by poor people. Better evidence of the equity impact of interventions working with the private sector is needed for more robust conclusions to be drawn

The Genomes of the Fungal Plant Pathogens Cladosporium fulvum and Dothistroma septosporum Reveal Adaptation to Different Hosts and Lifestyles But Also Signatures of Common Ancestry.

We sequenced and compared the genomes of the Dothideomycete fungal plant pathogensCladosporium fulvum (Cfu) (syn. Passalora fulva) and Dothistroma septosporum (Dse) that are closely related phylogenetically, but have different lifestyles and hosts. Although both fungi grow extracellularly in close contact with host mesophyll cells, Cfu is a biotroph infecting tomato, while Dse is a hemibiotroph infecting pine. The genomes of these fungi have a similar set of genes (70% of gene content in both genomes are homologs), but differ significantly in size (Cfu \u3e61.1-Mb; Dse 31.2-Mb), which is mainly due to the difference in repeat content (47.2% in Cfu versus 3.2% in Dse). Recent adaptation to different lifestyles and hosts is suggested by diverged sets of genes. Cfu contains an α-tomatinase gene that we predict might be required for detoxification of tomatine, while this gene is absent in Dse. Many genes encoding secreted proteins are unique to each species and the repeat-rich areas in Cfu are enriched for these species-specific genes. In contrast, conserved genes suggest common host ancestry. Homologs of Cfu effector genes, including Ecp2 and Avr4, are present in Dse and induce a Cf-Ecp2- and Cf-4-mediated hypersensitive response, respectively. Strikingly, genes involved in production of the toxin dothistromin, a likely virulence factor for Dse, are conserved in Cfu, but their expression differs markedly with essentially no expression by Cfu in planta. Likewise, Cfu has a carbohydrate-degrading enzyme catalog that is more similar to that of necrotrophs or hemibiotrophs and a larger pectinolytic gene arsenal than Dse, but many of these genes are not expressed in planta or are pseudogenized. Overall, comparison of their genomes suggests that these closely related plant pathogens had a common ancestral host but since adapted to different hosts and lifestyles by a combination of differentiated gene content, pseudogenization, and gene regulation

The Genomes of the Fungal Plant Pathogens Cladosporium fulvum and Dothistroma septosporum Reveal Adaptation to Different Hosts and Lifestyles But Also Signatures of Common Ancestry

We sequenced and compared the genomes of the Dothideomycete fungal plant pathogens Cladosporium fulvum (Cfu) (syn. Passalora fulva) and Dothistroma septosporum (Dse) that are closely related phylogenetically, but have different lifestyles and hosts. Although both fungi grow extracellularly in close contact with host mesophyll cells, Cfu is a biotroph infecting tomato, while Dse is a hemibiotroph infecting pine. The genomes of these fungi have a similar set of genes (70% of gene content in both genomes are homologs), but differ significantly in size (Cfu >61.1-Mb; Dse 31.2-Mb), which is mainly due to the difference in repeat content (47.2% in Cfu versus 3.2% in Dse). Recent adaptation to different lifestyles and hosts is suggested by diverged sets of genes. Cfu contains an a-tomatinase gene that we predict might be required for detoxification of tomatine, while this gene is absent in Dse. Many genes encoding secreted proteins are unique to each species and the repeat-rich areas in Cfu are enriched for these species-specific genes. In contrast, conserved genes suggest common host ancestry. Homologs of Cfu effector genes, including Ecp2 and Avr4, are present in Dse and induce a Cf-Ecp2- and Cf-4-mediated hypersensitive response, respectively. Strikingly, genes involved in production of the toxin dothistromin, a likely virulence factor for Dse, are conserved in Cfu, but their expression differs markedly with essentially no expression by Cfu in planta. Likewise, Cfu has a carbohydrate-degrading enzyme catalog that is more similar to that of necrotrophs or hemibiotrophs and a larger pectinolytic gene arsenal than Dse, but many of these genes are not expressed in planta or are pseudogenized. Overall, comparison of their genomes suggests that these closely related plant pathogens had a common ancestral host but since adapted to different hosts and lifestyles by a combination of differentiated gene content, pseudogenization, and gene regulatio