TRUSTD: Combat Fake Content using Blockchain and Collective Signature Technologies

The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called "filter-bubbles" leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolve some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature-based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents.Comment: arXiv admin note: text overlap with arXiv:1812.00315, arXiv:1807.06346, arXiv:1904.05386 by other author

From algorithm selection to generation using deep learning

Algorithm selection and generation techniques are two methods that can be used to exploit the performance complementarity of different algorithms when applied to large diverse sets of combinatorial problem instances. As there is no single algorithm that dominates all others on all problem instances, algorithm selection automatically selects an algorithm expected to perform best for each problem instance. Meanwhile, algorithm generation refers to combining different algorithms in a manner that allows the resulting method to improve the efficacy of a pool of algorithms. This thesis examines algorithm selection and generation within a single streaming problem domain, that is Bin-Packing, where novel approaches are proposed and evaluated on large problem sets. This research starts with presenting a novel feature-free approach to select the best performing heuristic by capturing the sequential information implicit in a streaming instance and using this as direct input to two Deep Learning (DL) models, Long-Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), to learn a mapping from an instance to an algorithm. Results obtained using the proposed approach show that the performance of the feature-free selectors significantly outperforms the performance of both the single best solver and the classical feature-based approach using well-known Machine Learning (ML) classifiers when applied to large sets of diverse problem instances. Next, a more radical approach is proposed: bypass algorithm selection altogether by training encoder-decoder LSTM using solutions obtained from a set of algorithms to directly predict a solution from the instance data behaving as an automatically generated algorithm. Experiments conducted on large datasets using problem batches of varying sizes show that the generated algorithm is able to accurately predict solutions, particularly with small batch sizes. Finally, the thesis develops the proposed encoder-decoder approach by introducing a novel neural approach for generating algorithms, in which a neural network acts as an algorithm by generating decisions. Two architectures are evaluated, an encoder-decoder LSTM and a feed-forward Neural Network (NN), and trained using the decisions output from existing algorithms on a large set of instances. Experiments show that the new generated algorithms are capable of solving a subset of instances better than the well-known bin-packing algorithms, and hence they can significantly improve the overall performance when they are added to a pool of algorithms

Spectral analysis of Monte Carlo calculated fluence correction and cema conversion factors for high-energy photon beams at different depths

Purpose: The aim of this study is to investigate the depth-dependent detector response of detailed thimble air-filled ionization chambers by calculating spectral charged particle fluence correction factors at different depths in water. Those spectral correction factors will help to understand, how the detector response varies at different depths and what kind of influences disparate effects have on the spectral detector response.Methods: The cema-approach can be used to obtain spectral charged particle fluence-based correction factors for various measurement conditions by substituting the commonly well-known dose conversion factor with a conversion factor based on the dosimetric quantity cema (“converted-energy per unit mass”). The resulting spectral fluence correction factors were calculated with the EGSnrc software toolkit and analyzed for two air-filled cylindrical ionization chambers (PTW type 31021 Semiflex 3D, SNC125c™) at different depths in a water phantom irradiated with a 6 MV linear accelerator x-ray spectrum. The ionization chamber models have been stepwise decomposed to investigate the perturbation caused by internal and external effects on the fluence distribution within the detector.Results: Monte Carlo calculated fluence-based perturbation correction factors revealed that for all investigated detectors, considerable fluence disturbances occur, especially in the build-up region of depth-dose curves. Our results have shown that even slight variations in depth can have major consequences on the differential charged particle fluence within the ionization chamber, mainly due to internal cavity-specific effects. Furthermore, the results showed that in the case of relative dose measurements, the depth-depending detector response can significantly differ from unity in a range of 1.4%–2.8% depending on the ionization chamber design.Conclusion: The complexity of different effects on the fluence disturbance could be broken down with regard to their influence on the spectral fluence distribution in the sensitive volume of the investigated detectors. It could be demonstrated, that the displacement of water is a depth-depending effect, which can not be compensated or corrected ideally for each investigated water depth by the shift of the effective point of measurement. Generally, the spectral analysis of those energy-dependent correction factors serves to a deeper understanding of the detector response under various conditions

Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient’s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With $93.5\%$ accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches

We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types of recurrent neural networks to predict a packing heuristic in online bin-packing, selecting from four well-known heuristics. As input, the RNN methods only use the sequence of item-sizes. This contrasts to typical approaches to algorithm-selection which require a model to be trained using domain-specific instance features that need to be first derived from the input data. The RNN approaches are shown to be capable of achieving within 5% of the oracle performance on between 80.88 and 97.63% of the instances, depending on the dataset. They are also shown to outperform classical machine learning models trained using derived features. Finally, we hypothesise that the proposed methods perform well when the instances exhibit some implicit structure that results in discriminatory performance with respect to a set of heuristics. We test this hypothesis by generating fourteen new datasets with increasing levels of structure, and show that there is a critical threshold of structure required before algorithm-selection delivers benefit

Present-day stress state in northwestern Syria

Para estudiar la tectónica activa y el patrón de estrés en el límite de placas convergentes de Arabia-Eurasia, el este del Mediterráneo de Siria nororiental es un área clave. Este estudio tiene como objetivo delinear el régimen de estrés actual en esta región, utilizando las soluciones de plano de falla de los eventos más grandes registrados por la Red Sismológica Nacional de Siria, de 1995 a 2011. Se obtuvo un conjunto de datos de soluciones de plano de falla para 48 eventos que tienen al menos 5 polaridades de onda P. El régimen tectónico para la mayoría de estos eventos es extenso y produce mecanismos normales de acuerdo con las configuraciones locales de las fallas sismogénicas en la región. Los mecanismos de deslizamiento son más escasos y están restringidos a ciertas áreas, como la extensión norte del sistema de fallas del Mar Muerto. Los resultados del estudio actual revelan las variaciones espaciales en la orientación de la tensión horizontal máxima (SHmax) en la región noroeste de Siria. Estas variaciones resaltan el papel de las principales zonas de cizallamiento geométricamente complejas en el patrón de tensión actual de esta región. Sin embargo, estos resultados, independientemente de la magnitud relativamente pequeña de los eventos estudiados, proporcionan una imagen de las desviaciones de esfuerzos locales que se han estado produciendo actualmente a lo largo de las fallas activas locales. doi: https://doi.org/10.22201/igeof.00167169p.2020.59.4.203

Factitious psychogenic nonepileptic paroxysmal episodes

Mistaking psychogenic nonepileptic paroxysmal episodes (PNEPEs) for epileptic seizures (ES) is potentially dangerous, and certain features should alert physicians to a possible PNEPE diagnosis. Psychogenic nonepileptic paroxysmal episodes due to factitious seizures carry particularly high risks of morbidity or mortality from nonindicated emergency treatment and, often, high costs in wasted medical treatment expenditures. We report a case of a 28-year-old man with PNEPEs that were misdiagnosed as ES. The patient had been on four antiseizure medications (ASMs) with therapeutic serum levels and had had multiple intubations in the past for uncontrolled episodes. He had no episodes for two days of continuous video-EEG monitoring. He then disconnected his EEG cables and had an episode of generalized stiffening and cyanosis, followed by jerking and profuse bleeding from the mouth. The manifestations were unusually similar to those of ES, except that he was clearly startled by spraying water on his face, while he was stiff in all extremities and unresponsive. There were indications that he had sucked blood from his central venous catheter to expel through his mouth during his PNEPEs while consciously holding his breath. Normal video-EEG monitoring; the patient's volitional and deceptive acts to fabricate the appearance of illness, despite pain and personal endangerment; and the absence of reward other than remaining in a sick role were all consistent with a diagnosis of factitious disorder