Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightweight Searchable Public-Key Encryption for Cloud-Assisted Wireless Sensor Networks

The industrial Internet of Things is flourishing, which is unprecedentedly driven by the rapid development of wireless sensor networks (WSNs) with the assistance of cloud computing. The new wave of technology will give rise to new risks to cyber security, particularly the data confidentiality in cloud-assisted WSNs (CWSNs). Searchable public-key encryption (SPE) is a promising method to address this problem. In theory, it allows sensors to upload public-key ciphertexts to the cloud, and the owner of these sensors can securely delegate a keyword search to the cloud and retrieve the intended data while maintaining data confidentiality. However, all existing and semantically secure SPE schemes have expensive costs in terms of generating ciphertexts and searching keywords. Hence, this paper proposes a lightweight SPE (LSPE) scheme with semantic security for CWSNs. LSPE reduces a large number of the computation-intensive operations that are adopted in previous works; thus, LSPE has search performance close to that of some practical searchable symmetric encryption schemes. In addition, LSPE saves considerable time and energy costs of sensors for generating ciphertexts. Finally, we experimentally test LSPE and compare the results with some previous works to quantitatively demonstrate the above advantages.
Bridging the Gap: a Semantic Similarity Measure between Queries and Documents

The main approach of traditional information retrieval (IR) is to examine how many words from a query appear in a document. A drawback of this approach, however, is that it may fail to detect relevant documents where no or only few words from a query are found. The semantic analysis methods such as LSA (latent semantic analysis) and LDA (latent Dirichlet allocation) have been proposed to address the issue, but their performance is not superior compared to common IR approaches. Here we present a query-document similarity measure motivated by the Word Mover's Distance. Unlike other similarity measures, the proposed method relies on neural word embeddings to calculate the distance between words. Our method is efficient and straightforward to implement. The experimental results on TREC and PubMed show that our approach provides significantly better performance than BM25. We also discuss the pros and cons of our approach and show that there is a synergy effect when the word embedding measure is combined with the BM25 function.
Characterising Probabilistic Processes Logically

In this paper we work on (bi)simulation semantics of processes that exhibit both nondeterministic and probabilistic behaviour. We propose a probabilistic extension of the modal mu-calculus and show how to derive characteristic formulae for various simulation-like preorders over finite-state processes without divergence. In addition, we show that even without the fixpoint operators this probabilistic mu-calculus can be used to characterise these behavioural relations in the sense that two states are equivalent if and only if they satisfy the same set of formulae.
Virulence Factors and Antibiotic Resistance of Klebsiella pneumoniae Strains Isolated From Neonates With Sepsis

Introduction: Klebsiella pneumoniae is one of the most important infectious agents in neonates. There are “classic” and hypervirulent strains of K. pneumoniae. The “classic” non-virulent strain of K. pneumoniae, producing extended-spectrum beta-lactamases (ESBLs), is associated with nosocomial infections. Hypervirulent K. pneumoniae strains are associated with invasive infections in previously healthy adult people, and most of them exhibit antimicrobial susceptibility. The role of virulent strains of K. pneumoniae (including hv-KP) in neonatal infections is unknown. The aim of the study was the assessment of the impact of virulence factors and antibiotic resistance of K. pneumoniae strains on clinical features and outcomes of neonatal infection. Materials and Methods: Two groups of infants were enrolled. The first group consisted of 10 neonates with sepsis caused by K. pneumoniae. The second group consisted of 10 neonates with urinary tract infection (UTI) caused by K. pneumoniae. We investigated the susceptibility of K. pneumoniae isolates to antibiotics, the ability of the microorganism to produce ESBL, and virulence factors, including the rmpA gene, aerobactin, and colibactin genes. In neonates with sepsis, we investigated K. pneumoniae isolates, which was taken from the blood, in neonates with UTI—from the urine. Results: In neonates with sepsis testing of K. pneumoniae isolates for ESBL production was positive in 60% of cases, in neonates with UTI—in 40% of cases. All blood and urine ESBL producing K. pneumoniae isolates were resistant to ampicillins, including protected ones, and third-generation cephalosporins. At the same time, these isolates were sensitive to meropenem, amikacin, and ciprofloxacin. The rmpA gene was detected in four blood, and three urine K. pneumoniae isolates. In neonates with sepsis rmpA gene in two cases was detected in ESBL-producing K. pneumoniae isolates. They were infants with meningitis, and both cases were fatal. In the group of infants with UTI, the rmpA gene was detected only in K. pneumoniae isolates not producing ESBL. Aerobactin and colibactin genes were detected in two neonates with sepsis and in three neonates with UTI. In all cases, aerobactin and colibactin genes were detected only in rmpA-positive K. pneumoniae isolates. Out of three fatal outcomes, two cases were caused by hv-KP producing ESBL. Conclusion: The prevalence of virulent strains of K. pneumoniae among neonates with sepsis and other neonatal infection is higher than we think. The most severe forms of neonatal sepsis with an unfavorable outcome in our study were due to virulent strains of K. pneumoniae.
Online search in english as a non‐native language

Non‐native English speakers (NNESs) often search in English due to the limited availability of information in their native language on the Web. Information seeking in a non‐native language can present special challenges for users. Current research literature on non‐native language search behavior is insufficient and even less is known about how online systems and tools may accommodate NNESs’ needs and assist their behaviors. To gain a better understanding of user behavior and the search process of NNESs, this paper presents a study of online searching in English as a foreign language (EFL) or second‐language (L2). Particular attention is paid to language selection, search challenges, query formulation and reformulation, as well as user interaction with online systems and tools. Results from eight focus groups (36 participants) and 36 questionnaires indicate NNESs face a unique set of challenges that may not be present for native speakers when searching for information in English. A user interaction model is abstracted to address the iterative and spiral search process of NNESs. Implications for design of systems and tools to assist this particular user group are discussed.
Persistence of Zika Virus in Body Fluids — Final Report

Background To estimate the frequency and duration of detectable Zika virus (ZIKV) RNA in human body fluids, we prospectively assessed a cohort of recently infected participants in Puerto Rico. Methods We evaluated samples obtained from 295 participants (including 94 men who provided semen specimens) in whom ZIKV RNA was detected on reverse‐transcriptase–polymerase‐chain‐reaction (RT‐PCR) assay in urine or blood at an enhanced arboviral clinical surveillance site. We collected serum, urine, saliva, semen, and vaginal secretions weekly for the first month and at 2, 4, and 6 months. All specimens were tested by means of RT‐PCR, and serum was tested with the use of anti–ZIKV IgM enzyme‐linked immunosorbent assay. Among the participants with ZIKV RNA in any specimen at week 4, collection continued every 2 weeks thereafter until all specimens tested negative. We used parametric Weibull regression models to estimate the time until the loss of ZIKV RNA detection in each body fluid and reported the findings in medians and 95th percentiles. Results The medians and 95th percentiles for the time until the loss of ZIKV RNA detection were 15 days (95% confidence interval [CI], 14 to 17) and 41 days (95% CI, 37 to 44), respectively, in serum; 11 days (95% CI, 9 to 12) and 34 days (95% CI, 30 to 38) in urine; and 42 days (95% CI, 35 to 50) and 120 days (95% CI, 100 to 139) in semen. Less than 5% of participants had detectable ZIKV RNA in saliva or vaginal secretions. Conclusions The prolonged time until ZIKV RNA clearance in serum in this study may have implications for the diagnosis and prevention of ZIKV infection. In 95% of the men in this study, ZIKV RNA was cleared from semen after approximately 4 months. (Funded by the Centers for Disease Control and Prevention.)
Critical Neuronal Models with Relaxed Timescale Separation

Power laws in nature are considered to be signatures of complexity. The theory of self-organized criticality (SOC) was proposed to explain their origins. A longstanding principle of SOC is the \emph{separation of timescales} axiom. It dictates that external input is delivered to the system at a much slower rate compared to the timescale of internal dynamics. The statistics of neural avalanches in the brain was demonstrated to follow a power law, indicating closeness to a critical state. Moreover, criticality was shown to be a beneficial state for various computations leading to the hypothesis, that the brain is a SOC system. However, for neuronal systems that are constantly bombarded by incoming signals, separation of timescales assumption is unnatural. Recently it was experimentally demonstrated that a proper correction of the avalanche detection algorithm to account for the increased drive during task performance leads to a change of the power-law exponent from $1.5$ to approximately $1.3$, but there is so far no theoretical explanation for this change. Here we investigate the importance of timescales separation, by partly abandoning it in various models. We achieve it by allowing for external input during the avalanche, without compromising the separation of avalanches. We develop an analytic treatment and provide numerical simulations of a simple neuronal model.
A Grid Synchronization PLL Method Based on Mixed Second- and Third-Order Generalized Integrator for DC Offset Elimination and Frequency Adaptability

The second-order generalized integrator (SOGI) has been widely used to implement grid synchronization for grid-connected inverters, and from grid voltages, it is able to extract the fundamental components with an output of two orthogonal sinusoidal signals. However, if there is a dc offset existing in the grid voltages, the general SOGI’s performance suffers from its generated dc effect in the lagging sine signal at the output. Therefore, in this paper, a mixed second- and third-order generalized integrator (MSTOGI) is proposed to eliminate this effect caused by the dc offset of grid voltages. A detailed theoretical analysis on the proposed MSTOGI is presented to reveal the mechanism of eliminating the dc offset. After that, the MSTOGI is applied to a phase-locked loop (PLL) and thereby establish an MSTOGI-PLL which is more adaptable to various grid conditions and power quality. Moreover, a frequency-adaptive control scheme is added to the proposed MSTOGI-PLL to eliminate the phase difference between the PLL output and the grid in grid-connected applications where the grid frequency may vary. Finally, the experimental results from a laboratory prototype are given to demonstrate and verify the effectiveness of the proposed MSTOGI-PLL in terms of steady-state performance, dynamic response, and frequency adaptability.
Domperidone for insufficient lactation in England 2002‐2015: A drug utilization study with interrupted time series analysis

Our aim was to describe trends in the prescription of domperidone for insufficient lactation in England, the characteristics of women prescribed it postpartum, and the impact of a 2014 European Medicines Agency (EMA) recommendation to restrict its use due to a potential increased risk of sudden cardiac death associated with its use.
Deep Learning Approaches for Predictive Masquerade Detection

In computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Convolutional Neural Networks (CNN). In order to surpass previous studies on this subject, we used three UNIX command line-based datasets, with six variant data configurations implemented from them. Furthermore, static and dynamic masquerade detection approaches were utilized in this study. In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. On the other hand, a CNN model is employed in a dynamic approach. Moreover, twelve well-known evaluation metrics are used to assess model performance in each of the data configurations. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper. The results not only show that deep learning models outperform all traditional machine learning methods in the literature but also prove their ability to enhance masquerade detection on the used datasets significantly.
A Normalized Complex LMS Based Blind I/Q Imbalance Compensator for GFDM Receivers and Its Full Second-Order Performance Analysis

Generalized frequency division multiplexing (GFDM) has become one of the most important waveform candidates for air interface in 5G communications. However, like the standard orthogonal frequency division multiplexing (OFDM), its direct-conversion receivers are vulnerable to radio frequency impairments, due to their cost and size constraints. In this paper, the performance deterioration resulting from inphase/quadrature (I/Q) imbalance is first analyzed in terms of signal-to-interference plus noise ratio (SINR) for typical GFDM receivers. Next, a blind adaptive I/Q imbalance compensator based on normalized complex least mean square (NCLMS), originally designed for OFDM receivers, is extended for GFDM ones. In order to provide more physical insight into its compensation capability, a full second-order performance assessment is established, via a joint consideration of the weight error variance and its complementary variance of the proposed compensator in both the transient and steady-state stages. Apart from an accurate evaluation on its overall self-image attenuation performance, the proposed full second-order analysis is also able to quantify the individual contributions from both the I and Q channels of the compensator, an important finding missing in the literature and not possible to discover by using the standard variance analysis only. This analysis also facilitates theoretical quantifications of SINR improvements by NCLMS for GFDM receivers. Simulations on GFDM waveforms support the analysis.
Saturation Effects in Frequency Pulling of Gyrotrons Operating in High-Order Axial Modes

Spectroscopic applications have stimulated strong interest in frequency tunable gyrotrons. One of the possibilities to tune gyrotron frequency is known as frequency pulling, which is based on the effect of the electron beam on the oscillation frequency. Recently, the linear theory of this effect was developed, and the results were obtained for a sequence of modes with different axial indices. This paper is intended to analyze saturation effects in such gyrotrons. The nonlinear theory describing the frequency pulling and the efficiency in gyrotrons is developed in the cold-cavity approximation. The results are obtained for the modes with different axial indices (from 1 to 4). The frequency shift and the efficiency are calculated in a wide range of the dimensionless parameters characterizing the beam current and the external magnetic field. These results related to the frequency pulling are compared with those of the linear theory. Also, comparison is made with the results of the self-consistent theory and experiments.
Rethinking Gamma-secretase Inhibitors for Treatment of Non–small-Cell Lung Cancer: Is Notch the Target?

Lung cancer is the leading cause of cancer-related deaths among men and women. γ-Secretase inhibitors, a class of small-molecule compounds that target the Notch pathway, have been tested to treat non–small-cell lung cancer (NSCLC) in preclinical and clinical trials. Although γ-secretase inhibitors elicit a response in some tumors as single agents and sensitize NSCLC to cytotoxic and targeted therapies, they have not yet been approved for NSCLC therapy. We discuss our recently published preclinical study using the γ-secretase inhibitor AL101, formerly BMS906024, on cell lines and PDX models of NSCLC, primarily lung adenocarcinoma. We propose that Notch pathway mutations may not be the most suitable biomarker for predicting NSCLC response to γ-secretase inhibitors. γ-Secretases have over 100 known γ-secretase cleavage substrates. Many of the γ-secretase substrates are directly involved in carcinogenesis or tumor progression, and are ideal candidates to be the “on-target” biomarkers for γ-secretase inhibitors. We propose the need to systematically test the γ-secretase and other targets as potential biomarkers for sensitivity before continuing clinical trials. Now that we have entered the postgenome/transcriptome era, this goal is easily attainable. Discovery of the biomarker(s) that predict sensitivity to γ-secretase inhibitors would guide selection of the responder population that is most likely to benefit and move the compounds closer to approval for therapeutic use in NSCLC.
Phylogenetic and antimicrobial resistance gene analysis of Salmonella Typhimurium strains isolated in Brazil by whole genome sequencing

Whole genome sequencing (WGS) has been used as a powerful technology for molecular epidemiology, surveillance, identification of species and serotype, identification of the sources of outbreaks, among other purposes. In Brazil, there is relatively few epidemiological data on Salmonella. In this study, 90 Salmonella Typhimurium strains had their genome sequenced to uncover the diversity of Salmonella Typhimurium isolated from humans and food, between 1983 and 2013, from different geographic regions in Brazil based on single nucleotide polymorphism (SNP) analysis. A total of 39 resistance genes were identified, such as aminoglycoside, tetracycline, sulfonamide, trimethoprim, beta-lactam, fluoroquinolone, phenicol and macrolide, as well as the occurrence of point mutations in some of the genes such as gyrA, gyrB, parC and parE. A total of 65 (72.2%) out of 90 S. Typhimurium strains studied were phenotypically resistant to sulfonamides, 44 (48.9%) strains were streptomycin resistant, 27 (30%) strains were resistant to tetracycline, 21 (23.3%) strains were gentamicin resistant, and seven (7.8%) strains were resistant to ceftriaxone. In the gyrA gene, it was observed the following amino acid substitutions: Asp(87)→Gly, Asp(87)→Asn, Ser(83)→Phe, Ser(83)→Tyr. Phylogenetic results placed the 90 S. Typhimurium strains into two major clades suggesting the existence of a prevalent subtype, likely more adapted, among strains isolated from humans, with some diversity in subtypes in foods. The variety and prevalence of resistant genes found in these Salmonella Typhimurium strains reinforces their potential hazard for humans and the risk in foods in Brazil.
Effect of Stimulus Contrast and Visual Attention on Spike-Gamma Phase Relationship in Macaque Primary Visual Cortex

Brain signals often show rhythmic activity in the so-called gamma range (30–80 Hz), whose magnitude and center frequency are modulated by properties of the visual stimulus such as size and contrast, as well as by cognitive processes such as attention. How gamma rhythm can potentially influence cortical processing remains unclear; previous studies have proposed a scheme called phase coding, in which the intensity of the incoming stimulus is coded in the position of the spike relative to the rhythm. Using chronically implanted microelectrode arrays in the primary visual cortex (area V1) of macaques engaged in an attention task while presenting stimuli of varying contrasts, we tested whether the phase of the gamma rhythm relative to spikes varied as a function of stimulus contrast and attentional state. A previous study had found no evidence of gamma phase coding for either contrast or attention in V1, but in that study spikes and local field potential (LFP) were recorded from the same electrode, due to which spike-gamma phase estimation could have been biased. Further, the filtering operation to obtain LFP could also have biased the gamma phase. By analyzing spikes and LFP from different electrodes, we found a weak but significant effect of attention, but not stimulus contrast, on gamma phase relative to spikes. The results remained consistent even after correcting the filter induced lags, although the absolute magnitude of gamma phase shifted by up to ~15°. Although we found a significant effect of attention, we argue that a small magnitude of phase shift as well as the dependence of phase angles on gamma power and center frequency limits a potential role of gamma in phase coding in V1.
The prognostic value of red blood cell characteristics on diabetic retinopathy development and progression in type 2 diabetes mellitus.

BACKGROUNDAlthough it is considered that the pathogenesis of diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) is primarily due to chronic hyperglycemia resulting in vascular changes and retinal ischemia, the red blood cells (RBCs) disorders might also represent an important pathophysiological risk factor.OBJECTIVETo evaluate whether the RBC properties contribute to DR development and progression in T2DM.METHODSThis prospective observational study comprised 247 persons with T2DM free of DR or with non proliferative DR without any signs of anaemia. The patients were reacessed after 60-months.RESULTSThe mean age of our study population was 56 years, 54.9% males with diabetes duration of 11,18±1,28 years. During the follow up, 16 (5.84% ) participants developed non proliferative DR and 9 (3.64% ) progressed to PDR while the mean corpuscular volume (MCV) and red cell distribution width (RDW) MCV rose. Both MCV and RDW correlated positively with HbA1c (r = 0,468, p = 0.003 and r = 0.521, p < 0.001), while Cox regression analysis revealed that besides age, diabetes duration, HbA1c, hypertension and dyslipidemia presence, MCV and RDW are also associated with the risk of DR development and progression (HR 1.057 and 1.237, p < 0.001). CONCLUSIONSWe clearly demonstrated that RBC's characteristics might represent a risk factor for DR development and progression.