Miracle-dz/UniFAR-FTUs huggingface dataset:
Biases in Data Science Lifecycle

In recent years, data science has become an indispensable part of our society. Over time, we have become reliant on this technology because of its opportunity to gain value and new insights from data in any field - business, socializing, research and society. At the same time, it raises questions about how justified we are in placing our trust in these technologies. There is a risk that such powers may lead to biased, inappropriate or unintended actions. Therefore, ethical considerations which might occur as the result of data science practices should be carefully considered and these potential problems should be identified during the data science lifecycle and mitigated if possible. However, a typical data scientist has not enough knowledge for identifying these challenges and it is not always possible to include an ethics expert during data science production. The aim of this study is to provide a practical guideline to data scientists and increase their awareness. In this work, we reviewed different sources of biases and grouped them under different stages of the data science lifecycle. The work is still under progress. The aim of early publishing is to collect community feedback and improve the curated knowledge base for bias types and solutions.
Improving Robustness and Generality of NLP Models Using Disentangled Representations

Supervised neural networks, which first map an input $x$ to a single representation $z$, and then map $z$ to the output label $y$, have achieved remarkable success in a wide range of natural language processing (NLP) tasks. Despite their success, neural models lack for both robustness and generality: small perturbations to inputs can result in absolutely different outputs; the performance of a model trained on one domain drops drastically when tested on another domain. In this paper, we present methods to improve robustness and generality of NLP models from the standpoint of disentangled representation learning. Instead of mapping $x$ to a single representation $z$, the proposed strategy maps $x$ to a set of representations $\{z_1,z_2,. ,z_K\}$ while forcing them to be disentangled. These representations are then mapped to different logits $l$s, the ensemble of which is used to make the final prediction $y$. We propose different methods to incorporate this idea into currently widely-used models, including adding an $L$2 regularizer on $z$s or adding Total Correlation (TC) under the framework of variational information bottleneck (VIB). We show that models trained with the proposed criteria provide better robustness and domain adaptation ability in a wide range of supervised learning tasks.
Can questions summarize a corpus? Using question generation for characterizing COVID-19 research

What are the latent questions on some textual data? In this work, we investigate using question generation models for exploring a collection of documents. Our method, dubbed corpus2question, consists of applying a pre-trained question generation model over a corpus and aggregating the resulting questions by frequency and time. This technique is an alternative to methods such as topic modelling and word cloud for summarizing large amounts of textual data. Results show that applying corpus2question on a corpus of scientific articles related to COVID-19 yields relevant questions about the topic. The most frequent questions are "what is covid 19" and "what is the treatment for covid". Among the 1000 most frequent questions are "what is the threshold for herd immunity" and "what is the role of ace2 in viral entry". We show that the proposed method generated similar questions for 13 of the 27 expert-made questions from the CovidQA question answering dataset. The code to reproduce our experiments and the generated questions are available at: this https URL
Remote sensing image fusion based on Bayesian GAN

Remote sensing image fusion technology (pan-sharpening) is an important means to improve the information capacity of remote sensing images. Inspired by the efficient arameter space posteriori sampling of Bayesian neural networks, in this paper we propose a Bayesian Generative Adversarial Network based on Preconditioned Stochastic Gradient Langevin Dynamics (PGSLD-BGAN) to improve pan-sharpening tasks. Unlike many traditional generative models that consider only one optimal solution (might be locally optimal), the proposed PGSLD-BGAN performs Bayesian inference on the network parameters, and explore the generator posteriori distribution, which assists selecting the appropriate generator parameters. First, we build a two-stream generator network with PAN and MS images as input, which consists of three parts: feature extraction, feature fusion and image reconstruction. Then, we leverage Markov discriminator to enhance the ability of generator to reconstruct the fusion image, so that the result image can retain more details. Finally, introducing Preconditioned Stochastic Gradient Langevin Dynamics policy, we perform Bayesian inference on the generator network. Experiments on QuickBird and WorldView datasets show that the model proposed in this paper can effectively fuse PAN and MS images, and be competitive with even superior to state of the arts in terms of subjective and objective metrics.
What is an intelligent system?

The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this paper is to give a general description of an intelligent system, which integrates previous approaches and takes into account recent advances in artificial intelligence. The paper describes an intelligent system in a generic way, identifying its main properties and functional components, and presents some common categories. The presented description follows a practical approach to be used by system engineers. Its generality and its use is illustrated with real-world system examples and related with artificial intelligence methods.
Combined approach for automatic and robust calculation of dominant frequency of electrogastrogram

We present a novel method for automatic and robust detection of dominant frequency (DF) in the electrogastrogram (EGG). Our new approach combines Fast Fourier Transform (FFT), Welch's method for spectral density estimation, and autocorrelation. The proposed combined method as well as other separate procedures were tested on a freely available dataset consisted of EGG recordings in 20 healthy individuals. DF was calculated in relation (1) to the fasting and postprandial states, (2) to the three recording locations, and (3) to the subjects' body mass index. For the estimation of algorithms performance in the presence of noise, we created a synthetic dataset by adding white Gaussian noise to the artifact-free EGG waveform in one subject. The individual algorithms and novel combined approach were evaluated in relation to the signal-to-noise ratio (SNR) in range from -40 dB to 20 dB. Our results showed that the novel combined method significantly outperformed the commonly used approach for DF calculation - FFT in noise presence when compared to the benchmark data being was manually corrected by an expert. The novel method outperformed autocorrelation and Welch's method in accuracy. Additionally, we presented a method for optimal window width selection when using Welch's spectrogram that showed that for DF detection, window length of N/4 (300 s), where N is the length of EGG waveform in samples, performed the best when compared to the benchmark data. The combined approach proved efficient for automatic and robust calculation of dominant frequency on openly available EGG dataset recorded in healthy individuals and is promising approach for DF detection.
First Prototype of the Infectious Diseases Seeker (IDS) Software for Prompt Identification of Infectious Diseases

The rapid detection of ongoing outbreak – and the identification of causative pathogen – is pivotal for the early recognition of public health threats. The emergence and re-emergence of infectious diseases are linked to several determinants, both human factors – such as population density, travel, and trade – and ecological factors – like climate change and agricultural practices. Several technologies are available for the rapid molecular identification of pathogens [e.g. real-time polymerase chain reaction (PCR)], and together with on line monitoring tools of infectious disease activity and behaviour, they contribute to the surveillance system for infectious diseases. Web-based surveillance tools, infectious diseases modelling and epidemic intelligence methods represent crucial components for timely outbreak detection and rapid risk assessment. The study aims to integrate the current prevention and control system with a prediction tool for infectious diseases, based on regression analysis, to support decision makers, health care workers, and first responders to quickly and properly recognise an outbreak. This study has the intention to develop an infectious disease regressive prediction tool working with an off-line database built with specific epidemiological parameters of a set of infectious diseases of high consequences. The tool has been developed as a first prototype of a software solution called Infectious Diseases Seeker (IDS) and it had been established in two main steps, the database building stage and the software implementation stage (MATLAB® environment). The IDS has been tested with the epidemiological data of three outbreaks occurred recently: severe acute respiratory syndrome epidemic in China (2002–2003), plague outbreak in Madagascar (2017) and the Ebola virus disease outbreak in the Democratic Republic of Congo (2018). The outcomes are promising and they reveal that the software has been able to recognize and characterize these outbreaks. The future perspective about this software regards the developing of that tool as a useful and user-friendly predictive tool appropriate for first responders, health care workers, and public health decision makers to help them in predicting, assessing and contrasting outbreaks.
Quantitative Evaluation of Forces and Jacobians for Real-Time Garment Simulation with Semi-Implicit Euler Integration and Hardware Assisted Vector Calculation

Real-time cloth simulation is a key factor in 3D garment computer-aided-design (CAD) system. This paper tries to provide a quantitative analysis and optimal selection of forces and Jacobian terms for particle-based garment simulation. Particle based method of Baraff and Witkin (1998) was optimized using the selected force and Jacobian terms. And sub-modules comprising the whole system, including collision detection/response step, linear matrix system solving step, force/Jacobian calculation step, and rendering, were analyzed in detail to find the time bottle neck in realizing real-time simulation. Hardware assisted acceleration techniques, like multi-threading, SSE (Streaming SIMD Extensions), and openMP (Open Multi-Processing) as well as data vectorization, were also tested to find the possibility of real-time cloth simulation in cloud computing environment.
Spatial-temporal reasoning in construction scenes using advanced directional relations *

Visual spatial-temporal (4D) modeling technologies play an important role in the realization of complex construction projects and programs facing the problems of trustworthy planning, advanced communication among stakeholders, better utilization of critical resources and effective coordination of works. Popular commercial 4D modeling systems provide basic functionality to simulate project activities in space dimensions and across time, but are still limited in validation of project schedules against possible spatial-temporal conflicts, particularly, in identification of such situations as missing supporting neighbouring elements and unsteady element placement. In this paper an approach is proposed to validate construction project schedules based on spatial-temporal reasoning formalisms and methods. We provide a brief overview of currently available qualitative spatial reasoning calculi and argue the need of advanced directional relations. Three sorts of advanced directional relations have been proposed and discussed in conformity to the declared validation problems and developed methods. As expected, their introduction into industry would enable anticipation and avoidance of potential problems at earlier planning phases and would allow reducing risks and waste at final construction phases often undergone delays and reworks.
Evaluating Immune Dysregulation in Patients With COVID-19 Requires a More Accurate Definition of the CD45RA+ T-cell Phenotype

Older children and adolescents ranged from 1 year to 21 years of age. Mean NP viral load was significantly higher in infants as compared with older children and adolescents (mean Ct, 21.05 vs 27.25; P < .01) (Figure 1). However, a significantly lower proportion of infants had severe disease as compared with the older patients (n = 1 [5%] vs n = 12 [32.4%]; P = .02). Mean time to test positivity from symptom onset was lower in infants than older children (2 vs 3.8 days, P < .01). Similar proportions in both groups were tested within 7 days of symptom onset (91.2% vs 100%, P = .47). Our report suggests that symptomatic infants have higher NP viral loads at presentation but develop less severe disease as compared with older children and adolescents. Whether this is attributable to slightly earlier presentation to clinical care versus host biology requires investigation. These data have implications for mitigating spread, especially in congregate settings (eg, child care centers) or hospital units (eg, neonatal ICUs) that serve this group.
A Sphingosine-1-Phosphate Receptor Modulator Attenuated Secondary Brain Injury and Improved Neurological Functions of Mice after ICH

Background Stroke activates the immune system and induces brain infiltration by immune cells, aggravating brain injury. Poststroke immunomodulation via (S1P-)receptor modulation is beneficial; however, the S1P-modulator in clinical use (FTY-720) is unspecific, and undesirable side effects have been reported. Previously, we tested effects of a novel selective S1P-receptor modulator, Siponimod, on ICH-induced brain injury in acute stage of the disease. In the current study, we investigated whether protective effects of Siponimod, evaluated in a short-term study, will protect the brain of ICH animals at long term as well. Methods 134 C57BL/6N mice were divided into sham and ICH-operated groups. Collagenase model of ICH was employed. ICH animals were divided into Siponimod treated and nontreated. Dose- and time-dependent effects of Siponimod were investigated. Contraplay between development of brain injury and the number of lymphocytes infiltrating the brain was investigated by forelimb placing, T-Maze test, brain water content calculation, MRI scanning, and immunostaining. Results Depending on the therapeutic strategy, Siponimod attenuated the development of brain edema, decreased ICH-induced ventriculomegaly and improved neurological functions of animals after ICH. It was associated with less lymphocytes in the brain of ICH animals. Conclusion Siponimod is able to decrease the brain injury and improves neurological functions of animals after ICH.
Hair Washing Formulations from Aloe elegans Todaro Gel: The Potential for Making Hair Shampoo

This study aimed to describe the gross phytochemical constituents of Aloe elegans Todaro gel and evaluate the characteristics and quality of lab-made hair washing formulations prepared from the gel to show its potential in formulating hair washing shampoos. A. elegans gel mass was prepared from mature, healthy leaves collected from natural stand. Samples of 100% methanol extract of the gel were subjected to standard phytochemical screening and gas chromatography-mass spectroscopy (GC-MS) analysis. Five hair washing formulations (Fs) were, likewise, prepared by mixing 4.0–10.0 mL of gel with one (0.05 mL) to two (0.10 mL) drops of six synthetic and natural ingredients, namely, coconut oil, jojoba oil, olive oil, pure glycerin oil, lemon juice, and vitamin E. The gel to the total ingredient ratios (v/v) of the five formulations were 93 : 7 (F1), 94.5 : 5.5 (F2), 96.4 : 3.6 (F3), and 96.6 : 3.4 (F4 and F5). The formulations were evaluated using sensory inspection and common physicochemical methods. The phytochemical screening and GC-MS analysis revealed that A. elegans gel is the source of important chemical constituents used in the formulation of shampoos and similar products including saponins, capric acid, lauric acid, myristic acid, palmitic acid, linoleic acid, stearic acid, and phytol. Lab-made A. elegans hair washing formulations, especially those with 96.4–96.6% gel, were found to have similar characteristics and qualities with a common marketed shampoo. All the formulations were turbid with characteristic odor as the marketed shampoo. The pH values of the hair washing formulations (6.4–4.6) were comparable to those of the marketed shampoo (6.7). Formulations with higher proportion of gel had better foam stability, higher solid content (26–29%), higher surface tension (33–38 dynes/cm), shorter wetting time (150–160 sec), equivalent viscosities (26.45–26.73 poise), and conditioning performance than the marketed shampoo. These findings demonstrate that A. elegans gel mass can be used in the formulation of good-quality hair washing shampoos. We recommend future studies that aim to develop the phytochemical profile of the plant and a refined protocol of hair washing shampoo formulation.
Predaceous Fire Ants (Hymenoptera: Formicidae) at Sea Turtle (Testudines: Cheloniidae) Nesting Beaches and Hatcheries in El Salvador

Abstract As in many other parts of the world, in El Salvador, few sea turtle (Testudines: Cheloniidae) eggs develop and hatch in situ on nesting beaches. Instead, conservationists relocate most sea turtle eggs to hatcheries for protection. Hatchery managers incubate the eggs in artificial nests within protected enclosures and then release the hatchling sea turtles into the ocean. We surveyed ants (Hymenoptera: Formicidae) on 2 sea turtle nesting beaches and at 14 sea turtle hatchery sites in El Salvador to evaluate the potential threat of predaceous ant species to sea turtle eggs and hatchlings. Of the ant species we found, only the tropical fire ant, Solenopsis geminata (F.) (Hymenoptera: Formicidae), is a known threat to sea turtle hatchlings. We found S. geminata at 5 of 7 (71%) and 7 of 30 (23%) baits along sea turtle nesting beaches at Las Bocanitas and Las Isletas, respectively, and within the nest enclosures at 7 of 14 (50%) hatchery sites. Given the widespread use of hatcheries for protecting sea turtle eggs worldwide, we believe it is important for hatchery managers to recognize the potential threat that predaceous ants pose to hatchling sea turtles. Hatchery managers may be unknowingly releasing apparently healthy but stung hatchlings to the ocean, only to have the hatchlings soon die from sting-related impairment. Fortunately, because of the small size of the incubation enclosures, controlling ants at hatcheries by using chemicals that have low toxicity to vertebrates and that degrade quickly (e.g., hydramethylnon) should be safe, simple, and relatively inexpensive.
Shrec: bandwidth-efficient transaction relay in high-throughput blockchain systems

The success of Bitcoin and Ethereum has attracted many efforts to build high-throughput blockchain systems. This paper focuses on transaction dissemination --- a rather overlooked issue in these systems. We argue that efficient transaction dissemination is the key for a blockchain system to sustain at high-throughput --- usually thousands of transactions per second --- and the existing solutions fell short at doing so. This paper presents Shrec, a novel transaction relay protocol for high-throughput blockchain systems built around a hybrid transaction hashing scheme that has a low hash collision rate, is resilient to collision attacks, and is fast to construct. Our experiments demonstrate that when propagating transactions, Shrec utilizes network efficiently: compared to alternative designs, Shrec reduces the bandwidth consumption by 60% at modest CPU overhead and improves the system throughput by up to 90%.
Management of pregnancy-related hypertensive disorders in patients infected with SARS CoV-2: pharmacological and clinical issues

Abstract Aims Coronavirus-19 disease (COVID-19) continues to spread throughout the world. It is known that among patients with hypertension, diabetes, chronic respiratory disease, or cardiovascular diseases, COVID-19 is associated with greater morbidity and mortality compared with patients without these conditions. This correlation is of great importance in pregnant women affected by COVID-19, since it usually leads to the development of a serious clinical complication. In particular, managing hypertensive disorders in pregnancy can be problematic because antihypertensive medications may interact pharmacologically with drugs used to treat COVID-19. This review focuses on the safety of drug treatment for COVID-19 in pregnant women treated with antihypertensive medication. Methods and results Several databases were searched to identify relevant literature. A few antihypertensive drugs and antithrombotic treatments are known for having a beneficial effect in the management of hypertension and hypertensive disorders in pregnancy. In this review, we focus on the expected drug–drug interactions with the experimental agents most often used to treat COVID-19. Conclusions The current indications for the management of hypertension-related disorders in pregnancy maintain their validity, while the risk of pharmacological interaction with the currently tested anti-SARS-CoV-2 medications is relatively low.