Miracle-dz/UniFAR-FTUs huggingface dataset:
Water, sanitation, and hygiene (WASH) in healthcare facilities of 14 low- and middle-income countries: to what extent is WASH implemented and what are the ‘drivers’ of improvement in their service levels?

Prevention and control of healthcare-associated infections through the provision of water, sanitation, and hygiene (WASH) in healthcare facilities (HCF) is inadequate in low- and middle-income countries (LMICs), resulting in high patient morbidity and mortality, additional costs, and increased risk of antibiotic resistance. There is little evidence describing factors leading to improved WASH conditions in LMICs. We aim to identify the extent to which WASH is implemented in HCFs in LMICs and understand the ‘drivers’ of improvement in their service levels. WASH service levels in 14 LMICs were descriptively analysed, and potential drivers of service-level differences were explored using univariable and multivariable mixed-model logistic regression analyses. Descriptive analysis showed a lack of adequate water quality, sanitation, hand, and environmental hygiene, and waste disposal. We found that the presence of infection prevention and control protocols (IPCPs), having an IPC/WASH focal person at the facility, and conducting WASH training for staff were associated with higher levels of WASH services. This study demonstrates a lack of basic WASH services in HCF in LMICs. We show that there are potential interventions, such as implementing IPCPs, identifying WASH leaders in HCF, and conducting training that may lead to service improvements.
Multilingual Turkic Thesaurus as Education Support Tool

This paper presents integral linguistic model of the frame ontology and “Turkic Morpheme” linguistic portal, which is built on the basis of this model. The integrality of the model is based on fact that it combines two models of well-known linguistic resources namely WordNet and FrameNet. These models are expanded in the way of taking into account the structural and functional features of Turkic languages. The resources presented in the paper have two use cases for educational process. On the one hand, the portal database is a rich informational and reference material for the study of Turkic languages, and on the other hand, the ontology itself and the portal are used to teach semantic frames and ontologies in disciplines “Computational linguistics” and “Knowledge processing”.
Arbitrary Verification of Ontology Increments using Natural Language

Parallel to the advancement of practical use cases in computers, the trend toward collaborative ontology engineering is accelerating. Both domain experts and ontologists must collaborate in collaborative ontology engineering processes. However, the bulk of domain experts are not computer experts (i.e. lawyers, medical doctors, bankers, etc.). Question and Answer on Linked Data (QALD) is a suggested method for noncomputer domain experts to engage with the ontology increments as they evolve. Existing QALD methods and systems, on the other hand, have a number of drawbacks, including significant setup requirements, domain dependence, and user discomfort. As a result, a new QALD algorithm and QALD system designed with the usage of First Order Logic (FOL) are presented in order to address the shortcomings of current QALD mechanisms. The suggested FOL based, QALD mechanism was tested quantitatively and qualitatively over three distinct ontology increments. This experiment had an overall acceptance rate of 79 percent from all stakeholders. Keywords—First order logic; linked data; ontologist; iterative framework
Cloud-Scale Runtime Verification of Serverless Applications

Serverless platforms aim to simplify the deployment, scaling, and management of cloud applications. Serverless applications are inherently distributed, and are executed using shortlived ephemeral processes. The use of short-lived ephemeral processes simplifies application scaling and management, but also means that existing approaches to monitoring distributed systems and detecting bugs cannot be applied to serverless applications. In this paper we propose Watchtower, a framework that enables runtime monitoring of serverless applications. Watchtower takes program properties as inputs, and can detect cases where applications violate these properties. We design Watchtower to minimize application changes, and to scale at the same rate as the application. We achieve the former by instrumenting libraries rather than application code, and the latter by structuring Watchtower as a serverless application. Once a bug is found, developers can use the Watchtower debugger to identify and address the root cause of the bug.
A ROBUST CLOUD RESOURCE PROVISIONING UNDER DEMAND UNCERTAINTY

— Cloud registration arrangements usually deliver two kinds of usage plans: saved and on-request. Saved plans provide less expensive assets for long haul contracts while on-request plans are accessible for short or small stretches and are increasingly costly. Most current works focus on either less costly arrangements with saved assets that could result in under-supply or oversupply, or exorbitant arrangements with on-demand assets. Since wastefulness of distributing cloud assets in cloud request can cause tremendous provisioning expenses and vacillation, asset allocation becomes a profound testing problem. A half and half method of spreading cloud assets as demonstrated by the complex client requests is proposed in this proposed work. This method is generated as a two-stage calculation which includes stage of reservation and dynamic arrangement. Here, it is restricted to the aggregate costs of sending by identifying each stage as a question of enhancement thus serving the essence of administration. A stochastic advancement approach is built up by demonstrating client requests as irregular variables because of the unsure existence of cloud requests. The estimate is tested using different optimization, and show its effectiveness in slowly designating cloud properties.
Deep Grid Scheduler for 5G NB-IoT Uplink Transmission

Since the birth of narrowband Internet of Things (NB-IoT), the Internet of Things (IoT) industry has made a considerable progress in the application for smart cities, smart manufacturing, and healthcare. Therefore, the number of UEs is increasing exponentially, which brings considerable pressure to the efficient resource allocation for the bandwidth and power constrained NB-IoT networks. In view of the conventional algorithms that cannot dynamically adjust resource allocation, resulting in a low resource utilization and prone to resource fragmentation, this paper proposes a double deep Q-network (DDQN)-based NB-IoT dynamic resource allocation algorithm. It first builds an NB-IoT environment model based on the real environment. Then, the DDQN algorithm interacts with the NB-IoT environment model to learn and optimize resource allocation strategies until it converges to the optimum. Finally, the simulation results show that the DDQN-based NB-IoT dynamic resource allocation algorithm is better than the traditional algorithm in the resource utilization, average transmission rate, and UE average queuing time.
This is not the End: Rethinking Serverless Function Termination

Elastic scaling is one of the central benefits provided by serverless platforms, and requires that they scale resource up and down in response to changing workloads. Serverless platforms scale-down resources by terminating previously launched instances (which are containers or processes). The serverless programming model ensures that terminating instances is safe assuming all application code running on the instance has either completed or timed out. Safety thus depends on the serverless platform’s correctly determining that application processing is complete. In this paper, we start with the observation that current serverless platforms do not account for pending asynchronous I/O operations when determining whether application processing is complete. These platforms are thus unsafe when executing programs that use asynchronous I/O, and incorrectly deciding that application processing has terminated can result in data inconsistency when these platforms are used. We show that the reason for this problem is that current serverless semantics couple termination and response generation in serverless applications. We address this problem by proposing an extension to current semantics that decouples response generation and termination, and demonstrate the efficacy and benefits of our proposal by extending OpenWhisk, an open source serverless platform.
Lessons Learned From the Implementation of a Participant-Collected, Mail-Based SARS-CoV-2 Serological Survey in Massachusetts, USA

The rapid spread of SARS-CoV-2 is largely driven by pre-symptomatic or mildly symptomatic individuals who transmit the virus. Serological tests to identify antibodies against SARS-CoV-2 are an important tool to characterize subclinical infection exposure, which is critical in determining transmission trajectories and consequent population immunity. During the summer of 2020, a mail-based serological survey with self-collected dried blood spot (DBS) samples was implemented among university affiliates and their household members in Massachusetts, USA. Described here are some of the challenges faced and novel procedures used during the implementation of this study to assess the prevalence of SARS-CoV-2 antibodies amid the global pandemic. Important challenges included remote and contact-minimized participant recruitment, limited availability of commodities and laboratory capacity, a potentially biased sample population, and policy changes impacting the distribution of clinical results to study participants. Methods used to surmount these challenges and lessons learned are presented to inform similar studies. Key lessons relate to the acceptability and feasibility of DBS sampling, supply requirements, the logistics of packing and shipping packages, data linkages to enrolled household members, and the utility of having an on-call nurse available for participant concerns during sample collection. Future studies might consider additional recruitment techniques such as conducting studies during academic semesters when recruiting in a university setting, partnerships with supply and shipping specialists, and using a stratified sampling approach to minimize potential biases in recruitment. This study design highlights the feasibility and acceptability of self-collected bio-samples and has broad applicability for other serological surveys for a range of pathogens.
Quality Prediction of Scientific Documents Using Textual and Visual Content

i Acknowledgements ii 1 I N T R O D U C T I O N 2 2 T H E O R E T I C A L B AC K G R O U N D 4 2.1 Machine Learning . 4 2.1. 1 Supervised Learning . 4 2.1. 2 Unsupervised Learning . 4 2.1. 3 Transfer Learning . 5 2.1. 4 Multi-task Learning . 5 2.2 Artificial Neural Networks . 7 2.2.1 Perceptrons . 8 2.2.2 Activation Functions . 9 2.2.3 Loss Functions . 11 2.2.4 Optimization . 12 2.2. 5 Convolutional Neural Networks . 13 2.2. 6 Recurrent Neural Networks . 15 2.3 Natural Language Processing . 17 2.3.1 Word Representations . 17 2.3.2 Encoder/Decoder . 18 2.3.3 Attention . 19 2.3.4 Transformers . 21 2.3.5 BERT . 22 2.4 Related Work . 25 3 M E T H O D S 27 3.1 Datasets . 27 3.1. 1 PeerRead Dataset . 27 3.1. 2 ACL-BiblioMetry Dataset . 28 3.1. 3 AAPR Dataset . 28 3.2 Citation Labels . 28 3.3 Evaluation Metrics . 29 3.3.1 Accept/reject Prediction . 29 3.3.2 Citation Prediction . 29 3.4 Textual . 30 3.4. 1 Textual Baseline Models . 30 3.4. 2 SChuBERT . 30 3.5 Visual . 32 3.5. 1 Building the dataset . 32 3.5. 2 Visual Baseline Models . 33 3.5. 3 INCEPTIONGU . 34
Plant Archives

Colonization and growth of cyanobacteria on exposed surfaces of stone-made temples and monuments result in their biodeterioration in form of both aesthetic and structural damage. This study investigates the epilithic cyanobacterial colonization of exteriors of 11 different ancient stone temples, showing varying degree of deterioration, located in Garhwal region of Uttarakhand. Cyanobacteria-dominated biofilms/ crusts occurring on the exteriors of the temples were sampled and analyzed. The intensity of cyanobacterial colonization of temples, as assessed by quantifying the chlorophyll a (Chl a ) content of biomass of phototrophic biofilms/crusts developed on wall surfaces of temples, ranged from 6.8 µg Chl a cm -2 to 14.5 µg Chl a cm -2 . A total of 22 epilithic cyanobacterial species (17 genera) representing unicellular, colonial and filamentous (heterocystous and non-heterocystous) forms were isolated from epilithic biofilms/crusts and identified. The results showed that epilithic cyanobacteria dwelling on investigated temples have ample diversity.Among the recorded taxa, the commonly occurring and widely distributed taxa with percentage occurrence of 50% and above comprised Asterocapsa sp. , Gloeocapsaatrata , Gloeocapsa sp.
Open Text Ontology Mining to Improve Retrievals of Information

Information retrieval is the main task to extract relevant information from documents. Mostly, the information retrieval system is based on the keyword approach to extract the knowledge of relevant documents. The experiment shows the ontology can improve the result to overcome the weakness of keyword approach. Ontology implementation method is based on phrase formation and semantic relationships between words. This study tested 10 Malay documents using ontology to retrieve information. The results obtained were compared with the result obtained from manual information retrieval done by experts for precision and recall measure. In this study, there are three semantic relationships between words that are capable of expressing knowledge in documents. They are taxonomy relationship, attribute relationship and non-taxonomy relationship. The relationship of ontology can be formed by using taxonomy relationships algorithm, attribute relationships algorithm and non-taxonomy relationships algorithm based on the linguistic rules of the Malay language. The result of precision and recall for this experiment shows that the ontology approach can enhance the performance of information retrieval from the
Primary school pupils’ images of mathematics

Images of mathematics are one of the critical elements of students’ knowledge in mathematics learning. The knowledge is formed through students’ various activities and experiences throughout their lives, including mathematics education events. This study examines the images of mathematics held by three primary school pupils of one small town located on the outskirt of Pahang. Data were collected through unstructured interview sessions and written question forms. One Year 4 male pupil, one Year 5 male pupil, and a Year 6 female pupil were selected using the convenience sampling method. Several matters related to the images of mathematics learning and metaphors related to mathematics are discussed in this paper.
A pneumatically controlled microfluidic rectifier enabling zero backflow under pulsatile flow regime

A pulsatile microfluidic pump transports discrete volume to specific target locations. However, backflow in the pulsatile microfluidic pump often decreases the pumping efficiency and causes unwanted sample mixing due to unsteady boundaries. Employing the concept of a transistor, we developed a pneumatically controlled microfluidic rectifier (PCMR) that prevents backflow in the pulsatile flow regime. This PCMR was interconnected with a microfluidic pulsatile pumping system to characterize the functions of PCMR under continuous pumping sequences (CPS) and discrete pumping sequences (DPS) with various gate pressures on the PCMR. In the passive mode (zero gate pressure), 1.07% backflow for a DPS and 0.12% for a CPS were achieved. However, by adjusting the gate pressure on PCMR actively, we were able to achieve zero backflow for both pumping sequences at the cost of pumping efficiency. Interestingly, when the flow passes through the PCMR under the controlled gate-pressure, a fluidic oscillation was observed due to the force balance between the fluidic pressure and the gate-pressure. This microfluidic system successfully demonstrated half-rectification from various pulsatile flow profiles. By incorporating multiple three-microvalve systems in parallel with frequency modulation, a fully rectified flow pattern can be realized to enable steady laminar flow from pulsatile micropumps.
Assessing rice production efficiency of the granary and non-granary areas in Malaysia using data envelopment analysis approach

The performance of rice production efficiency between two homogenous regions (granaries and non-granaries) at the regional level in Malaysia is evaluated by applying the data envelopment analysis (DEA) approach. In general, the granary area receives much attention to achieve sustainable rice development compared to non-granaries. The period observed from 2009 to 2018 related to the comprehensive government transformation in strengthening paddy and rice value-chain after the 2007-2008 international food crisis. Under the new National Agrofood Policy (NAFP) 2011-2020, more agricultural assistance and development expenditure was allotted. The total of eighteen regions was classified into eight granaries and ten regions for non-granaries. The output-input oriented analysis under CRS and VRS specifications were performed. From CRS and VRS technical efficiency scores ratio, the scale efficiencies were calculated. The result shows that eight (44.44%) out of eighteen regions maintain high efficiency with scores greater than 0.9000 for both specifications. Meanwhile, under ‘pure’ technical efficiency (VRS), eleven (61.11%) out of eighteen regions maintain high efficiency with scores greater than 0.9000. For productivity status, twelve (66.67%) out of eighteen areas are optimum and increasing productivity approach and the remaining six (33.33%) are decreasing productivity. This empirical study also demonstrates substantial performance variations between output (rice production) and production factors (land, planting frequency, irrigation areas, total subsidies and incentives received). Overall, the Malaysia paddy and rice industry’s efficiency performance can be considered still inefficient but has the potential to maximize the production through rationalizing the inputs used and obtain production efficiency, especially the inefficient regions.
Robust Hotelling’s T2 statistic based on M-estimator

Hotelling’s T 2 statistic is the multivariate generalization of the student’s t-statistic. Hotelling’s T 2 statistics is a method for testing hypotheses about multidimensional means. However, the classical Hotelling’s T 2 statistic is very sensitive to the presence of outliers. In order to overcome this limitation, a modification is needed so that Hotelling’s T 2 is robust. In this paper, classical Hotelling’s T 2 statistic has been modified by substituting mean vector and covariance matrix with a robust estimator. M-estimator has been used for this modification. The performance of modified Hotelling’s T 2 statistic has been compared with the classical Hotelling’s T 2 statistic and discussed in this paper to illustrate the advantage of modified Hotelling’s T 2 statistic towards outliers. The performance of modified Hotelling’s T 2 statistic is better than classical Hotelling’s T 2 when number of sample, n and dimension, p is small.
New Class of 2-Partition Poisson Quadratic Stochastic Operators on Countable State Space

The idea of quadratic stochastic operator (QSO) which was originally introduced by Bernstein in the early 20th century through his work on population genetics has been significantly developed for decades to describe dynamical systems in many areas. In this research we construct the dynamical systems generated by a new class of 2-partition of Poisson QSO defined on countable state space, X = {0,1,2,…}. Our main goal is to investigate the trajectory behavior of such operators by reducing its infinite variables into a one-dimensional setting that correspond to the number of defined partitions. We present some cases of 2-measurable partition with singleton and two points of two different parameters. Measure and probability theory alongside the functional analysis will be applied to investigate the limit behavior and characteristics of fixed points. These results suggest that the QSO generated by a 2-measurable partition defined on countable state space for both singleton and two points of two different parameters is a regular transformation for some values of parameters.
Analysis of the Tools to Support Systematic Literature Review in Software Engineering

A thorough and accurate review of the literature is a necessary condition for relevant research. However, the literature review is a laborious, error-prone process. For this reason, various software tools are being developed to support researchers in conducting literature reviews. In this paper, three tools to support a systematic literature review in software engineering were analysed and evaluated using the DESMET methodology. Tools were found by conducting mapping study and were selected since they were most promising to support an overall systematic literature review process. Although the tools do not yet support the whole systematic review process on a high level, they provide a reasonable basis for further development. It is needed to set features that need to be supported by the tools.
Priors comparison in Bayesian Models of risk factor of Malaysian coronary artery disease male patients

Coronary artery disease (CAD) continues to be one of the leading causes of morbidity and mortality globally. Of particular relevance for this issue is that major efforts should be focused on understanding the risk factor involved. In this study, three types of Bayesian models, each with different prior distribution were considered to identify associated risk factors in CAD among Malaysian male patients presenting with ST-Elevation Myocardial Infarction (STEMI) and to obtain a feasible model to fit the data. The results of the three models were compared to find the best model. A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Univariate and multivariate analyses for the three models were performed using one of the Bayesian Markov Chain Monte Carlo (MCMC) simulation approach known as Gibbs sampling. Models’ performances are evaluated through overall model fit. Bayesian model C which used both Beta and Dirichlet prior distributions, consisted of six significant variables namely diabetes mellitus, family history of cardiovascular disease, chronic lung disease, renal disease, Killip class and age group was considered as the best model. The same set of variables that were observed to be significant in the Bayesian model C was also found to be significant in models A and B which used single prior distribution, respectively. Model C has a better fit than models A and B as the deviance value produced was the smallest. This study showed that posterior estimation was mostly influenced by the existing prior knowledge. Though applying the non-informative prior which were both Beta and Dirichlet distribution priors, model C can minimise uncertainty in making effective clinical decisions and provides better parameters estimates of the posterior distribution.