Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Advances in Representation Learning for Electronic Health Records: A Systematic Review

Representation Learning (RL) aims to convert data into low-dimensional and dense real-valued vectors, so as to realize reasoning in vector space. RL is one of the important research contents in the analysis of health data. This paper systematically reviews the latest research on Electronic Health Records (EHR) RL. We searched the Web of Science, Google Scholar, and Association for Computing Machinery Digital Library for papers involving EHR RL. On the basis of literature review, we propose a new taxonomy to categorize the state-of-the-art EHR RL methods into three categories: statistics learning-based RL methods, knowledge RL methods and graph RL methods. We analyze and summarize their characteristics according to the input data form and underlying learning mechanisms. In addition, we provide evaluation strategies to verify the quality of EHR representations from both intrinsic and extrinsic perspectives. Finally, we put forward three promising research directions to promote future research. Overall, this survey aims to provide a profound overview of state-of-the-art developments in the field of EHR RL and to help researchers find the most appropriate methods.
King Khalid University Faculty of Languages and Translation English Depart Arabic diglossia and its impact on the social communication and learning process of non-native Arabic learners: Students’ perspective. By

The Arabic language is characterized as a diglossic language. Hence, this study dealt with this sociolinguistic phenomenon and examine the impact of the Arabic diglossic situation in terms of social communication and the Arabic language learning process based on the second Arabic language learners' perspective. The study starts with reviewing and discussing the literature by considering the Arabic diglossia from both linguistic and educational perspectives. To achieve the research objectives the researcher used mixed-method research namely, a close-ended questionnaire for quantitative data and open-ended questions for qualitative data. The sample included 26 students from different nationalities at the Arabic Language Center for Speakers of Other Languages at King Khalid University. The researcher concluded that the Arabic diglossic situation, especially the difference between the functionality of MSA (Modern Standard Arabic) and CA (Colloquial Arabic) is an impactful factor that can create an obstacle for the second Arabic language learners in terms of social communication in real context as well as the Arabic language learning process. The data prove that the second Arabic language learners in the Arabic Language Center for Speakers of Other Languages at King Khalid University are aware of Arabic diglossia and its impact on their social communication in real-life situations. Moreover, it was found that the Arabic diglossic situation slightly impacted their Arabic language learning process in terms of their desire to continue learning the Arabic language and the switching between MSA and CA that take place in the educational setting by some teachers.
Better to be indirect? Testing the accuracy and cost-savings of community leader surveys for poverty targeting

We test the validity of key informant reports of household characteristics. We compare household and informant reports of assets, develop poverty indices from both, and examine errors in reporting and targeting resulting from using indirectly reported variables. Informant-based targeting indices are highly correlated with household measures, do not vary systematically across informant characteristics, and can be used to assign a simulated anti-poverty program with similar error rates as related methods. Informant indices can be reasonable substitutes for self-reported indices in simple regressions. In our setting, eliminating direct collection of household information would have reduced survey costs by 50%. JEL codes: O12; I32; I38; C83
Exploring the Application of Reinforcement Learning Techniques for Scheduling in a Dense Wireless Sensor Network

In dense WSNs (Wireless Sensor Networks) there are often hundreds or maybe thousands of nodes deployed. This type of network configuration has emerged and increased rapidly in recent years as a result of the advent of IoT (Internet of Things). The task of coordinating these nodes so that they are all allocated bandwidth to transmit is called scheduling. Many traditional approaches to scheduling which were not designed for this application and break, incapable of handling such a highly congested network. In recent years there have been a number of advancements in wireless network scheduling brought about by the integration of Reinforcement Learning(RL) techniques into the scheduling protocols. Being such a recent development, the application of these techniques to deployment in a WSN is still relatively unexplored. In this thesis, the Q-Mod scheduling protocol is proposed. This protocol leverages existing RL techniques to achieve high bandwidth utilisation and fairness across the network. This protocol is highly resilient to rapid changes in the network and can accommodate large numbers of sensor nodes, making it a perfect candidate for deployment in the context of a WSN. In the latter half of this thesis, a number of improvements are suggested that can be made to the Q-Mod scheduler to further increase its potential to maximise bandwidth utilisation and minimise the age of information across the network. The number of IoT devices and networks are increasing exponentially. If sufficiently optimised this research could eventually be used in a host of applications from Smart Cities to agriculture to V2X (Vehicle-to-anything) communications.
A Review of Business Models for Shared Mobility and Mobility-as-a-Service (MaaS)

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Towards Scalable Parallelization of Functional System Simulation with SimuBoost

Analyzing program and system behavior has become a challenging task with the advent of increasingly complex and parallel software. At the same time, hybrid main memory architectures are emerging that require an even better understanding of low-level data structure use and memory access patterns. Functional full system simulation (FFSS), that is simulation at the instruction level, provides a powerful foundation to study the runtime behavior and interaction of computer architecture, operating system, and applications [1, 6, 12, 13]. Since the entire execution environment in a functional simulation is virtual, every operation carried out can be easily inspected.
The critique of action research in Administration as a way of recovering its potential for transformative action

Débora Coutinho Paschoal Dourado https://orcid.org/0000-0001-8316-8551 Professor at the Department of Administrative Sciences (DCA) of the Federal University of Pernambuco (UFPE) PhD in Administration from the Federal University of Pernambuco (UFPE) debora.cpdourado@ufpe.br ABSTRACT This article aimed to analyze how action research has been applied in the field of administration in Brazil. Bibliographical research was carried out, based on the Scientific Periodicals Electronic Library (SPELL). For the research corpus, 22 articles were selected and analyzed based on four categories: intervention, intervention with participation, beneficiaries, and sociopolitical or technical aspects. The findings indicate that most of the analyzed articles use action research in a limited way, reducing it to a technique for managerial problem-solving, and deviating its transformative potentiality. The importance of action research that considers the context of those involved and encourages critical self-reflection is highlighted here, so that ready-made solutions are not applied to the problems of the studied collectivity.
RMHP_A_349545 243..255

1Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 3School of Mathematics & Physics and Big Data, Chongqing University of Science and Technology, Chongqing, People’s Republic of China; 4Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
Almost Linear Time Algorithms for Flows in Graphs

We study the main, high-level ingredients of the nearly-linear time Laplacian solver of Spielman and Teng and their application to finding an approximately maximum flow in a graph in almost-linear time. We start with basic tools from linear algebra, such as properties of symmetric and positive semidefinite matrices, as well as the Moore-Penrose pseudoinverse. We then move to the Laplacian matrix of a graph and some of its applications, such as computing the number of spanning trees (the so-called Matrix Tree Theorem) and approximating the sparse cuts of a graph. Next we describe the well-known Conjugate Gradient Method, an iterative algorithm to approximate a solution to a linear system, and use this method with preconditioning to construct an efficient Laplacian solver. In the end, we describe an algorithm to find an approximately maximum flow in undirected graphs in almost-linear time with the help of nearly-linear Laplacian solvers and the multiplicative weights update method. While the main algorithms covered here are not the fastest known, they contain the majority of the ingredients and tools from the latter.
Strategy to Increase Consumers in Buying Ethnic Restaurants through the Perception of Authenticity, Brand Awareness and Brand Image on Brand Loyality

This study aims to determine the effect of Perception of Authenticity, Brand Awareness, and Brand Image on Brand Loyalty, to determine the effect of Perception of Authenticity, Brand Awareness, Brand Image and Brand Loyalty on consumer purchase intentions and to determine the effect of Perception of Authenticity, Brand Awareness, and Brand Image on Consumer Purchase Intention through Brand Loyalty. The nature of this research is quantitative. The variables of this study are Perception of Authenticity, Brand Awareness, Brand Image, Brand Loyalty and consumer purchase intention. The population in this study are ethnic restaurants in Banjarnegara. The sampling method is purposive sampling, based on the characteristics or criteria of the total sample of 150 respondents. The data collection method uses a questionnaire. The analysis technique used is multiple linear regression equations with a significance level of 5%. The results show that the perception of authenticity, Brand awareness and Brand image partially have a positive and significant effect on brand loyalty. Brand loyalty has a positive and significant effect on consumer purchase intentions. However, perception of authenticity has no effect on consumer purchase intentions. Brand awareness and Brand Image have a positive and significant effect on consumer purchase intentions. Brand loyalty is able to mediate the relationship between the perception of authenticity, brand awareness and brand image on consumer purchase intentions.
Building Effective Student Project Teams: What Has Shared Transformational Leadership Got to do With it?

The central problem this study seeks to solve pertains to the inability of college graduates to engage effectively in teamwork. To solve this problem, we explored the antecedent and outcome of teamwork quality—shared transformational leadership as an antecedent and team effectiveness as an outcome. We hypothesized that the effect of shared transformational leadership on team effectiveness will be mediated by teamwork quality. The sample consists of 98 GSA students, sub-divided into 20 project teams. The findings, using MPLUS software, provide support for the hypothesis, as evidenced by a significant indirect effect between shared transformational leadership and team effectiveness (B = 0.56, p < 0.01), through teamwork quality. We also hypothesized that individual-level transformational leadership will predict shared transformational leadership. The hypothesis was rejected, as it was not supported by the findings. The findings have important implications for leaders, leadership educators, leadership researchers, and organizations.
University of Groningen Interrelationships between maternal DHA in erythrocytes, milk and adipose tissue. Is 1wt% DHA the optimal human milk content? Data from four Tanzanian tribes differing in lifetime stable intakes of fish Luxwolda,

Little is known about the interrelationships between maternal and infant erythrocyte-DHA, milk-DHA and maternal adipose tissue (AT)-DHA contents. We studied these relationships in four tribes in Tanzania (Maasai, Pare, Sengerema and Ukerewe) differing in their lifetime intakes of fish. Cross-sectional samples were collected at delivery and after 3 d and 3 months of exclusive breast-feeding. We found that intra-uterine biomagnification is a sign of low maternal DHA status, that genuine biomagnification occurs during lactation, that lactating mothers with low DHA status cannot augment their infants’ DHA status, and that lactating mothers lose DHA independent of their DHA status. A maternal erythrocyte-DHA content of 8 wt% was found to correspond with a mature milk-DHA content of 1·0 wt% and with subcutaneous and abdominal (omentum) AT-DHA contents of about 0·39 and 0·52 wt%, respectively. Consequently, 1 wt% DHA might be a target for Western human milk and infant formula that has milk arachidonic acid, EPA and linoleic acid contents of 0·55, 0·22 and 9·32 wt%, respectively. With increasing DHA status, the erythrocyte-DHA content reaches a plateau of about 9 wt%, and it plateaus more readily than milk-DHA and AT-DHA contents. Compared with the average Tanzanian-Ukerewe woman, the average US woman has four times lower AT-DHA content (0·4 v. 0·1 wt%) and five times lower mature milk-DHA output (301 v. 60 mg/d), which contrasts with her estimated 1·8–2·6 times lower mobilisable AT-DHA content (19 v. 35–50 g).
The Development of a SYBR Green I Multiple Real-time Fluorescence PCR Assay for Detection of Actinobacillus pleuropneumoniae, Haemophilus parasuis and Pasteurella multocida

Actinobacillus pleuropneumoniae, Haemophilus parasuis, and Pasteurelle multocida are common pathogens of respiratory diseases in the pig industry, and they may cause secondary infections and serious economic losses to the pig industry. The clinical symptoms caused by these three pathogens are difficult to distinguish with the naked eye, and mix infections bring difficulties to the diagnosis of diseases. In this study, specific primers were designed on the basis of A. pleuropneumoniae Apx IV, H. parasuis Omp P2 and P. multocida PlpE gene. The expected amplified products of A. pleuropneumoniae, H. parasuis, and P. multocida were 157, 120 and 305 bp, respectively. After the amplified fragment was cloned into a vector, a standard plasmid was constructed. By using the standard plasmid as template, a fl uorescence quantitative PCR method for simultaneous detection of A. pleuropneumoniae, H. parasuis, and P. multocida multiple SYBR Green I was established. Combined with melting curve analysis, the sensitivity, specificity, and repeatability were also evaluated. The results showed that the sensitivity of the method for detecting the three pathogens were 147, 145, and 61 copies/μL. On the same melting curve that produced three specific Tm peaks, no cross reaction with other bacteria was observed, and the method demonstrated good specificity and repeatability. This method could be used for the simultaneous detection of the three pathogens, thus providing an eff ective detection tool for disease prevention and treatment.
Fairkit-learn: A Fairness Evaluation and Comparison Toolkit

Advances in how we build and use software, specifically the integration of machine learning for decision making, have led to widespread concern around model and software fairness. We present fairkit-learn, an interactive Python toolkit designed to support data scientists’ ability to reason about and understand model fairness. We outline how fairkit-learn can support model training, evaluation, and comparison and describe the potential benefit that comes with using fairkit-learn in comparison to the state-of-the-art. Fairkit-learn is open source at https://go.gmu. edu/fairkit-learn/.
An Exploitation of Heterogeneous Graph Neural Network for Extractive Long Document Summarization

Heterogeneous Graph Neural Networks (Het-001 erGNN) has been recently introduced as an 002 emergent approach for many Natural Language 003 Processing (NLP) tasks by enriching the com-004 plex information between word and sentence. 005 In this paper, we try to improve the perfor-006 mance of Extractive Document Summarization 007 (EDS) for long-form documents based on the 008 concept of HeterGNN. Specifically, long docu-009 ments (e.g., Scientific Papers) are truncated for 010 most neural-based models, which leads to the 011 challenge in terms of information loss of inter-012 sentence relations. In this regard, we present 013 a new method by exploiting the capabilities of 014 HeterGNN and pre-trained language models. 015 Particularly, BERT is considered for improv-016 ing the sentence information into the Heteroge-017 nous graph layer. Accordingly, two versions 018 of the proposed method are presented which 019 are: i) Multi Graph Neural Network (MTGNN-020 SUM), by combining both heterogeneous graph 021 layer and graph attention layer; and ii) Het-022 erGNN with BERT (HeterGNN-BERT-SUM), 023 by integrating BERT directly into the hetero-024 geneous graph structure. Experiments on two 025 benchmark datasets of long documents such 026 as PubMed and ArXiv show that our method 027 outperforms state-of-the-art models in this re-028 search field. 029
A review of simultaneous localization and map building algorithms for mobile robots in unknown environment

Simultaneous localization and mapping (SLAM) algorithm for mobile robots is a key problem in the field of robotics. The latest progress of SLAM algorithms is surveyed, and the key techniques adopted. Various existing methods were analyzed and compared in details of map-building model, computation complexity, robustness and so on. Finally, the key problems and future research trend of SLAM approaches are presented.
A Performance Comparison of Modern Garbage Collectors for Big Data Environments

The use of Java to develop Big Data platforms (e.g., Hadoop, Spark) has been a favoured choice among developers due to its fast development of large-scale systems, in part due to the automatic memory management. However, the impact of garbage collection on these platforms has been more and more of a concern, as platforms increasingly require lower pause times, higher throughput, and better memory usage by the garbage collector. In this project, we aim to understand how different garbage collectors scale in terms of throughput, latency, and memory usage in memory-hungry environments, so that, for given a platform with particular performance needs, we may map the most suitable garbage collection (GC) al-gorithm. Previous works on this subject have used workloads that either failed to represent realistic use case scenarios of Big Data platforms or were run on top of academic implementations of the JVM, that are not meant to run Big Data applications. In this work, we use a combination of Big Data platforms (e.g., Cassandra, Lucene, and GraphChi) and real-world-based benchmarks (e.g., DaCapo) on top of an industrial JVM (OpenJDK HotSpot JVM) which provides a high degree of accuracy to the results. Additionally, we develop fine-grained benchmarks to study in more detail how particular techniques (e.g., barriers) employed by garbage collectors affect different performance metrics.
Prevalence and correlates of video gaming addiction among Nigerian in-school adolescents

Video gaming has become a popular phenomenon among in-school adolescents in many parts of the world due to rapid development of the gaming industry in the 21st century. Resultantly, in-school adolescents have access to a broad spectrum of video games with diverse potentials and capabilities for maximal user experience. Whilst video gaming can provide some bene its for in-school adolescents, it can also pose some dire challenges. Thus, video gaming addiction has come to public attention and is gaining traction amongst stakeholders in the Nigerian school system. Hence, this study investigated prevalence rates and correlates of video gaming addiction among in-school adolescents randomly selected from public and private secondary schools in Kwara State, Nigeria. A total of 850 in-school adolescents were sampled from twenty secondary schools. The independent variables (gender, academic performance, life satisfaction, self-esteem, peer in luence, perceived level of stress and executive function) were assessed using a bio-data form and standardized scales respectively while the dependent variable (video gaming addiction) was examined using the Game Addiction Scale. Data were subjected to frequency counts, percentage, correlation analysis and multiple regression. The results of this study showed that a majority of the respondents (74%) played of line and online video games, while 13.5% of the respondents were identi ied as having a video gaming addiction. Gaming addiction was signi icantly correlated with peer in luence, gender, academic performance, executive function and life satisfaction. It was recommended that effective preventive measures should be put in place to curb the menace of video game addiction and its associated effects among Nigerian adolescents.
Predicting the Gullibility of Users from their Online Behaviour

In this research we aimed to explore the predictors of gullibility in an online environment. We used machine learning algorithms to build models for predicting gullibility from social media behaviour. In total 103 Twitter users had completed the survey containing a scale for measuring gullibility. Survey data was then combined with the features extracted from the user’s activity on Twitter. Besides data that was directly accessible through the Twitter API, we engineered new features containing punctuation data, usage of emojis and text vectorization with TF-IDF. This data was then standardized and reduced using Principal Component Analysis. In the modeling phase we used both regression and classification techniques. After comparison of the results with their baselines, we conclude that there is an indication that gullibility can be predicted from online behaviour. Further research and analysis are planned and are needed for a better understanding of the relationship between social media activity and gullibility. Results from this experiment showed us great potential for future work.
OF VECTOR FILTERING SCHEMES FOR COLOR IMAGES CORRUPTED BY IMPULSE NOISE

S rozvojom nových komunikačných technológií, napr. systémov pre videotelefóny, videokonferenčných systémov, HDTV, internetu a digitálnej televízie, vyvstala aj požiadavka číslicového spracovania farebných obrazov. Vzhľadom na skutočnosť, že farebné obrazy predstavujú vektorovo-hodnotové signály, dôraz pri spracovaní sa kladie na použitie vektorových techník. V prípade odstraňovania šumu v obrazoch sa tento problém dotýka vektorových filtrov. Z týchto dôvodov je témou tohto článku prehľad najčastejšie používaných vektorových filtrov pre potláčanie impulzového šumu vo farebných obrazoch.