Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Success Factors for Developing e-Skills in a Developing Country : South African Case Study

The establishment and development of the Internet and other Information and Communication Technologies (ICT) in recent decades, enabled by increasing capacity, mobility, accessibility affordability and “vision based” applications of ICT, represent the material foundation for the networked society. This is often referred to as “Information Society” and/or “Knowledge Economies”. However, the creation of such a technology-supported society that holds the promise of being increasingly self-reliant and equitable, involves fundamentally new ways of thinking, working and living. It requires the building of new capacities, particularly those related to the effective appropriation of ICT, not only in the work force but across the dynamic of the entire population and specifically including the low socio-economic groups. These capacities have often been referred to as “e-skills”, “e-literacy” or “e-competences”. They are deemed as indispensable in addressing important social issues such as poverty and unemployment, the safety of citizens and in building an inclusive and capable developmental state. Recent research in South Africa confirms that building these skills and competencies in the context of a developing country (and the developmental state) require careful planning and execution Crucially it depends on inter alia : (i) a systemic, multi-stakeholder, collaborative and aggregated approach, (ii) the creation of an influential and credible e-skills ecosystem, (iii) the development of an appropriate e-skills taxonomy, (iii) the astute use of ICT and e-skills/e-competences (e-Astuteness and e-Social Astuteness) and (iv) meaningfully supporting national socio-economic developmental strategies and agendas. It is envisaged that this report will benefit practitioners (principally government planners) in developing, particularly African, countries as well as researchers willing to advance this work.
Blocking Smad 7 restores TGF-b 1 signaling in chronic inflammatory bowel disease

TGF-b1 functions as a negative regulator of T cell immune responses, signaling to target cells using the Smad family of proteins. We show here that Smad7, an inhibitor of TGF-b1 signaling, is overexpressed in inflammatory bowel disease (IBD) mucosa and purified mucosal T cells. Both whole tissue and isolated cells exhibit defective signaling through this pathway, as measured by phospho-Smad3 immunoreactivity. Specific antisense oligonucleotides for Smad7 reduce Smad7 protein expression in cells isolated from patients with IBD, permitting the cells to respond to exogenous TGF-b1. TGF-b1 cannot inhibit proinflammatory cytokine production in isolated lamina propria mononuclear cells from patients with Crohn disease (CD), but inhibition of Smad7 restores TGF-b1 signaling and enables TGF-b1 to inhibit cytokine production. In inflamed mucosal tissue explants from patients with CD, inhibition of Smad7 also restores p-Smad3 and decreases proinflammatory cytokine production, an effect that is partially blocked by anti–TGF-b1. These results show that Smad7 blockade of TGF-b1 signaling helps maintain the chronic production of proinflammatory cytokines that drives the inflammatory process in IBD and that inhibition of Smad7 enables endogenous TGF-b to downregulate this response. Article
Development of Probabilistic Neural Network Model for Weld Quality Prediction

There has been a great increase in the number of automatic and semi-automatic processes designed to speed up welding production. Submerged Arc Welding (SAW) is often the method of choice in pressure vessel fabrication. This process features high production rates, welding energy and/or welding speed and requires minimal operator skill. The selection of appropriate parameters in SAW is essential, not only to optimize the welding process in order to maintain the highest level of productivity, but also to obtain the most desirable mechanical properties of the weld. This paper explores the development of Probabilistic Neural Network (PNN) model for weld quality prediction. Experiments were designed and conducted by Taguchi’s design of experiments. Results from the PNN model are compared with the Neural Network model trained with Back Propagation (BPNN) algorithm in terms of computational speed and accuracy. The performance of PNN is better than BPNN model and is reasonably more accurate. Confirmatory experiments are done to validate this approach and reported.
Law Analysis using Deep NLP and Knowledge Graphs

We present a system for mapping facts and knowledge in legal texts, in particular case law opinions and holdings to knowledge graphs, enabling advanced semantic search over the case law corpus, as well as matching of case descriptions onto case laws using graph similarity. The essential components for knowledge graph generations are deep linguistic NLP components. We discuss how the deep analyses provided by these components allow us to process not only the core semantic relations in the legal documents, but also to process advanced semantic and pragmatic properties, including implicatures and presuppositions.
The effect of Marriage and Employment on Leaving Home in Italy

The paper examines the leaving home decision of young Italians (aged 18-32) considering simultaneously both the employment and the marital status of young people after leaving. Stylized facts and previous studies in fact, have shown that when studying leaving home decision in Italy, the decision to form a family and the probability of finding a job should also be analysed. Moreover, the paper analyses the association between the economic status of the family of origin and the nest-leaving decision. The sample consists of young Italians (aged 18-32) drawn from European Union Statistics on Income and Living Conditions (EU-SILC) for the period 2004-2011, the time span gives us the possibility to look at individuals before and after the economic crisis. We estimate a trivariate probit model for the probability of leaving home, being employed and being married allowing the error terms to be correlated. Results show that employment and marital status are key factors to escape from parental home. Moreover, according to the existing literature, individual from richer family tend to bride their offsprigs at home as long as they can, in fact the probability of leaving home decrease as income increases. As expected, after 2008 young Italians are less likely to leave parental home, to be employed and to marry.
Time-series as Background Data for Relating Medical Diagnoses Terms

Relating terms from different ontologies or identifying the most relevant entry in an ontology for a given term is an important task in various settings involving the use of ontologies. Often the task of relating terms is achieved by considering the instance level matchings within the ontologies being aligned, or using an external common ontology for indirect linking, or using annotated text corpus. In this paper, we focus on a variant of this problem that occurs in relating medical diagnosis terms. We propose a novel unsupervised approach that exploits the availability of time-series data medical events of patients during their stay in the intensive care unit (ICU). Our method, called DECREE, is evaluated using a large-scale real-world medical repository of ICU data including event data from laboratory test measurements to quantify the relationship strength between terms from a given ontology. We further outline how DECREE can be used to assign diagnoses terms in case of unlabeled pathology as well. We show that DECREE can discover better quality relationships and is more scalable than state-of-the-art time-series techniques.
Interdependent ) Cultures

Failing to practice what you preach is often condemned as hypocrisy in the West. Three experiments and a field survey document less negative interpersonal reactions to misalignment between practicing and preaching in cultures encouraging individuals’ interdependence (Asian and Latin American) than in those encouraging independence (North American and Western Europe). In Studies 1–3, target people received greater moral condemnation for a misdeed when it contradicted the values they preached than when it did not – but this effect was smaller among participants from Indonesia, India, and Japan than among participants from the USA. In Study 4, employees from 46 nations rated their managers. Overall, the more that employees perceived a manager’s words and deeds as chronically misaligned, the less they trusted him or her – but the more employees’ national culture emphasized interdependence, the weaker this effect became. We posit that these cultural differences in reactions to failures to practice what one preaches arise because people are more likely to view the preaching as other-oriented and generous (vs. selfish and hypocritical) in cultural contexts that encourage interdependence. Study 2 provided meditational evidence of this possibility. We discuss implications for managing intercultural conflict, and for theories about consistency, hypocrisy, and moral judgment.
UTILIZING A BIOPHYSICAL MODEL OF ECOSYSTEM-ATMOSPHERE EXCHANGE TO IMPROVE FIRE DANGER ASSESSMENT AND MESOSCALE METEOROLOGICAL FORECASTS

Forecasting of the regional (mesoscale) weather including stability and wind flow pattern is an important aspect of the fire operations and fire management efforts during the active season. Accurate prediction of meteorological conditions from 24 to 60 hours into the future is critical for fighting wildland fires, and making go/no-go decisions about prescribed burns. Predictions of atmospheric parameters such as temperature, humidity, wind speed, and wind direction as well as fuel moisture need be considered to properly assess immediate fire danger, and allocate fire-fighting resources. Knowing with a high degree of certainty how future wind fields and atmospheric stability might change over a region is also indispensable for evaluation of smoke dispersion from prescribed burns. The latter is critical for air quality management and mitigation of the smoke impact on public health. The safety of fire fighters is another vital management issue, which calls for reliable and accurate local weather forecast. Corrected
Wikipedia articles extracted from Polish Corpus of Wrocław University of Technology

The resource is the part of the Polish Corpus of Wroclaw University of Technology (fully available on the website http://nlp.pwr.wroc.pl/kpwr). The documents within this collection are the samples of the Polish Wikipedia articles manually annotated on the level of chunks and selected predicate-argument relations, named entities, relations between named entities, anaphora relations and word senses.
R EDUCING LEAKAGE IN DISTRIBUTED DEEP LEARNING FOR SENSITIVE HEALTH DATA

For distributed machine learning with health data we demonstrate how minimizing distance correlation between raw data and intermediary representations (smashed data) reduces leakage of sensitive raw data patterns during client communications while maintaining model accuracy. Leakage (measured using KL Divergence between input and intermediate representation) is the risk associated with the invertibility from intermediary representations, can prevent resource poor health organizations from using distributed deep learning services. We demonstrate that our method reduces leakage in terms of distance correlation between raw data and communication payloads from an order of 0.95 to 0.19 and from 0.92 to 0.33 during training with image datasets while maintaining a similar classification accuracy.
Medico-Legal Applications of Multiplex STR System to Show Allel Frequencies of D16S 539, D13S317, and D7S820 in Iraqis

INTRODUCTION: One of the most revolutionary advances in identification in recent years is the so-called 'DNA profiling or fingerprinting'. This is a technique in which virtually unique sequences of bases in the DNA strands of chromosomes are used to compare one blood or tissue sample with another, and to investigate genetic relationships. (1) The technique of determining the sequences is extremely complex, relying on cutting the DNA strands at predetermined points by the use of restriction enzymes. The fragments of DNA are separated using electrophoresis and the different
Comprehensive Compact Model of I-V Characteristics , Temperature Dependency , Variations , and Noise for Integrated Reproducible Metal-Oxide Memristors

--We present a comprehensive empirical model for the crossbar integrated metal-oxide memristors. The model consists of static and dynamic equations, which are obtained by fitting a large amount of experimental data, collected on several hundred devices. The static equation describes the device current, at non-disturbing memory state stress conditions, as a sum of three components, representing the average behavior and its device-to-device and temporal variations. All three components are expressed as functions of the applied voltage, ambient temperature, and the current memory state. The dynamic equation models the change in the memory state as a function of the amplitude and duration of the applied voltage pulse and the current memory state, and is also described as a sum of the average and device-todevice variation terms. Both equations are explicit, computationally inexpensive, and suitable for SPICE modeling. At the same time, the model has good predictive power, which is supported by the validation results. We expect that the presented model will be very useful for realistic simulations of various memristor-based circuits, such as, e.g., in-situ trained mixed-signal neuromorphic networks, which rely on analog properties of the memory devices and are highly sensitive to their non-ideal behavior. Although our results are obtained for particular, though representative devices, the modelling approach is quite general and can be applied to other nonvolatile memory technologies.
rotective factors and resilience in adolescents : The mediating role f self-regulation aulo

The main goal of this study was to explore the mediating role of self-regulation in the relationship between protective factors and resilience. The sample was composed of 393 adolescents who attended secondary education. Participants were assessed using the Healthy Kids Resilience Assessment to collect information about four protective factors–school, home, community and peer environment–and resilience. Two dimensions of self-regulation–goal setting and impulse control–were assessed using the Short Self-Regulation Questionnaire. The results indicated that home, community and peer environment predicted significantly the levels of resilience of the students. Home environment was the main resilience predictor. Goal setting and impulse control abilities were also predictors of resilience but no evidence was found for a mediating effect of the self-regulation dimensions on the relationship between protective factors and resilience. Results are discussed and implications for prevention from a developmental perspective are presented. © 2016 Colegio Oficial de Psicólogos de Madrid. Published by Elsevier España, S.L. U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Factores de protección y resiliencia en adolescentes: la autorregulación como variable mediadora
A Framework for IT Investment

The IT services structure opens the possibility for a number of studies aimed at determining the value of IT investments. Previous studies have not had much success in establishing a link between IT investments and firm performance. Much of this may be due to the presence of risky IT projects that consume a large portion of the budgets . The use of IT services, however, could serve as good indicators of the extent of IT within an organization while searching for the value links thought to be present. Lastly, services can be aligned to the stra-tegic intent of an organization as viewed by different stakeholders. In this fashion we can study the viewpoints of different groups in their pursuit of firm success. The contingencies that may be found could provide a good indica-tor of the differences between information professionals and general management.
Respiratory function in patients with lumbar stenosis: A comparative analysis

Degenerative diseases of the spine are conditions that involve loss of structure and normal function of the spine and may lead to worsening functional capacity, decreased exercise tolerance, and reduced quality of life due to neurogenic lameness and chronic low back pain. The choice of arthrodesis is due to the presence of evolutionary neurological injury or intractable pain. Objective: To analyze and compare spirometric values and respiratory muscle strength in patients with lumbar stenosis. Method: cross-sectional study with 38 patients of both sexes, divided into a group of 19 operated patients and another group of 19 patients awaiting surgery, aged between 50 and 80 years, who were evaluated by spirometry and manovacuometry. Results: In our study, it was observed that pulmonary function was within the reference values, considering that the group of operated patients presented better performance in spirometry and manovacuometry. Conclusion: Operated patients showed improvement in lung function compared to non-operated patients.
Overview of the KAUST ’ s Cray X 40 System – Shaheen II

In November 2014, King Abdullah University of Science and Technology (KAUST) acquired a Cray XC40 supercomputer along with DataWarp technology, a Cray Sonexion 2000 storage system, a Cray Tiered Adaptive Storage (TAS) system and a Cray Urika-GD graph analytics appliance. This new Cray XC40 system installed in March 2015, named Shaheen II, will deliver 25 times the sustained computing capability of KAUST’s current system. Shaheen II is composed of 6174 nodes representing a total of 197,568 processors cores tightly integrated with a richly layered memory hierarchy and dragonfly interconnection network. Total storage space is of 17 PB with additional 1.5 PB dedicated to burst-buffer. An overview of the systems specifications, the challenges raised in term of power capping, and the software ecosystem monitoring the usage will be presented and discussed. Keywords-ShaheenII; KAUST, Cray XC40, Sonexion, TAS, DataWarp, monitoring
Image classification using embedded spaces generated by Siamese Networks

With the emergence of new algorithms and the increase in computing power, machine learning techniques are gaining enormous importance in a variety of fields. An important limitation of these systems is the need to have a very high amount of data to train them. Siamese networks are a special type of neural networks that have certain advantages when dealing with problems where limited data is available. In this document, an analysis of this type of networks is carried out, which provides an insight into its true potential and its limitations. A comparison between existing architectures is presented and possible improvements are introduced. In addition, its parameters are exposed and the most appropriate configuration of them for each scenario is suggested. The final objective of this project is to establish a reference that will help data scientists to deal with problems that were difficult to address due to the reduced amount of data available until now.
Chapter 6 Places of Life Events as Bequestable Wealth : Family Territory and Migration in France , 19 th and 20 th Centuries

Previous studies have shown that the family influences migration decisions in various ways, but very few of them take into account past migrations among the kinship group. In this study, we take advantage of new historical data, based on the TRA survey, to discuss the extent to which kinship influences migration. We use the concept of spatial capital to capture all the knowledge families possess about geographical locations. We are then able to show how this knowledge is—or is not—handed down from one generation to another. This is a key point of the analysis of migration as it means that migration decisions are not only influenced by individual characteristics or economic or historical context, but also by the past migration behavior of the family. As such, migration is not only an investment for the migrant or for his close relatives but can be seen as a long-term investment of the kinship group.
Is PageRank All You Need for Scalable Graph Neural Networks ?

Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, efficiently utilizing them on web-scale data remains a challenge despite related advances in research. Most recently proposed scalable GNNs rely on an expensive recursive message-passing procedure to propagate information through the graph. We circumvent this limitation by leveraging connections between GNNs and personalized PageRank and we develop a model that incorporates multi-hop neighborhood information in a single (non-recursive) step. Our work-in-progress approach PPRGo is significantly faster than multi-hop models while maintaining state-of-the-art prediction performance. We demonstrate the strengths and scalability of our approach on graphs orders of magnitude larger than typically considered in the literature. ACM Reference Format: Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Martin Blais, Amol Kapoor, Michal Lukasik, and Stephan Günnemann. 2019. Is PageRank All You Need for Scalable Graph Neural Networks? In 15th International Workshop on Mining and Learning with Graphs, August 05, 2019, Anchorage, AK. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/1122445.
Sentence-Level Adaptation for Low-Resource Neural Machine Translation

Current neural machine translation (NMT) approaches achieve state-of-the-art accuracy in high-resource contexts. However, NMT requires a great deal of parallel data to deliver acceptable results; thus, it is currently unsuited for translating in low-resource contexts (especially when compared to phrase-based approaches). We propose a method that better leverages the limited data available in such low-resource settings by adapting the model for each sentence at inference time. A general NMT model is trained on the limited training data; then, for each test sentence, its parameters are ﬁne-tuned over a subset of similar sentences extracted from the training set. We experiment with various similarity metrics to extract these similar sentences. It is observed that the sentence-adapted models achieve slightly increased BLEU scores compared to standard neural approaches on a Xhosa-English dataset.
HOW MUCH HEAVIER IS A “HOE” FOR WOMEN? WAGE GENDER DISCRIMINATION IN THE BRAZILIAN AGRICULTURAL SECTOR

This paper aims at analyzing the existence of wage differentials by genders in the agricultural labor market in the Brazilian rural area, with the purpose of verifying if the differentials are due to differences in explained characteristics or discriminatory. Equations were estimated and the wage differential detailed decomposition with microdata from the PNAD 2015 with the Heckman's correction. The method used was the decomposition of Oaxaca-Blinder. The main results show that the wage/hour of men is 157.62% higher than that of women, and the effect of discrimination is 108.38% this gap. The main conclusion indicates that the gender wage differential decreases with the formality of work and with the increase of hours worked by women. It is worth nothing that the results provide the visualization of gender discrimination in the rural labor market, adding contributions to the empirical studies on wage differentials.