Miracle-dz/UniFAR-FTUs huggingface dataset:
11 An Overview on ARIES : Reliable European Identity Ecosystem

1University of Murcia, Murcia, Spain 2GEMALTO, Czech Republic 3Atos Research and Innovation, Atos, Calle Albarracin 25, Madrid, Spain 4Saher Ltd., United Kingdom 5SONAE, Portugal 6Office of the Police and Crime Commissioner for West Yorkshire, (POOC), West Yorkshire, United Kingdom E-mail: jorgebernal@um.es; rtorres@um.es; martin.david@gemalkto.com; alberto.crespo@atos.net; skarmeta@um.es; dave@saher-uk.com; juliet@saher-uk.com; tioliveira@sonae.pt; mhsilva@sonae.pt; stuart.martin@westyorkshire.pnn. police.uk; julivale@um.es; ignacio.alamillod@um.es
Neural Network Models to Identify Medication Relations from Clinical Narratives

Electronic health records contain valuable information about patient care and experience, especially related to prescribed medications. Identifying medication relations between drugs and associated attributes automatically from clinical narratives can help develop advanced tools for decision support. In this paper, we investigate the strengths of neural network models to identify eight medication relations. We find that relation extraction is sensitive to complexity of data patterns as well as model capacity. Our results show that bidirectional LSTM models were the best models for the relation classification task, and achieved an overall micro-averaged F1 score of 0.892, outperforming SVM and CNN models, with benefit of bidirectional structure and long dependence memory.
Prediction of Fuel Consumption of Long Haul Heavy Duty Vehicles using Machine Learning and Comparison of the Performance of Various Learning Techniques

This study aims at a possible solution to predict the fuel consumption of heavy duty diesel trucks, particularly, the tractor semitrailer for their long haul operations using various machine learning techniques. It intends to provide a possible alternative to simulation or physics based models, which often are very complicated. The stringent laws on emission control set by the Paris Agreement and the fact that heavy duty trucks contribute to almost 27% of CO2 emissions from road transport and their dependence on diesel for operations (in long haul) makes it the need of the hour to first, have an estimate on the emissions being produced and second, to develop technologies to reduce those emissions. This study focuses specifically on the first part i.e., estimating the amount of fuel consumed by heavy duty trucks in the European Union and thereby determine the emissions being produced. The main objective is to examine whether an approach of machine learning could be a viable option to predict fuel consumption. This thesis is part of the AEROFLEX project and was done in collaboration with TNO, which provided all the data-sets required for the study. The idea was to explore the regime of machine learning for one time step ahead prediction of fuel consumption. Furthermore, this study also focused on the development of another model by not using any variables affected by the driver as input into the training model. This exclusion was necessary to make sure the model remained adaptive to new routes and new trucks, especially because large scale on-road testing of the newly developed trucks is impossible and also because this way would help predict the fuel consumed by a truck without the necessity of it driving on a road. The study concludes with a comparison with an existing simulation model at TNO and provide an alternative machine learning solution. It also provides a comparison between different machine learning techniques and suggest the most accurate one. It was found that machine learning could potentially be used to predict the amount of fuel consumed by a long haul heavy duty truck driving on a motorway. It was also found that engine torque was the variable that affected the fuel consumption of the truck the most. Furthermore, Neural Network was the most potent algorithm among all the other learning techniques for both the models developed in this study with it performing better than the simulation tool by a factor of approximately 3.8 in the model where the driver/drive influenced inputs were not considered in the training data-set. The results obtained from this work at a sampling frequency of 10 Hz. (i.e., 0.1 seconds) are comparable to the ones reported by other sources at a sampling rate of 0.016 Hz. (i.e., 1 minute) or 0.0016 Hz. (i.e., 10 minutes). This goes on to say that the machine learning algorithms are also potent at much higher sampling frequencies.
Selected Papers of Beijing Forum 2005 Ambient Air Pollution and Adverse Health Effects

Pollution of the air have been known since the early man discovered fire and started to use biomass for heating and preparing food. However, many people consider that ambient air pollution is a modern phenomenon linked to industrial development, although health problems related to air pollution have been known before the industrial age. London has always been considered synonymous with of air pollution due to heavy industrial activity and use of poor quality coal for heating and industrial purposes. The famous London smog (smoke and fog) in 1950, which was mainly due to particles and sulfur dioxide, resulted in a significantly increased mortality. Today, such pollution episodes are much less frequent in developed Western countries, whereas they still occurs in some Asia countries with very high levels of air pollution due to the burning of biomass and very high daily concentrations, especially during the winter. The London smog episode became the catalyst for modern air pollution research and regulation of air pollution based upon health outcome. It is now widely accepted that exposure to outdoor air pollution is associated with a broad range of acute and chronic health effects, ranging from minor physiological disturbances to death from respiratory and cardiovascular diseases. The World Health Organization (WHO) estimates that urban air pollution contributes to approximately 800.000 death and 4.6 million lost life-years (DALY) worldwide (WHO 2002). However, this burden is not distributed equally globally, and it is assumed that the problem in Asia is greater. However, a concern is that these estimates are mainly based upon US data and not Asian data. A large regional variation in the estimated disease burden has been reported, and air pollution is estimated to account for 5.9% of the total DALY’s in East Asia, and 3.7% in South East Asia, and urban outdoor air pollution is responsible for 1.4% and 0.55% respectively, with household air pollution being of the greater concern. The health effects of outdoor air pollution in developing countries of Asia have recently been reviewed by the Health Effect Institute (HEI, 2004). In spite of this greater risk associated with in-door air pollution the focus on this paper will be on ambient outdoor air pollution.
Psychoacoustic Characterisation of a Small Fixed-pitch Quadcopter

In this study, a series of acoustic measurements were taken on a small fixed-pitch quadcopter, both in a controlled indoor acoustic environment and while flying outdoors. At controlled indoor conditions, audio signatures were recorded with the quadcopter fixed to a horizontal stand, with 1, 2 and 4 rotor blades operating, and only with the electric motors working. At field conditions, audio signatures were recorded on the ground, varying the relative (lateral) distance and altitude between the quadcopter flight track and the microphone, and the quadcopter payload. Frequency spectra, in the form of narrowband sound-levels, and a series of sound quality metrics (loudness, sharpness, roughness, fluctuation strength and tonality) were calculated from the recorded audio signatures to investigate the specific psychoacoustic characteristics of the small quadcopter under the whole set of experimental conditions tested. On the other hand, at a first step to anticipate potential noise issues of quadcopters operating in urban environments, an objective comparison on the basis of frequency characteristics and sound quality metrics is performed between the small quadcopter and a number of road vehicles and aircraft.
High Heat Generating Granites of Western Saudi Arabian Shield

With growing demand for electricity and drinking water, Saudi Arabia needs to reassess its energy policy and source. The country has considerable volume of high heat generating granites along the western Arabian shield. These granites are under favorable stress condition and suitable to initiate EGS projects. The Midyan granite, located in the north, for example is capable of generating 160  10 kWh of electricity. With the EGS technology making considerable advancement, Saudi Arabia is placed in a comfortable position to mitigate CO2 emission and to meet the future energy and water demand.
Detecting deceptive reviews using Argument Mining

The unstoppable rise of social networks and the web is facing a serious challenge: identifying the truthfulness of online opinions and reviews. In this paper we use Argument Mining to extract Argumentation Frameworks (AFs) from reviews and explore whether the use of these AFs can improve the performance of machine learning techniques in detecting de-ceptive behaviour, resulting from users lying in order to mis-lead readers. The AFs represent how arguments from reviews relate to arguments from other reviews as well as to arguments about the goodness of the items being reviewed
Deduplication of Hospital Data using Genetic Programming

The record deduplication is the task of identifying, in a data repository, records that refer to the same real world entity or object in spite of misspelling words, types, different writing styles or even different schema representations or data types. Several systems that rely on consistent data to offer high-quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of duplicates, quasi replicas, or nearduplicate entries in their repositories. Because of that, there have been significant investments from private and government organizations for developing methods for removing replicas from its data repositories. This is due to the fact that clean and replica-free repositories not only allow the retrieval of higher quality information but also lead to more concise data and to potential savings in computational time and resources to process this data. Genetic Programming, new methodology has been proposed to reduce the computation time and resources to process data. Genetic programming combines several different pieces of evidence extracted from the data content to find a deduplication function that is able to identify whether two entries in a repository are replicas or not.
Ceramic Particle Armor

One of the deadliest threats that ground combat vehicles regularly encounter is the Explosively Formed Penetrator (EFP). The extremely high impact velocities that are typical of EFPs necessitate extremely heavy armor, which is often impractical due to the corresponding compromise in mobility and reliability. One possible solution to this threat is to use granular ceramics as an alternative to current armor solutions. An evaluation of high-speed impacts into granular ceramics and extensive testing across a wide range of parameters provides data to support this proposal. These results demonstrate an impressive potential for granular ceramics in EFP protection kits with a substantial reduction in both cost and weight to achieve the same level of protection as plate or sheet materials. Citation: P. Kopinski, “Ceramic Particle Armor”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.
Se p 20 19 r-wise fractional L-intersecting family

Let L = {1 b1 , . , as bs }, where for every i ∈ [s], ai bi ∈ [0, 1) is an irreducible fraction. Let F = {A1, . , Am} be a family of subsets of [n]. We say F is a r-wise fractional L-intersecting family if for every distinct i1, i2, . , ir ∈ [m], there exists an a b ∈ L such that |Ai1 ∩Ai2 ∩ . ∩Air | ∈ { a b |Ai1 |, a b |Ai2 |, . , a b |Air |}. In this paper, we introduce and study the notion of r-wise fractional L-intersecting families. This is a generalization of notion of fractional L-intersecting families studied in [1].
Video Segments Retrieval System based on A entive CNN

In the past few years, video retrieval has aracted a lot of aention. However, traditional methods simply relay on manual hash tags, title and description of video compared with free text query, which suers from high bias and low accuracy. Hence, in video retrieval, how to understand video as well as the relationship between query text still needs more exploration. In this project 1, we proposes a novel video segments retrieval system based on architecture called Aentive Convolutional Nerual Network (ACNN). In our model, video clips are re-embedded by contextual information using attentive N-gram model and combined with query text features in cross-model. e probability of reference and osets of start and end points are loss function to train model. Our results are promising compared with baseline model ACRN in [5].
S 2 Appendix : Determinants of plasma homocysteine as ingredients for the novel drink powder supplement

Existing evidence suggests that women conceiving in the dry season in The Gambia have particular micronutrient deficiencies in nutrients related to one-carbon metabolism that result in a lower maternal methylation potential (1). This is in turn associated with a pattern of methylation suggestive of loss of imprinting at the gene VTRNA2-1 (2). Our group’s long-term goal is therefore to design a periconceptional nutritional intervention for women that improves the regulation of the infant epigenome by providing micronutrients in the quantity necessary for optimal one-carbon metabolism all year round. To achieve this we first require a proof-of-concept trial showing that a nutritional supplement can optimise the metabolome in non-pregnant women by correcting nutritional imbalances and increasing the methylation potential by reducing homocysteine. Should the supplement work it could be a promising candidate for future pregnancy trials investigating epigenetic outcomes.
Homophobia, conversion therapy, and care models for trans youth: defending the gender-affirmative approach

Abstract In recent years, opponents of the gender-affirmative approach to trans youth have argued that it bears homophobic roots and may be tantamount to conversion therapy. This argument is mistaken. In this article, I first argue that there is no evidence that social and/or medical transition is motivated by homophobia. Contrary to the critique’s tacit premise, many if not most trans people are LGBQ following transition. Furthermore, despite social progress in the last decade, transphobia remains more common than homophobia. Second, the gender-affirmative approach is fundamentally dissimilar to conversion therapy, unlike clinical approaches that oppose affirmation and seek to prevent transition. The comparison to conversion therapy relies on a superficial understanding of sexual orientation, such that a change of label (e.g. straight, bisexual, gay, lesbian) is equivalent to a change of sexual orientation even without changes to the targets of sexual attraction. By contextualizing conversion therapy in relation to trans youth care, I show that, on the contrary, conversion therapy has long focused on preventing transgender youth from growing up trans.
CHALLENGES FOR HUMAN RESOURCE PRACTITIONERS : SOME EMPIRICAL EVIDENCE OF NEW ZEALAND ORGANISATION ’ S EXPECTATIONS

Emphasis on ‘people’ as a source of competitive advantage increases the challenges and expectations for human resource practitioners in New Zealand organisations. To compete effectively in global economies require HR practitioners to live up to the challenges and expectations in HR functions. The aim of this study was to establish if HR practitioners in NZ are equipped with the capabilities for the challenges and expectations of HR effectiveness. Data was collected via an e-survey questionnaire, from 364 members of the Human Resource Institute of New Zealand. The response rate was 41%. Five closely related HR themes were focused on in the research and three are discussed in this paper due to paper limit. Findings show high positive results in self-identified HR capabilities. This signifies that HR practitioners in NZ possess capabilities that can increase HR effectiveness.
The end of model democracy ? An editorial comment

Imagine you are hosting a garden party tomorrow and you are trying to decide whether or not to put up a tent against the rain. You read the weather forecast in the newspaper and you ask the farmer next door, and you look at the sky (knowing that persistence is often not a bad weather forecast). So you get three predictions, but how would you aggregate them? Would you average them with equal weight? You might trust the forecast model more (or less) than the farmer, not because you understand how either of them generates their prediction, but because of your past experience in similar situations. But why seek advice from more than one source in the first place? We intuitively assume that the combined information from multiple sources improves our understanding and therefore our ability to decide. Now having read one newspaper forecast already, would a second and a third one increase your confidence? That seems unlikely, because you know that all newspaper forecasts are based on one of only a few numerical weather prediction models. Now once you have decided on a set of forecasts, and irrespective of whether they agree or not, you will have to synthesize the different pieces of information and decide about the tent for the party. The optimal decision probably involves more than just the most likely prediction. If the damage without the tent is likely to be large, and if putting up the tent is easy, then you might go for the tent in a case of large prediction uncertainty even if the most likely outcome is no rain. Although it may seem far-fetched at first, the problem of climate projection is in fact similar in many respects to the garden party situation discussed above. So far, projections from multiple climate models were often aggregated into simple averages, standard deviations and ranges. One example is the recent Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC),
Natural Language Understanding as a classification process : report of initial experiments and results

In this paper, we show how Natural Language Understanding can be modeled as a classification task, regardless of the desired output format (an utterance, a logical formula or a frame). Named entities (and synonyms) can be easily incorporated in the whole process. Several scenarios are used in our experiments, corresponding to the different typical outputs of Natural Language Understanding systems: in the first, given an utterance, the system is supposed to identify the most similar utterance from a pre-defined database of utterances; in the second, the system aims at mapping a given utterance into a logical form; in the third, the system creates a frame from a given utterance.
Patch-Based Reconstruction of a Textureless Deformable 3D Surface from a Single RGB Image

We propose a deep learning method for reconstructing a textureless deformable 3D surface from a single RGB image, under various lighting conditions. One of the challenges when training a neural network to predict the shape of a deformable object is that the object exhibits such a great deal of shape variation that it is essentially impractical to have a training set consisting of all possible deformations the object may realize. However, different areas of the deformable object may exhibit similar types of deformations, e.g. similar wrinkles might appear in different areas on the surface of a cloth. Motivated by this, we propose learning local models of shape variation from image patches that we then combine into a global reconstruction of the observed object. Initially, we divide the input image into overlapping patches and a zero-mean depth map as well as a normal map are estimated for each patch using deep learning. Stitching of depth maps is performed by finding the optimal translation of each patch depth map along the viewing direction of the camera and averaging the depth predictions of neighboring patches at their overlapping areas. Stitching of normal maps is performed by normalizing and averaging the normals predictions of neighboring patches at their overlapping areas. Finally, bilateral filtering is performed on the stitched depth and normal maps in order to perform fine-scale smoothing at the regions around patch boundaries. We show increased accuracy compared to previous work even in the presence of limited training data and more effective generalization to unseen objects.
Scalability and Performance for Data Management in the Cloud

Online generated content, combined with new scientific applications, and the growing traditional data warehouses create today a data explosion problem. However, as all hardware components are lagging behind data growth, waiting for the next hardware generation to solve today’s problem is not an option. It has become clear in recent years that data management systems need to become parallel and employ an increasing number of servers to be able to keep up the pace. In this paper we review existing systems capable of handling large scale data, starting with Gamma[7], one of the first and most influential parallel databases, up to modern data management frameworks such as MapReduce[6] and HadoopDB[2]. We explain how query processing is performed in such systems and identify their strong points and weaknesses. Finally we argue that existing systems still have important drawbacks in handling general workloads and propose future research on creating a data management system capable of combining the benefits of the best systems existing today.
aiai at FinSBD task: Sentence Boundary Detection in Noisy Texts From Financial Documents Using Deep Attention Model

This paper describes how we tackle the FinSBD-2019 shared task in IJCAI-2019. The deep attention model based on word embedding is proposed to detect the sentence boundary in noisy English and French texts extracted from the financial documents. The experiment is shown that the model has good performance for predicting beginning and end index of sentence in the two tasks which the result achieved the score of F1 for BS, ES are 0.88,0.91 respectively in English task, and score of F1 for BS, ES are 0.91, 0.92 respectively in French task.
Analysis with Filtering Vulgarity & Ranking System Based On Transaction Id and OTP

Mrs. S. Vidya Assistant Professor Department Of Computer Science and Engineering St.Joseph’s Institute Of Technology India ABSTRACT Recommender systems represent technologies that assist users in finding a set of interesting or relevant items, typically by predicting the “rating” (i.e., an indicator of preference) that users would give to an item they had not yet considered. Recommender systems play an important role in a variety of settings, most prominently ecommerce. Many companies, including Amazon, Netflix, and Pandora, use recommender systems to suggest alternative or cross-selling products to their customers. For example, Netflix has reported that roughly 75 percent of what their subscribers watch (including both DVDs by mail and videos streamed online) has been recommended to customers by its recommender system. Therefore, offering good recommendations to customers is critical in order to retain users and can also contribute to the enhancement of product Reviews, product ranking, opinion mining, POS, xml documents