Miracle-dz/UniFAR-FTUs huggingface dataset:
Programming Techniques – Project Report

Specification The purpose of this project is take an existing object-oriented (OO) system (Webb & White, 2003) written using a proprietary software development system, Rational Rose RealTime (RRT), and convert key parts of it to generic C and C++. The system is an executable model of a biological cell. It contains an inheritance hierarchy (see Figure 1), as well as a containment hierarchy (see Figure 2).
Event Marketing: Measuring an experience?

The research presents a critical evaluation of the current methods used to measure the effectiveness of experiential marketing techniques. The paper begins by reviewing the literature relating to event and experiential marketing and related evaluation techniques. Secondary research is then used to highlight specific tools and methods currently being used in the experiential marketing industry. Interviews were conducted with providers of marketing events and show that current methods are not seen as comprehensive or reliable due to the intangibility of the event experience. Based on these findings a framework is developed to guide future research, both academic and practitioner, into measuring the effectiveness of experiential marketing events.
Review on The Research of Online Reviews’ Perceived Credibility

Online review has become one of the most important references for modern consumers to purchase products or service ． Based on combing the related literature on credibility of internet public opinions, this paper gives a summary of influence factors of perceived credibility of online reviews through three aspects: the credibility of source, the credibility of channel and the credibility of information. From the angle of online reviews information structure characteristics (including additional reviews vs. one-time reviews), this paper aims to discuss which one has higher perceived credibility, and information quality adjust the differences in perceived credibility.
COMPARATIVE ANALYSIS OF REAL-TIME MULTI-VIEW RECONSTRUCTION OF A SIGN LANGUAGE INTERPRETER

Following the proliferation of smart handheld devices coupled with the launch of HbbTV 2.0 specification there is an emerging trend among broadcaster organisations to enable a higher degree of content accessibility across disabled communities. In this respect, the sign language interpreters play a crucial role in facilitating accessibility to main stream broadcaster content for the deaf community. Addressing the increasing demand placed on sign language interpreters and current limitations on delivering sign language content complementary to main stream media, this paper proposes the use of a low-cost 3D studio environment which enables photorealistic reconstruction of human avatars that captures the interpreter while eliminating the background. The core novelty of the proposed approach relies on the information fusion framework for correlating multi-sensor RGB-Depth sensors. The performance of the proposed approach for the reconstruction of a sign language interpreter has been evaluated using two low-cost sensors namely Kinect V2 and RealSense D435.
MALDI-TOF identification of Campylobacter isolated from patients consulted in private laboratories 
in France

Campylobacter is a major agent of gastroenteritis worldwide. The incidence and prevalence of campylobacteriosis have been increasing in both developed and developing countries over the last decade. In this study, 197 strains of successive Campylobacter-like were identified in French laboratories in September 2018. Bacterial isolates from clinical samples were identified with a mass spectrometer (Ultraflex III TOF/TOF and the BIOTYPER database from Bruker Daltonics). Of the 197 isolates tested, 143 were identified as Campylobacter jejuni (72.59%), 28 as Campylobacter coli (14.21%), 2 as Arcobacter butzleri (1.02%), 1 as Campylobacter fetus and 1 as Campylobacter lari with (0.51% each) by the MALDI-TOF mass spectrometry. Isolation rate of Campylobacter was highest in the 0 - 9 age group (22%). The proportion of male and female patients was 59.4% (CI 95% = 52.2-66.3) and 40.6% (CI 95% = 33.7-42.8) respectively. Sixty strains (30.5%) were resistant to tetracycline and 52 (26.4%) resistant to ampicillin. This study showed that the MALDI-TOF mass spectrometry is a rapid and accurate identification method of Campylobater spp in patients treated in private French laboratories. Key words: Campylobacter, identification, MALDI-TOF, patients, France.
The Norwegian National Ground Segment; Preservation, Distribution and Exploitation of Sentinel Data

In order to take advantage of the Sentinel program, the Norwegian Space Agency decided to establish a national collaborative ground segment for satellite data with the purpose of simplifying data access, ensure support for operational national services and long term preservation of data. This is the NBS where MET Norway has the technical responsibility in terms of providing the infrastructure and storage capacity for data management. Serving the data through two separate platforms, the end users have access to the data in its original format in addition to Sentinel-1 and Sentinel-2 products in NetCDF-4/CF. Using the latter format, services like regridding, subsetting, visualization and aggregation are integrated utilizing OPeNDAP in combination with OGC WMS and OGC WPS. In addition, data uploading and retrieving operations are simplified for an end user since streaming of data by means of OPeNDAP is supported in multiple programming languages. Due to the strong coupling between space based earth observations, in-situ observation, model data etc, disseminating data in a generic data management system utilizing NetCDF-4/CF and OPeNDAP is convenient for seamless integration across branches. However, the current CF version is not mature for handling all parts of the Sentinel data but future development looks very promising.
Upscaling of small-scale heterogeneities to flow units for reservoir modelling

The effect of small-scale heterogeneities on fluid flow through a reservoir is determined using one of so called mathematical upscaling methods, that as such are fairly well established. Less well established, however, are the operations that provide their input, i.e. construction of reservoir model and acquisition of data. We thereto propose systematic description of facies and bed and systematic data sampling. We upscale the ensuing model and data through a two-step flow simulation, i.e. first on the flow cell and then on the flow unit scale. This work provides for a straightforward and easily applicable upscaling method that incorporates the effects of all heterogeneities. r 2006 Elsevier Ltd. All rights reserved.
Priming the Pump: Does Aid Pave the Way for Investment?

Recent advances in the coverage and precision of development data have opened exciting new avenues for analyzing the political economy behind the allocation and effectiveness of foreign aid. While a number of recent papers have looked at the social, environmental or welfare implications of aid, this paper instead focuses on how aid impacts an intermediate outcome in the development process, foreign direct investment (FDI). Combining geo-referenced data on foreign aid with a similarly coded dataset of FDI project locations, the paper uses a quasi-experimental, spatial-temporal, identification strategy to evaluate if the location of foreign aid projects presages later FDI. Drawing on the literature on the political economy of aid, the paper develops theoretical expectations about a given donor’s aid and FDI projects from that state before finding that local aid increases the chance of a location attracting FDI by up to 40 percent. While aid and FDI from bilateral donors goes hand-in-hand, both Chinese and EU aid also attracts FDI from other sources.
Asian Transport Studies, Volume 5, Issue 3 (2019), 480–492

This study aims to investigate important factors affecting intention for ceasing driving in the local city in Japan. 321 elder driver data collected in Toyota City are used for empirical analysis. One trivariate ordered probit model is used to measure anxiety about driving, the intention for continuing driving and that for surrendering driving licenses in one framework. The Gibbs Sampler algorithm is applied to estimate the unknown parameters. The major findings indicate: 1) elder drivers more than 74 years old are willing to return their driving licenses compared to elder drivers between 69 and 74 years old; 2) elder drivers liking to drive are unwilling to cease driving compared to other drivers; 3) there is a synergetic effect between anxiety about driving with intention for surrendering driving licenses; and 4) there is a complementary effect between intention for continuing driving and that for returning driving licenses as we expected.
Application of a CFD-based tool to optimize an industrial pultrusion process

Pultrusion is one of several manufacturing processes for producing reinforced polymer composites. In this process, fibers are continuously pulled through a resin bath and after impregnation, the fiber-resin assembly is cured in a heated mold. In order to obtain a composite uniformly cured with minimum energy rate and high production rate, it is necessary an optimization procedure to calculate it. In this work, we propose a CFD-based tool for optimization of a typical industrial pultrusion process. The purpose is to increase production rate of composite material without affecting the quality material and, at the same time, to minimize energy rate consumed during the reaction. To do so, the CFD approach was applied to the energy and mass balance and kinetic equations coupled to an external optimization algorithm to maximize the objective function, that is written as the minimum energy rate. The algorithm used for this purpose was the particle swarm optimization, PSO, that presents a stochastic strategy to search the optimal point. At each PSO iteration, the CFD model is used as a subroutine in order to evaluate the degree of cure and temperature profile required for objective function evaluation. We demonstrated that the proposed optimization strategy was successfully in optimizing the industrial pultrusion plant by reducing energy cost and increasing production rate, showing that this process may be considerably optimized if the kinetic behavior is well known. The proposed algorithm has shown a powerful strategy to future implementations for pultrusion optimization and also may be applied to other composite processes.
GAIN-SCHEDULED L2 CONTROL OF DISCRETE-TIME POLYTOPIC TIME-VARYING SYSTEMS

L. A. Rodrigues, larpig@gmail.com School of Mechanical Engineering, University of Campinas UNICAMP J. De Caigny, jan.decaigny@mech.kuleuven. be Department of Mechanical Engineering, Katholieke Universiteit Leuven J. F. Camino, camino@fem.unicamp.br School of Mechanical Engineering, University of Campinas UNICAMP P. L. D. Peres, peres@dt.fee. unicamp.br School of Electric and Computing Engineering, University of Campinas UNICAMP
Neighborhood Overlapped Propagation Algorithm For Community Neighborhood Overlapped Propagation Algorithm For Community Detection Based On Label Time-Sequence Detection Based On Label Time-Sequence

The community detection algorithms based on label propagation (LPA) receive broad attention for the advantages of near-linear complexity and no prerequisite for any object function or cluster number. However, the propagation of labels contains uncertainty and randomness, which affects the accuracy and stability of the LPA algorithm. In this study, we propose an efficient detection method based on COPRA with Time-sequence (COPRA_TS). Firstly, the labels are sorted according to a new label importance measure. Then, the label of each vertex is updated according to time-sequence topology measure. The experiments on both the artificial datasets and the real-world datasets demonstrate that the quality of communities discovered by COPRA_TS algorithm is improved with a better stability. At last some future research topics are given.
A Joint Many-Task Model: How to Grow a Neural Net for Multiple NLP tasks

Transfer and multi-task learning have traditionally focused on either a single 1 source-target pair or a few, similar tasks. Ideally, the linguistic levels of morphol-2 ogy, syntax and semantics would beneﬁt each other by being trained in a single 3 model. We introduce such a joint many-task model together with a strategy for 4 successively growing its depth to solve increasingly complex tasks. All layers in-5 clude short-circuit connections to both word representations as well as lower-level 6 task predictions. We use a simple regularization term to allow for optimizing all 7 model weights to improve one task’s loss without exhibiting catastrophic forgetting 8 of the other tasks. Our single end-to-end trainable model obtains state-of-the-art 9 results on chunking, dependency parsing, semantic relatedness and entailment. It 10 also performs competitively on POS tagging. Our dependency parsing component 11 does not require a beam search or a re-ranker. 12
Metric Learning-Based Multimodal Audio-Visual Emotion Recognition

People express their emotions through multiple channels, such as visual and audio ones. Consequently, automatic emotion recognition can be significantly benefited by multimodal learning. Even-though each modality exhibits unique characteristics; multimodal learning takes advantage of the complementary information of diverse modalities when measuring the same instance, resulting in enhanced understanding of emotions. Yet, their dependencies and relations are not fully exploited in audio–video emotion recognition. Furthermore, learning an effective metric through multimodality is a crucial goal for many applications in machine learning. Therefore, in this article, we propose multimodal emotion recognition metric learning (MERML), learned jointly to obtain a discriminative score and a robust representation in a latent-space for both modalities. The learned metric is efficiently used through the radial basis function (RBF) based support vector machine (SVM) kernel. The evaluation of our framework shows a significant performance, improving the state-of-the-art results on the eNTERFACE and CREMA-D datasets.
Clustering Large-scale Diverse Electronic Medical Records to Aid Annotation for Generic Named Entity Recognition

The full extent of diversity in clinical documents and the effects on natural language processing (NLP) tasks in the medical domain have not been well studied. In supervised NLP tasks, it is vital to have training data that resembles test data [27]. In the medical domain, this often translates to uniform subject matter distribution [16]. We have access to a corpus of 157 million documents from 42 different electronic medical record (EMR) vendors, with over 40,000 distinct categories assigned to the documents. The sheer diversity of the documents is an obstacle to an accurate sub-sampling of the data for annotation. We propose that clustering clinical text documents is an effective way to aid the annotation effort and ensure coverage. We demonstrate the effect of lack of coverage in training data for a supervised generic named entity recognition(GNER) task and the impact of clustering on the task. We will also examine the characteristics of clusters generated from a diverse dataset.
Dirty Clicks: A Study of the Usability and Security Implications of Click-related Behaviors on the Web

Web pages have evolved into very complex dynamic applications, which are often very opaque and difficult for non-experts to understand. At the same time, security researchers push for more transparent web applications, which can help users in taking important security-related decisions about which information to disclose, which link to visit, and which online service to trust. In this paper, we look at one of the simplest but also most representative aspect that captures the struggle between these opposite demands: a mouse click. In particular, we present the first comprehensive study of the possible security and privacy implications that clicks can have from a user perspective, analyzing the disconnect that exists between what is shown to users and what actually happens after. We started by identifying and classifying possible problems. We then implemented a crawler that performed nearly 2.5M clicks looking for signs of misbehavior. We analyzed all the interactions created as a result of those clicks, and discovered that the vast majority of domains are putting users at risk by either obscuring the real target of links or by not providing sufficient information for users to make an informed decision. We conclude the paper by proposing a set of countermeasures.
Screening for community-acquired strains of methicillinresistant Staphylococcus aureus susceptible to extracts of Centaurea nigrescens

The rates of infection by community-acquired multi-drug resistant Staphylococcus aureus have risen dramatically over fifteen years in the United States. Community-acquired multi-drug resistant Staphylococcus aureus is responsible for rapidly progressive diseases, including necrotizing pneumonia, severe sepsis, and necrotizing fasciitis. Consequently, novel antibacterial strategies are needed to combat the rising antibiotic resistance seen in community-acquired multi-drug resistant strains. We have screened the Nebraska Transposon Mutant Library for MRSA strains that are either susceptible or resistant to methanol extracts of Centaurea nigrescens leaves and flowers. 10 strains containing mutations affecting transporter proteins were identified as having either significant resistance or susceptibility to Centaurea extract. Insertions in two different drug efflux transporter families have been identified. The EmrB/QacA drug resistance transporter subfamily is a multi-drug efflux pump responsible for the export of toxic molecules from bacteria and yeast. The ABC transporters are involved in drug import and export. These results confirm the effectiveness of the screen as a means for identifying drug-resistance genes affected by the C. nigrescens methanolic extract and suggest a role for drug efflux proteins in the resistance of S. aureus community-acquired multi-drug resistant Staphylococcus aureus to antibacterial plant metabolites
Development of an Internet-Based Collaborative Design System in a Context of Concurrent Engineering

: This work presents a description of the implementation of the WebMachining methodology and system (http://WebMachining.AlvaresTech.com) developed in a context of e-Mfg and Concurrent Engineering, aiming at integrating CAD/CAPP/CAM for the remote manufacturing of cylindrical components through the Internet. The methodology and its implementation are conceived starting from the modelling paradigm based on synthesis of design features, in order to allow the integration among collaborative design (Web-CADbyFeatures), generative process planning (WebCAPP) and manufacturing (WebTurning). The system is implemented in a distributed environment of agents (agents' community)
Trust Me, He’s Not Right for You: Factors Predicting Trust in Network Members’ Disapproval of a Romantic Relationship

ABSTRACT When individuals experience disapproval of their romantic relationship from friends or family members, how do they determine whether they should trust or believe that negative opinion? In this study, we examined a hypothesised model in which level of perceived relationship expertise, level of perceived bias, quality of evidence provided, and level of perceived approval for the romantic relationship from the broader social network predicted levels of trust/distrust in a disapproving opinion. Using hierarchical multiple regression, we found support for the hypothesised model in an online study (N = 173). Contrary to expectations, no differences in the model were found by relationship type (marginalised vs. non-marginalised). Implications and theoretical explanations for the findings are discussed.
Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we 1) describe the methodology used to perform forecasts; 2) evaluate model prediction accuracy against data collected from 10 locations over 4-years; and 3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end-of-season yields (relative root mean square error, RRMSE ~ 20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50 to 64%. Model initial conditions and management information accounted for 1⁄4 of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2=0.88), root depth (R2=0.83), biomass production (R2=0.93), grain yield (R2=0.90), plant N uptake (R2=0.87), soil moisture (R2=0.42), soil temperature (R2=0.93), soil nitrate (R2=0.77), and water table depth (R2=0.41). We concluded that model set-up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment. Disciplines Agriculture | Agronomy and Crop Sciences | Bioresource and Agricultural Engineering | Soil Science | Statistical Models Comments This is a manuscript of an article published as Archontoulis, Sotirios V. , Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez‐Feria et al. "Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt. " Crop Science (2020). doi: 10.1002/csc2.20039. Authors Sotirios V. Archontoulis, Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez-Feria, Laila Puntel, Raziel A. Ordonez, Javed Iqbal, Emily E. Wright, Ranae N. Dietzel, Matthew Helmers, Andy VanLoocke, Matt Liebman, Jerry L. Hatfield, Daryl Herzmann, S. Carolina Córdova, Patrick Edmonds, Kaitlin Togliatti, Ashlyn Kessler, Gerasimos Danalatos, Heather Pasley, Carl Pederson, and Kendall R. Lamkey This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/agron_pubs/623 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:
Identification of Factors Affecting Adoption of Improved Rice Varieties among Smallholder Farmers in the Municipality of Malanville, Benin

Adoption of improved rice varieties is important for improving rice yield in Benin. This study employed a double-hurdle model to assess factors influencing the adoption of improved rice varieties using a cross-sectional dataset of 360 farmers randomly selected in the municipality of Malanville in Benin. Estimation of the first hurdle indicates that education, frequency of extension visits, frequency of farmer-based organization meetings, access to credit, participation in market, off-farm activities, fertilizer use, perception of soil fertility, access to media and ownership of mobile phone play significant roles in adoption decision-making process in the municipality of Malanville. The results from the second hurdle show that the land under improved rice is positively associated with education, rice-farming experience, credit, off-farm activities, and soil fertility. These findings suggest a strong institutional support measures for promoting the improved rice