Miracle-dz/UniFAR-FTUs huggingface dataset:
DO QMS AUDITORS NEED FURTHER EDUCATION? VIEWS FROM PANEL OF EXPERTS AND CERTIFICATION BODIES

This paper investigates and highlights the similarities and differences of perspectives between the panel of experts and certification bodies on the need for a further and wider education of QMS external quality auditors (EQAs). Two studies were conducted for this purpose. For the first study, a Delphi study was conducted involving panel of experts from Australia, New Zealand, Malaysia and India as respondents. The second study was carried out on accredited certification bodies in Malaysia. Thematic analysis was used to analyze the qualitative part of the findings from the studies. Based on the analysis, there are similarities and differences between the two groups on several aspects of the study. Both of the groups agree that there is a need to improve EQAs education, as most experts reported the quality of audit to be variable, inconsistent, poor and diminishing in value while the certification bodies received complaints about the competency of auditors. This paper contributes to the body of knowledge in the field of quality management system and audit where such work is still limited.
Improving BiDAF on SQuAD 2.0

Question Answering (QA) is a task in which a model is given a paragraph as well as a question about that paragraph, and whose goal is to correctly answer the question using a chunk of text from the paragraph, or in some cases, identify that the question is unanswerable given the paragraph. In this project, I aim to improve upon a baseline question answering model based on Bi-Directional Attention Flow (BiDAF) [1] that does not include a character-level embedding layer, and that is evaluated on SQuAD 2.0 [2]. The first improvement I made was implementing character-level embeddings and concatenating them with the baseline word-level embeddings, similar to that in the original BiDAF paper. I then modified the existing BiDAF model’s attention layer to include self attention, trying different hyperparameters along the way. As a final step, I evaluated these changes on SQuAD 2.0 compared to the baseline model, and found that while adding character embeddings improved my performance compared to the baseline, my method of self-attention was not as successful.
ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective

Traditional deep learning interpretability methods which are suitable for model users cannot explain network behaviors at the global level and are inflexible at providing fine-grained explanations. As a solution, concept-based explanations are gaining attention due to their human intuitiveness and their flexibility to describe both global and local model behaviors. Concepts are groups of similarly meaningful pixels that express a notion, embedded within the network's latent space and have commonly been hand-generated, but have recently been discovered by automated approaches. Unfortunately, the magnitude and diversity of discovered concepts makes it difficult to navigate and make sense of the concept space. Visual analytics can serve a valuable role in bridging these gaps by enabling structured navigation and exploration of the concept space to provide concept-based insights of model behavior to users. To this end, we design, develop, and validate ConceptExplainer, a visual analytics system that enables people to interactively probe and explore the concept space to explain model behavior at the instance/class/global level. The system was developed via iterative prototyping to address a number of design challenges that model users face in interpreting the behavior of deep learning models. Via a rigorous user study, we validate how ConceptExplainer supports these challenges. Likewise, we conduct a series of usage scenarios to demonstrate how the system supports the interactive analysis of model behavior across a variety of tasks and explanation granularities, such as identifying concepts that are important to classification, identifying bias in training data, and understanding how concepts can be shared across diverse and seemingly dissimilar classes.
Exploring the effects of semantic tag augmentations for SQuAD

Question-Answering is a broadly researched NLP problem which has seen many contributions. Many publications that approach the QA task leverage an attention mechanism of some kind. In our paper, we explore the effectiveness of augmenting three different models which use attention with character-level and semantic embeddings, and examine the qualitative effects on their attention behaviors
Disentangling the Attractiveness of Telework to Employees: A Factorial Survey Experiment

This research adds to the literature on the attractiveness of telework to employees. To this end, we set up an innovative factorial survey experiment in which a high-quality sample of employees evaluates job offers with diverging characteristics, among which a wide variation in telework possibilities. We find that the relationship between the possibility to telework and job attractiveness is approximately linear: 10 percentage points more telework hours yield a rise of 2.2 percentage points in job attractiveness and, therefore, the willingness to give up an increase of 2.3 percentage points in wage in the new job. Our experimental design also allows us to investigate the underlying mechanisms of this relationship as well as its moderators. We find that the attractiveness of telework is particularly explained by expectations of an improved work-life balance, more work scheduling autonomy, a higher job satisfaction, and more work methods autonomy in jobs with a greater possibility to telework. In addition, our analyses show that less conscientious employees are on average more attracted to jobs with greater telework possibilities, so that it is important that self-selection in jobs with more telework is well-monitored.
Features and Methods of Making Nanofibers by Electrospinning, Phase Separation and Self-assembly

One of the major challenges in the field of tissue engineering is the production of scaffolding in nano-scale. The study of structural-functional connections in pathological and normal tissues with biologically active alternatives or engineered materials has been developed. Extracellular Matrix (ECM) is a suitable environment consisting of gelatin, elastin and collagen types I, II and III, etc., which are provided to cells for wound healing, embryonic development, cell growth and organogenesis, and. They also play a role in transmitting structural integrity and overall strength to tissues. In tissues, ECM manufacturers are structurally 50 to 500 nm in diameter; nanotechnology must be used to create scaffolds or ECM analogues. Recent advances in nanotechnology have led to the development of ECM-engineered analogues in various ways. To date, three self-assembly, phase separation and electrospinning techniques have been developed to activate nanofiber scaffolds. With these advances and the construction of a "biomimetic" environment, engineered tissue or scaffolding is now possible for a variety of tissues. This study will discuss the three existing methods for creating Tissue engineering scaffolds that are able to mimic new tissue, as well as the discovery of materials for use in scaffolding.
SQuAD-ing Up as a Winning Team: Character Embeddings and Dynamic Coattention Networks

Recent developments in the Question Answering space, a large interest in NLP, have been facilitated by both the advancement of deep learning models as well as more robust datasets, such as the Stanford Question Answering Dataset (SQuAD) [1]. In this paper, we seek to improve upon the provided baseline Bi-directional Attention Flow (BiDAF) model that only includes word-level embeddings. We first add character-level embeddings in addition to the provided word-level embeddings and implement the coattention mechanism described by Xiong et al. to replace the attention layer in BIDAF [2] [3]. We found that adding character-level embeddings as an input exceeds baseline performance. However, the combination of the Dynamic Coattention implementation and character embeddings only improved on the F1 scoring while scoring lower on the EM score when using the default hyperparameters. After model testing and a hyperparameter search, the Coattention-Layer BIDAF model with both char-level embeddings and word-level embeddings had the best performance with an EM score of 59.138 and an F1 score of 62.704 on the test set when using a learning rate of 0.6 and a dropout probability of 0.15.
Buckling length factors of hollow section members in lattice girders

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
Automatic Security Patching for Containerized Java Applications

Containers have become increasingly popular in distributed computing environments. However, recent studies have shown that con-tainerized applications are susceptible to various security attacks. We need to address underlying software vulnerabilities in these applications. Existing research has attempted to analyze the underlying software bugs with various static and dynamic approaches to extract vulnerability patterns in the application. However, these patterns can vary among applications and libraries. In addition, many solutions do not address the operational constraints and objectives of distributed infrastructures. In this work, we explore static program analysis to find and understand unique code patterns in the core Java libraries that are vulnerable to security attacks. Thereafter, we study how to deliver appropriate patches to Java applications in distributed container clusters.
Climate Sensitivity is Sensitive to Changes in Ocean Heat Transport

Do changes in ocean heat transport (OHT), that occur with CO2 forcing, impact climate sensitivity in Earth system models? Changes in OHT with warming are ubiquitous in model experiments: when forced with CO2, such models exhibit declining poleward OHT in both hemispheres at most latitudes, which can persist over multi-centennial time scales. To understand how changes in OHT may impact how the climate system responds to CO2 forcing, particularly climate sensitivity, we perform a series of Earth system model experiments in which we systematically perturb OHT (in a slab ocean, relative to its pre-industrial control climatology) while simultaneously doubling atmospheric CO2. We find that equilibrium climate sensitivity varies substantially with OHT. Specifically, there is a 0.6 K decrease in global mean surface warming for every 10% decline in poleward OHT. Radiative feedbacks from CO2-doubling, and the warming attributable to each of them, generally become more positive (or less negative) when poleward OHT increases. Water vapor feedback differences account for approximately half the spread in climate sensitivity between experiments, while differences in the lapse rate and surface albedo feedbacks account for the rest. Prescribed changes in OHT instigate opposing changes in atmospheric energy transport and the general circulation, which explain differences in atmospheric water vapor and lapse rate between experiments. Our results show that changes in OHT modify atmospheric radiative feedbacks at all latitudes, thereby driving changes in equilibrium climate sensitivity. More broadly, they demonstrate that radiative feedbacks are not independent of the coupled (atmosphere and ocean) dynamic responses that accompany greenhouse gas forcing.
Servlet And Jsp

Jsp and Servlet are often used in the WEB development, based on SUN's Java syntax rules, on the basis of most of the time in accordance with the requirements in the face of developing interface directly invoke methods, for the underlying principle and realization process of the Servlet and Jsp not to delve into. In order to more clear thorough understanding of both the clear division of labor in the WEB Container and the work process, we analysis the implementation process of the Servlet and Jsp, make application to the Servlet and Jsp development projects more handy.
Capturing Unit Startup and Shutdown Uncertainties in the Real-time Commitment Process

—Generation uncertainties, especially during the unit startup and shutdown (SU/SD) processes, pose uncertainties for the real-time market clearing process, and they are often underestimated. This paper proposes two approaches to predict generator SU/SD trajectories in the real-time operations of independent system operators or regional transmission organizations (ISO/RTOs). We ﬁrst collect and pre-process raw market data from state estimation. Then we investigate two approaches to account for the uncertainty in MW of generation SU/SD in the real-time market clearing. The ﬁrst is an ofﬂine approach that leverages a machine learning technique, gradient boosting tree, to effectively capture the nonlinear relationship between the SU/SD curves and selected feature maps. The ofﬂine approach works for predicting generator trajectories in the real-time Look Ahead Commitment (LAC) process, based on historical data. We also investigate an online approach using a long-short-term memory network that can learn from the last-interval error information and enhance the current prediction, potentially applicable for the real-time economic dispatch process. We validate the benefit of the proposed approach with a full-day rolling LAC framework on MISO-size test cases. The result shows that using the predicted curves could help system operators achieve better results in real-time commitment and dispatch processes.
Comparison of NLP Models for a Writer and Genre Style Controlled Movie Screenplay Generator

Our primary motivating question is to determine how do various NLP model architectures compare when tasked to generate a movie screenplay in the artistic style of both a user-inputted genre and movie director? There has been some previous work in long text generation, but we are building various models on a novel application of having multiple user-inputted stylistic dimensions to control in generative text. Crafting a movie screenplay is a very challenging and difficult task; however, we hope that our work will be able to aid writers in their goals of screenplay writing, bringing stylistic and creative ideas from previous directors’ work in a genre they desire. We compare various models and approaches, particularly OpenAI’s GPT-2 language model along with the DistilGPT-2 model, ultimately finding DistilGPT-2 under 10 epochs yields the best screenplay generation. Our future work might investigate LeakGAN and Aggressive Variational Autoencoder (VAE) models for text generation. We compare our model performances against a baseline LSTM architecture.
Prompt-based model editing

SERAC [1] is a retrieval-based algorithm for editing pretrained models which achieves state-of-the-art performance in question-answering, natural language inference, and sentiment modulation editing tasks. However, while performance of language models scales with the number of parameters in many tasks [2, 3], SERAC’s performance does not scale with the size of the base model being edited due to its decoupling of its base and counterfactual models. To this end, we propose a retrieval-and-continuous-prompting editing algorithm which replaces SERAC’s counterfactual model with a prompter network to predict continuous prompts which reliably modulate the base model’s outputs. We show that the prompt-based editor rivals the performance of SERAC on natural language inference and question-answering editing tasks. Empirically, our method shows promise in being able to generalize to unseen base model architectures and to reliably modulate language model outputs.
Level measurements with computer vision - comparison of traditional and machine learning computer vision methods

In this work, modern machine learning methods are compared against traditional image processing techniques, for the purpose of estimating the level of coffee beans in a transparent tank ﬁtted to a coffee machine. Measurements using both approaches are compared against manual level measurements. The resulting algorithm are analysed for repeatability under scene variations, such as orientation of the tank with respect to the camera and the distribution of coffee beans.
Dosage optimisation of magnetically retrievable zinc ferrite nanoparticles for photodegradation of methylene blue

In this paper, we report the studies on photocatalytic activity of the zinc ferrite nanoparticles for the degradation of methylene blue dye. The study reveals that an optimum minimum dosage of 1g/L can give a degradation efficiency of 89% within 90 minutes of its exposure to the sunlight. Zinc ferrite nanoparticles were synthesised using co-precipitation method in stoichiometric ratio. Structural, magnetic and optical characterisation were done using XRD, FTIR, VSM and UV-VIS-IR analysis which confirmed the crystalline, magnetic and visible light active nature of the sample. Feasibility for magnetic retrieval of the photocatalyst was studied after photodegradation of dye and then analysed using XRD which confirmed the phase purity of the sample even after retrieval.
Debiasing Models, Dataset Augmentation and MoE for Out-of-domain Generalization

Question answering (QA) has gained wide significance in recent years with the rise of chatbots and more powerful search engines, but the performance of current models is still quite limited in domain adaptation settings in which models which are trained on source training domains are tasked with generalizing to new target data distributions where training data is limited. In this work, we hypothesize that in QA settings, models fail to generalize to new data domains because they have overfit to spurious correlations in the source domain training sets which do not generalize to the target data distributions. Motivated by this hypothesis, we implement several approaches to prevent QA models from overfitting to spurious correlations – dataset augmentation with synonym replacement, debiasing techniques to prevent models from using biased features during training, and lastly, ensembling methods which combine multiple trained models with a mixture of experts. Early results show that the synonym augmentation approach improves upon the several baselines, whereas debiasing approaches does not achieve improved performance. We explore several reasons for these results and discuss future steps to improve performance in domain generalization for question answering.
New Evidence on the 2007 Parental Beneﬁts Reform in Germany: Selection and Reform Eﬀects

This paper studies the causal eﬀect of giving ﬁrst birth and how that eﬀect changes by the 2007 reform in parental beneﬁts in Germany. We further investigate how the reform aﬀects the selection of women into motherhood and how that selection eﬀect aﬀects labor market outcomes. A large novel data set merging data from the pension insurance with administrative labor market data provides information on all births. We apply a dynamic treatment eﬀect approach which diﬀers from other strategies used so far in most of the literature to estimate the causal eﬀect of motherhood and to evaluate the 2007 reform. The reform has positive medium-run eﬀects on earnings and employment. There are no eﬀects on second-order fertility and on full-time employment. While the reform slightly changes the selection of mothers, this has little impact on the reform eﬀect for the causal eﬀect of motherhood. The the 2000er Due lower intital level, it does not show the large decline form to 2012.
Exploiting satellite measurements to explore uncertainties in UK bottom-up NOx emission estimates

Nitrogen oxides (NOx , NO+NO2) are potent air pollutants which directly impact on human health and which aid the formation of other hazardous pollutants such as ozone (O3) and particulate matter. In this study, we use satellite tropospheric column nitrogen dioxide (TCNO2) data to evaluate the spatiotemporal variability and magnitude of the United Kingdom (UK) bottom-up National Atmospheric Emissions Inventory (NAEI) NOx emissions. Although emissions and TCNO2 represent different quantities, for UK city sources we find a spatial correlation of ∼ 0.5 between the NAEI NOx emissions and TCNO2 from the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI), suggesting a good spatial distribution of emission sources in the inventory. Between 2005 and 2015, the NAEI total UK NOx emissions and long-term TCNO2 record from the Ozone Monitoring Instrument (OMI), averaged over England, show annually decreasing trends of 4.4 % and 2.2 %, respectively. Top-down NOx emissions were derived in this study by applying a simple mass balance approach to TROPOMI-observed downwind NO2 plumes from city sources. Overall, these top-down estimates were consistent with the NAEI, but for larger cities such as London and Birmingham the inventory is significantly (> 25 %) less than the top-down emissions.
Inkjet and Aerosol Jet Printing of Electrochemical Devices for Energy Conversion and Storage  

Inkjet and aerosol jet printing have recently emerged as promising fabrication techniques for a broad range of devices for electrochemical energy conversion and storage – batteries, fuel cells, and supercapacitors. If fully realized, these printing techniques may enable device performance advantages accruing from precise micron scale patterning, thin layer deposition, and materials grading. Printing may also allow scalable, low materials waste manufacturing, and conformal integration of power elements into structural elements. This article reviews the fundamental capabilities of inkjet and aerosol jet printing relevant to electrochemical devices, surveys current literature, and presents future challenges which must be tackled to achieve high performance, printed electrochemical energy storage, and conversion devices.
Building QANet from Scratch

Question answering models have been the standard in NLP for demonstrating reading comprehension, and recent developments in model architecture have greatly increased performance. One of the most important innovations has been the application of the transformer architecture to question answering through the QANet architecture. In this paper, I describe my own implementation of QANet based on the original model. My model was able to improve performance on the Stanford question answering dataset when compared to a standard BiDAF model, despite issues with overfitting that decreased test performance. Through analysis of my model results, I have identified potential limitations of QANet, and found that it trains more slowly than recurrent models when limited by memory and computational power.
Why the quantum equilibrium hypothesis? From Bohmian mechanics to a many-worlds theory

The status and justiﬁcation of the quantum equilibrium hypothesis (QEH) in Bohmian mechanics is “a rather delicate matter”. In this paper, I present a new analysis of this hypothesis. It is argued that the QEH should be regarded not as a mere initial condition but more appropriately as one part of the law of motion. Moreover, it is more reasonable and simpler to assume that the QEH holds true consistently at all times. The resulting theory is a many-worlds theory of random discontinuous motion of particles in three-dimensional space. This new theory agrees with experiments, and it is arguably the simplest realist version of quantum mechanics.