Miracle-dz/UniFAR-FTUs huggingface dataset:
STUDENTS PREPAREDNESS TOWARDS WRITING A BACHELOR THESIS; A SURVEY-BASED INVESTIGATION

This paper investigates how students perceive their preparedness when they start writing their bachelor thesis. Students at the University of Agder (UiA) will, in their first semester, receive lectures in ethics, academic writing and referencing to equip them for future writing, mostly academic reports. In the Civil Engineering Department, an IMRAD report template is used during their bachelor thesis. The Civil Engineering Department also requires lecturers to use variations of the same template for every report to prepare the students for their bachelor thesis. This paper will use this year’s bachelor students as a case study. In January and February, the students attended a workshop and followed a course titled "TFL200 – system course for engineers". The workshop gave the students a recapitulation in academic writing, referencing, the writing process, project management, and how the template is structured, explaining the template's different parts. TFL200 covered ethics, systems theory, innovation and LCA, sustainability, and academic writing. In the spring semester, two surveys were sent out to third-year bachelor students in Civil Engineering writing their bachelor thesis. The first survey was sent out at the beginning of January, before the students started their course and workshop. This survey investigated to which degree the students feel prepared, motivated, and ready to write their bachelor thesis. The second survey was conducted in late February to evaluate the effects of TFL200 regarding the bachelor project and the workshop, measuring the student's current preparedness, motivation, and to which degree the students feel competent.
EMBEDDING CHANGEMAKER SKILLS AND SOCIAL INNOVATION INTO DESIGN STUDENT LEARNING

Building on entrepreneurship education and the United Kingdom (UK) government review on creativity in business, this paper discusses a case study of a funded project to develop design students' future skills by collaborating on transitioning student projects into start-up venture and commercialisation for the common good. Undergraduate students are shown to gain insights from different faculties and each other’s subject expertise using a variety of set activities throughout the academic year to challenge and support them. The learning for students included scheduled workshops with the possibility of new venture creation based on the new product development (NPD) methodology, student networking and applying their soft skills in a new context. Four workshop tasks (Enterprise, innovation and you, enterprise development, prototyping and venture articulation) were developed for this project and visualised in a route map. The collaboration of product design students with entrepreneurship students has been observed at the commencement stage, during and after the experience to understand their personal development and identify key learnings captured through opinion polls, focus groups and observation. Triangulation resulted through focus group and feedback from wider project team and academics reflecting on learning models in design/engineering versus entrepreneurship/business. This project is timely due to its year-long inter-faculty student experience during a pandemic using and developing ‘changemaker’ competencies and social innovation.
DJM IX : U NSUPERVISED T ASK - AGNOSTIC A UGMEN - TATION FOR I MPROVING R OBUSTNESS

Convolutional Neural Networks (CNNs) are vulnerable to unseen noise on input images at the test time, and thus improving the robustness is crucial. In this paper, we propose DJMix, a data augmentation method to improve the robustness by mixing each training image and its discretized one. Discretization is done in an unsupervised manner by an autoencoder, and the mixed images are nearly impossible to distinguish from the original images. Therefore, DJMix can easily be adapted to various image recognition tasks. We verify the effectiveness of our method using classification, semantic segmentation, and detection using clean and noisy test images.
Enhancing Growth and Glucosinolate Accumulation in Watercress (Nasturtium officinale L.) by Regulating Light Intensity and Photoperiod in Plant Factories

Recent advancements in light-emitting diode technology provide an opportunity to evaluate the correlation between different light sources and plant growth as well as their secondary metabolites. The aim of this study was to determine the optimal light intensity and photoperiod for increasing plant growth and glucosinolate concentration and content in watercress. Two-week-old seedlings were transplanted in a semi-deep flow technique system of a plant factory for 28 days under four photoperiod–light intensity treatments (12 h—266 μmol· m−2·s−1, 16 h—200 μmol· m−2·s−1, 20 h—160 μmol· m−2·s−1, and 24 h—133 μmol· m−2·s−1) with the same daily light integral. The mean values of shoot fresh and dry weights were the highest under the 20 h—160 μmol· m−2·s−1 treatment, although there was no significant difference. Net photosynthesis and stomatal conductance gradually decreased with decreasing light intensity and increasing photoperiod. However, total glucosinolate concentration was significantly higher under 20 h—160 μmol· m−2·s−1 and 24 h—133 μmol· m−2·s−1 compared with 12 h—266 μmol· m−2·s−1 and 16 h—200 μmol· m−2·s−1. The total glucosinolate content was the greatest under 20 h—160 μmol· m−2·s−1 treatment. These data suggest that the 20 h—160 μmol· m−2·s−1 treatment promoted the maximum shoot biomass and glucosinolate content in watercress. This study supplies the optimal light strategies for the future industrial large-watercress cultivation.
USING NARRATIVE ENQUIRY TO INVESTIGATE THE DEVELOPMENT OF STUDENTS’ ENGINEERING IDENTITY IN A DEGREE APPRENTICESHIP

Every year, around 35% of engineering graduates (mainly female and ethnic minority graduates) in the UK choose roles outside engineering (EngineeringUK 2019) [1]. Given that engineering as a profession struggles to attract recruits, this represents a significant loss of qualified talent the profession can ill afford. A possible reason why engineers choose not to practise after qualifying may be that they have not developed a professional engineering identity during their engineering education. Eliot and Turns (2011) [2] define professional identity as “personal identification with the duties, responsibilities, and knowledge associated with a professional role”. Engineering identity is the extent to which students identify themselves as engineers. Research shows that engineering identity is an important indicator of persistence in both engineering education and the engineering profession (Godwin, 2016) [3] (Beam et al 2009) [4] (Cech et al 2011) [5]. The purpose of this research is to find ways of reducing the number of engineers who graduate and then do not practise, by identifying ways in which they can better develop their engineering identity during their studies. The findings presented in this paper are surprising in that they seem to indicate that the four years of a degree apprenticeship have had little impact on students who already had either low or high identification with engineering. Engineering educators should consider how the development of an engineering identity can be supported in engineering degrees.
The effect of nurse case coordination on management of chronic conditions

A clinical decision report using: Ishani A, Greer N, Taylor BC, et al. Effect of nurse case management compared with usual care on controlling cardiovascular risk factors in patients with diabetes: a randomized controlled trial. Diabetes Care. 2011;34(8):1689-1694. https://doi.org/10.2337/dc102121 for a patient requiring care coordination for management of hypertension, diabetes, and hyperlipidemia.
L ATENT O PTIMIZATION V ARIATIONAL A UTOENCODER FOR C ONDITIONAL M OLECULE G ENERATION

Variational autoencoder (VAE) is a generation algorithm, consisting of an encoder and a decoder, and the latent variable is used as the input of the decoder. VAE is widely used for image, audio and text generation tasks. In general, the training of VAE is at risk of posterior collapsing especially for long sequential data. To alleviate this, modified evidence lower bounds (ELBOs) were propsed. However, these approaches heuristically control training loss using a hyper-parameter, and are not way to solve the fundamental problem of vanilla VAE. In this paper, we propose a method to insert an optimization step of the latent variable and alternately update the encoder and decoder for maximizing ELBOs. In experiments, we applied the latent optimization VAE (LOVAE) on ZINC dataset, consisting of string representation of molecules, for the inverse molecular design. We showed that the proposed LOVAE is more stable in the training and achieves better performance than vanilla VAE in terms of ELBOs and molecular generation performance.
Truly Deterministic Policy Optimization

In this paper, we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape. By avoiding noise injection all sources of estimation variance can be eliminated in systems with deterministic dynamics (up to the initial state distribution). Since deterministic policy regularization is impossible using traditional non-metric measures such as the KL divergence, we derive a Wasserstein-based quadratic model for our purposes. We state conditions on the system model under which it is possible to establish a monotonic policy improvement guarantee, propose a surrogate function for policy gradient estimation, and show that it is possible to compute exact advantage estimates if both the state transition model and the policy are deterministic. Finally, we describe two novel robotic control environments -- one with non-local rewards in the frequency domain and the other with a long horizon (8000 time-steps) -- for which our policy gradient method (TDPO) significantly outperforms existing methods (PPO, TRPO, DDPG, and TD3). Our implementation with all the experimental settings is available at https://github.com/ehsansaleh/code_tdpo
Modeling the short-term fire effects on vegetation dynamics and surface energy in Southern Africa using the improved SSiB4/TRIFFID-Fire model

Abstract. Fire causes abrupt changes in vegetation properties and modifies flux exchanges between land and atmosphere at subseasonal to seasonal scales. Yet these short-term fire effects on vegetation dynamics and surface energy balance have not been comprehensively investigated in the vegetation model coupled with the fire module. This study applies the SSiB4/TRIFFID-Fire model to study the short-term fire impact in Southern Africa with comprehensive evaluations of simulated fire regimes, vegetation productivity, and surface fluxes. We find an annual average reduction in grass cover by 4–8 % for widespread areas between 5–20° S and a tree cover reduction by 1 % at the southern periphery of tropical rainforests. The fire effects on regional scales accumulate during June–October and peak in November, the beginning of the rainy season. After the fire season ends, the grass cover quickly returns to unburned conditions before the next fire season, while the tree fraction hardly recovers in one rainy season. The vegetation clearance by fire has reduced the leaf area index (LAI) and gross primary productivity (GPP) by 3–5 % and 5–7 % annually, respectively. The exposure of bare soil has enhanced surface albedo and therefore decreased the absorption of shortwave radiation. Annual mean sensible heat has dropped by 1.4 W m−2 while the latent heat reduction is small (0.1 W m−2) due to the compensating effects between canopy transpiration and soil evaporation. A slight warming effect is simulated after fire, which could be enhanced when the surface darkening effect is incorporated.
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Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. MSDA is a crucial problem applicable to many practical cases where labels for the target data are unavailable due to privacy issues. Existing MSDA frameworks are limited since they align data without considering conditional distributions p(x|y) of each domain. They also do not fully utilize the target data without labels, and rely on limited feature extraction with a single extractor. In this paper, we propose MULTI-EPL, a novel method for multi-source domain adaptation. MULTI-EPL exploits label-wise moment matching to align conditional distributions p(x|y), uses pseudolabels for the unavailable target labels, and introduces an ensemble of multiple feature extractors for accurate domain adaptation. Extensive experiments show that MULTI-EPL provides the state-of-the-art performance for multi-source domain adaptation tasks in both of image domains and text domains.
Predicting Consumers’ Brand Sentiment Using Text Analysis on Predicting Consumers’ Brand Sentiment Using Text Analysis on Reddit Reddit

Abstract With the emergence of data privacy regulations around the world (e.g. GDPR, CCPA), practitioners of Internet marketing, the largest digital marketing channel, face the trade-off between user data protection and advertisement targeting accuracy due to their current reliance on PII-related social media analytics. To address this challenge, this research proposes a predictive model for consumers’ brand sentiment based entirely on textual data from Reddit, i.e. fully compliant with current data privacy regulations. This author uses natural language processing techniques to process all post and comment data from the r/ gadgets subreddit community in 2018 – extracting frequently-discussed brands and products through named entity recognition, as well as generating brand sentiment labels for active users in r/gadgets through sentiment analysis. This research then uses four supervised learning classifiers to predict brand sentiments for four brand clusters (Apple, Samsung, Microsoft and Google) based on the self-identified characteristics of Reddit users. Across all four brand clusters, the predictive model proposed by this research achieved a ROC AUC score above 0.7 (three out of the four above 0.8). This research thus shows the predictive power of self-identified user characteristics on brand sentiments and offers a non-PII-required consumer targeting model for digital marketing practitioners.
S PATIALLY D ECOMPOSED H INGE A DVERSARIAL L OSS BY L OCAL G RADIENT A MPLIFIER

Generative Adversarial Networks (GANs) have achieved large attention and great success in various research areas, but it still suffers from training instability. Recently hinge adversarial loss for GAN is proposed that incorporates the SVM margins where real and fake samples falling within the margins contribute to the loss calculation. In a generator training step, however, fake samples outside of the margins that partially include unrealistic local patterns are ignored. In this work, we propose local gradient ampliﬁer(LGA) which realizes spatially decomposed hinge adversarial loss for improved generator training. Spatially decomposed hinge adversarial loss applies different margins for different spatial regions extending over-all margin space toward all fake samples asymmetrically. Our proposed method is evaluated on several public benchmark data sets compared to state of the art methods showing outstanding stability in training GANs.
Association of Systemic Immune-Inflammation Index With Short-Term Mortality of Congestive Heart Failure: A Retrospective Cohort Study

Purpose: The present study aimed to clarify the potential predictive significance of Systemic immune-inflammation index (SII) in assessing the poor prognosis of critically ill patients with congestive heart failure (CHF). Methods: Detailed clinical data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care III database after gaining access and building the local platform. The 30- and 90-day and hospital all-cause mortalities of the patient was the primary outcome, and the readmission rate and the occurrence of major cardiovascular adverse events (MACEs) were the secondary outcomes. the Cox proportional hazard model and Logistic regression analysis were selected to reveal the relationship between SII level and the research outcome. Further, the propensity score matching (PSM) analysis was performed to improve the reliability of results by reducing the imbalance across groups. Results: There were a total of 4,606 subjects who passed the screening process and entered the subsequent analysis. Multivariate regression analysis showed that after adjusting for possible confounders, including age, heart rate, and albumin, etc., the high level of SII was independently associated with 30- and 90-day and hospital mortalities (tertile 3 vs. tertile 1: HR, 95% CIs: 1.23, 1.04-1.45; 1.21, 1.06-1.39; 1.26, 1.05-1.50) and the incidence of MACEs (tertile 3 vs. tertile 1: OR, 95% CI: 1.39, 1.12-1.73) in critically ill patients with CHF, but no significant correlation was found between SII and the readmission rate. Consistently, patients with high SII level still presented a significantly higher short-term mortality than patients with low SII in the PSM subset. Conclusion: In critically ill patients with CHF, high level of SII could effectively predict high 30- and 90-day and hospital mortalities, as well as the high risk of occurrence of MACEs.
Is Consistency the Panacea? Inconsistent or Consistent Tax Transfer Prices with Strategic Taxpayer and Tax Authority Behavior

This study investigates how strategic tax transfer pricing of a multinational company (MNC) and two tax authorities in different countries affects production and tax avoidance decisions at the firm level and tax revenues at the country level. We employ a game-theoretical model to analyze the costs and benefits of two tax transfer pricing regimes (consistency vs. inconsistency) under asymmetric information. Though tax transfer pricing harmonization is considered a promising instrument to fight undesired tax avoidance, the implications are largely unclear. We find tax avoidance in equilibrium in both countries under inconsistency. Surprisingly, we identify conditions under which low-tax countries benefit from consistency while high-tax countries benefit from inconsistency. This explains how the strategic interaction of taxpayer and tax authorities under firm-level heterogeneity challenges the implementation of consistent regimes. Understanding the implications of (in)consistent transfer pricing rules is crucial when reforming transfer pricing regulations to fight tax avoidance and double taxation.
R EVISITING THE STABILITY OF STOCHASTIC GRADI - ENT DESCENT : A TIGHTNESS ANALYSIS

The technique of algorithmic stability has been used to capture the generalization power of several learning models, especially those trained with stochastic gradient descent (SGD). This paper investigates the tightness of the algorithmic stability bounds for SGD given by Hardt et al. (2016). We show that the analysis of Hardt et al.
Previous approaches to measuring drug novelty

Consumers want innovations that will improve their health and extend their lives. Yet a steady stream of new medical products alone may not translate into improved health outcomes because many new products are very similar to existing ones. While such incremental innovations may improve efficacy or access on the margin, sustainable improvements in health and welfare depend on the development of products that treat medical conditions in novel ways. One of the most important areas of health innovation is the development of new drugs. Unfortunately, the process of drug discovery is difficult and outcomes are uncertain, so drug development projects fail at different stages and for a variety of reasons. Bringing a drug through the development and regulatory process to market is a highly uncertain process (in terms of both likelihood of success, and the profitability for drugs that do succeed ) and can take many years .
Exploring Structure Consistency for Deep Model Watermarking

The intellectual property (IP) of Deep neural networks (DNNs) can be easily ``stolen'' by surrogate model attack. There has been significant progress in solutions to protect the IP of DNN models in classification tasks. However, little attention has been devoted to the protection of DNNs in image processing tasks. By utilizing consistent invisible spatial watermarks, one recent work first considered model watermarking for deep image processing networks and demonstrated its efficacy in many downstream tasks. Nevertheless, it highly depends on the hypothesis that the embedded watermarks in the network outputs are consistent. When the attacker uses some common data augmentation attacks (e.g., rotate, crop, and resize) during surrogate model training, it will totally fail because the underlying watermark consistency is destroyed. To mitigate this issue, we propose a new watermarking methodology, namely ``structure consistency'', based on which a new deep structure-aligned model watermarking algorithm is designed. Specifically, the embedded watermarks are designed to be aligned with physically consistent image structures, such as edges or semantic regions. Experiments demonstrate that our method is much more robust than the baseline method in resisting data augmentation attacks for model IP protection. Besides that, we further test the generalization ability and robustness of our method to a broader range of circumvention attacks.
Quantitatively predicting modal thermal conductivity of nanocrystalline Si by full-band Monte Carlo simulations

Thermal transport of nanocrystalline Si is of great importance for the application of thermoelectrics. A better understanding of the modal thermal conductivity of nanocrystalline Si will be expected to benefit the efficiency of thermoelectrics. In this work, the variance reduced Monte Carlo simulation with full band of phonon dispersion is applied to study the modal thermal conductivity of nanocrystalline Si. Importantly, the phonon modal transmissions across the grain boundaries which are modeled by the amorphous Si interface are calculated by the mode-resolved atomistic Greens function method. The predicted ratios of thermal conductivity of nanocrystalline Si to that of bulk Si agree well with that of the experimental measurements in a wide range of grain size. The thermal conductivity of nanocrystalline Si is decreased from 54 percent to 3 percent and the contribution of phonons with mean free path larger than the grain size increases from 30 percent to 96 percnet as the grain size decreases from 550 nm to 10 nm. This work demonstrates that the full band Monte Carlo simulation using phonon modal transmission by the mode-resolved atomistic Greens function method can capture the phonon transport picture in complex nanostructures, and therefore can provide guidance for designing high performance Si based thermoelectrics.