Miracle-dz/UniFAR-FTUs huggingface dataset:
Different Modality, Different Design, Different Results: Exploring Self-regulated Learner Clusters' Engagement Behaviours at Individual, Group and Cohort Activities

Self-Regulated Learning (SRL) competence is an important aspect of online learning. SRL is an internal process, but analytics can offer an externalisation trigger to allow for observable effects on learner behaviours. The purpose of this paper is to explore the relationship between students’ SRL competence and their learning engagement behaviours observed in multimodal data. In a postgraduate course with 42 students, eighteen features from three types of data in seven learning activities were extracted to investigate multi-level SRL competence students’ engagement behaviours. The results revealed that students with different SRL competence clusters might exhibit different behaviours in individual, group, and cohort level learning activities. Also, students with similar SRL competence might exhibit significantly different engagement behaviours in different learning activities, depending on the learning design. Therefore, while using engagement data in AIED systems; the modality of the data, specific analysis techniques used to process it, and the contextual particularities of the learning design should all be explicitly presented. So that, they can be considered in the interpretations of automated decisions about student achievement.
Feature Extraction for Finger-Vein-Based Identity Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.
Glutathione Reductase Is Associated with the Clinical Outcome of Septic Shock in the Patients Treated Using Continuous Veno-Venous Haemofiltration

Background and objectives: At present, there is insufficient evidence to support the use of continuous veno-venous haemofiltration (CVVH) in the early treatment of septic shock. This study focuses on the association between survival and different parameters of oxidative stress (RedOx). Thereby, we evaluated whether RedOx markers are associated with the outcome of septic shock in patients under early-initiated CVVH treatment. Materials and Methods: We conducted a prospective observational study of 65 patients with septic shock who started CVVH within 12 h after hospital admission. Blood samples were taken from each patient prior to the start of CVVH. The following RedOx markers were measured: glutathione peroxidase, glutathione reductase (GR), total antioxidant capacity, superoxide dismutase, nitric oxide, malondialdehyde and 4-hydroxynonenal. The odds ratio (OR) was calculated using binary logistic regression and stepwise multivariable regression. Results: The 65 patients had a median age of 66 years and 39 were male. Based on the outcome, the patients were divided into two groups—non-survivors (n = 29) and survivors (n = 36)—and the levels of RedOx markers were compared between them. Of all the markers, only higher GR activity was found to be significantly associated with the fatal outcome; 100.3 U/L versus 60.5 U/L, OR = 1.027 (95% CI, 1.010–1.044). Following adjustment for the sequential organ failure assessment score and other parameters, GR activity still presented a significant association with the fatal outcome, OR = 1.020 (95% CI, 1.002–1.038). Conclusions: GR activity is associated with in-hospital fatal outcomes among septic shock patients under early-initiated CVVH treatment. Septic shock patients who have a lower GR activity at hospital admission may have a favourable outcome of the early initiation of CVVH.
SP-A and SPD : dual functioning immune molecules with antiviral and 1 immunomodulatory properties 2

18 Surfactant proteins A (SP-A) and D (SP-D) are soluble innate immune molecules which maintain lung 19 homeostasis through their dual roles as anti-infectious and immunomodulatory agents. SP-A and SP20 D bind numerous viruses including influenza A virus, respiratory syncytial virus (RSV) and human 21 immunodeficiency virus (HIV), enhancing their clearance from mucosal points of entry and 22 modulating the inflammatory response. They also have diverse roles in mediating innate and adaptive 23 cell functions and in clearing apoptotic cells, allergens and other noxious particles. We summarize here 24 how the properties of these first line defense molecules modulate inflammatory responses, as well as 25 host-mediated immunopathology in response to viral infections. 26 Since SP-A and SP-D are known to offer protection from viral and other infections, if their levels are 27 decreased in some disease states as they are in severe asthma and chronic obstructive pulmonary 28 disease (COPD), this may confer an increased risk of viral infection and exacerbations of disease. 29 Recombinant molecules of SP-A and SP-D could be useful in both blocking respiratory viral infection 30 whilst also modulating the immune system to prevent excessive inflammatory responses seen in, for 31 example, RSV or coronavirus disease 2019 (COVID-19). SP-A and SP-D could have therapeutic 32 potential in neutralizing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and 33 modulating the inflammation-mediated pathology associated with COVID-19. Further work 34 investigating the potential therapeutic role of SP-A and SP-D in COVID-19 and other infectious and 35 inflammatory diseases is indicated. 36 SP-A and SP-D: antiviral immunomodulators 2 This is a provisional file, not the final typeset article 37
Analysis of Non-Marital Fertility in Nigeria and Implications for Intervention and Future Research

Fertility and marriage are inextricably linked in sub-Saharan Africa, but recent changes, such as the rise in non-marital fertility, signals a weakening link, and the second demographic transition offers some explanations. Non-marital fertility comes with disadvantages, but it has not been adequately studied in Nigeria. Hence, this study examined the levels, patterns, and correlates of non-marital fertility, and offers implications for interventions and future research. Using data from the Nigeria Demographic and Survey 2008–2018, with a pooled weighted sample size of 11,925 unmarried women, percentage distribution was employed and a two-part model for count data was fitted, with the result showing that the level of non-marital fertility is 29%, and it is common among younger, rural dwelling, and uneducated unmarried women. The correlates of non-marital fertility include age, region of residence, level of education, religion, household wealth index, relationship status, ethnicity, work status, and age at sexual debut. Interventions to arrest rise of non-martial fertility due to its obvious disadvantages, should strengthen sexual and reproductive health programs for unmarried rural-dwelling young women, and revitalize welfare efforts for children born outside wedlock, for poor women, while future research should explore an in-depth understanding of non-marital births.
New Trends and Advances in Non-Variceal Gastrointestinal Bleeding—Series II

The gastrointestinal tract is a long tubular structure wherein any point in the mucosa along its entire length could be the source of a hemorrhage. Upper (esophagel and gastroduodenal) and lower (jejunum, ileum, and colon) gastrointestinal bleeding are common. Gastroduodenal and colonic bleeding are more frequent than bleeding from the small bowel, but nowadays the entire gastrointestinal tract can be explored endoscopically and bleeding lesions can be locally treated successfully to stop or prevent further bleeding. The extensive use of antiplatelet and anticoagulants drugs in cardiovascular patients is, at least in part, the cause of the increasing number of patients suffering from gastrointestinal bleeding. Patients with these conditions are usually older and more fragile because of their comorbidities. The correct management of antithrombotic drugs in cases of gastrointestinal bleeding is essential for a successful outcome for patients. The influence of the microbiome in the pathogenesis of small bowel bleeding is an example of the new data that are emerging as potential therapeutic target for bleeding prevention. This text summarizes the latest research and advances in all forms of acute gastrointestinal bleeding (i.e., upper, small bowel and lower). Diagnosis is approached, and medical, endoscopic or antithrombotic management are discussed in the text in an accessible and comprehensible way.
Creative PenPal: A Virtual Embodied Conversational AI Agent to Improve User Engagement and Collaborative Experience in Human-AI Co-Creative Design Ideation

In recent years, researchers have designed many co-creative systems that are very promising with a powerful AI, yet some fail to engage the users due to the unimpressive quality of the collaboration and interaction. Most of the existing co-creative systems use instructing interaction where users only communicate with the AI by providing instructions for contribution. In this paper, we demonstrate the prototype of a co-creative system for design ideation, Creative PenPal that utilizes an interaction model that includes human-AI conversing interaction using text and a virtual embodiment of the AI character. We hypothesize that this interaction model will improve user engagement, user perception about the AI, and the collaborative experience. We describe the study design to investigate the impact of this particular interaction model on user engagement and the overall collaborative experience. By the time of the workshop, we will have the data and insights from the study.
Making Business Partner Recommendation More Effective: Impacts of Combining Recommenders and Explanations through User Feedback

Business partnerships can help businesses deliver on opportunities they might otherwise be unable to facilitate. Finding the right business partner (BP) involves understanding the needs of the businesses along with what they can deliver in a collaboration. BP recommendation meets this need by facilitating the process of finding the right collaborators to initiate a partnership. In this paper, we present a real world BP recommender application which uses a similarity based technique to generate and explain BP suggestions, and we discuss how this application is enhanced by integrating a solution that 1. dynamically combines different recommender algorithms, and 2. enhances the explanations to the recommendations, in order to improve the user’s experience with the tool. We conducted a preliminary focus group study with domain experts which supports the validity of the enhancements achieved by integrating our solution and motivates further research directions.
The performance of an aerated stirred tank reactor on VHG batch fermentations

The quest for new and renewable energy sources has greatly increased due to the depletion of fossil fuels reserves. Agro-food wastes appear as a cheap and renewable energy source that can contain great amounts of carbon to be transformed in bioethanol that can be used as additive to gasoline. Industrial wastes of carob pod have a large content in carbohydrates that can be ferment into ethanol. Saccharomyces cerevisiae yeasts have been widely used in fermentation processes for bioethanol production because of its considerable tolerance to high concentrations of ethanol and sugar content and low pH values [1]. The scope of this study was to evaluate the performance of an aerated stirred tank reactor, when fermenting carob pod extract at high sugar concentration. Batch fermentations were carried out in a reactor with 2.4 l of carob syrup with 250 g/l in sugar content and supplemented with peptone and yeast extract at low concentrations, at two different aeration rates in order to verify the positive influence of different aeration flux, the tank geometry and mixing efficiency for high ethanol yields. Results showed that at a higher aeration rate, such as 0.63 vvm, ethanol production reaches its maximum of 70.7 g/l with a yield of 0.3 g of ethanol per g of substrate and S. cerevisae growth figured a specific growth rate of 0.1 h. This production fell short of the expected and theoretical yield of 0.51 g ethanol/g substrate, while at 0.13 vvm of aeration rate ethanol production reached 110.6 g/l showing a yield of 0.45 g ethanol/g substrate.
Adsorption of selected micropollutants on powdered activated carbon and biochar in the presence of kaolinite

AbstractCommercially available powdered activated carbon (PAC) and activated biochar (produced in the laboratory), combined with kaolinite, were used to determine the adsorption of a beta-blocker (atenolol, ATN) and sunscreen compounds (benzophenone, BZP; and benzotriazole, BZT); a hypothesis was made that the presence of kaolinite would increase the adsorption of those target compounds. Various synthetic solutions were prepared by altering the pH, background ions, ionic strength, and glucose/humic acid content to mimic various natural water conditions. The removal efficiency of biochar–kaolinite was higher than that of PAC–kaolinite, presumably because the relatively high surface area and pore volume of biochar resulted in a higher adsorption capacity for the target compounds. Removal of the compounds in the absence of kaolinite followed the order BZP > ATN > BZT (a (mg/g); Langmuir maximum adsorption capacities were as follows: 85.0, 20.6, and 15.6 for PAC, and 125, 37.5, and 25.9 for biochar, respectiv.
Leveraging Explainability for Comprehending Referring Expressions in the Real World

For effective human-robot collaboration, it is crucial for robots to understand requests from users and ask reasonable follow-up questions when there are ambiguities. While comprehending the users' object descriptions in the requests, existing studies have focused on this challenge for limited object categories that can be detected or localized with existing object detection and localization modules. On the other hand, in the wild, it is impossible to limit the object categories that can be encountered during the interaction. To understand described objects and resolve ambiguities in the wild, for the first time, we suggest a method by leveraging explainability. Our method focuses on the active regions of a scene to find the described objects without putting the previous constraints on object categories and natural language instructions. We evaluate our method in varied real-world images and observe that the regions suggested by our method can help resolve ambiguities. When we compare our method with a state-of-the-art baseline, we show that our method performs better in scenes with ambiguous objects which cannot be recognized by existing object detectors.
"A Virus Has No Religion": Analyzing Islamophobia on Twitter During the COVID-19 Outbreak

The COVID-19 pandemic has disrupted people's lives driving them to act in fear, anxiety, and anger, leading to worldwide racist events in the physical world and online social networks. Though there are works focusing on Sinophobia during the COVID-19 pandemic, less attention has been given to the recent surge in Islamophobia. A large number of positive cases arising out of the religious Tablighi Jamaat gathering has driven people towards forming anti-Muslim communities around hashtags like #coronajihad, #tablighijamaatvirus on Twitter. In addition to the online spaces, the rise in Islamophobia has also resulted in increased hate crimes in the real world. Hence, an investigation is required to create interventions. To the best of our knowledge, we present the first large-scale quantitative study linking Islamophobia with COVID-19. In this paper, we present CoronaBias dataset which focuses on anti-Muslim hate spanning four months, with over 410,990 tweets from 244,229 unique users. We use this dataset to perform longitudinal analysis. We find the relation between the trend on Twitter with the offline events that happened over time, measure the qualitative changes in the context associated with the Muslim community, and perform macro and micro topic analysis to find prevalent topics. We also explore the nature of the content, focusing on the toxicity of the URLs shared within the tweets present in the CoronaBias dataset. Apart from the content-based analysis, we focus on user analysis, revealing that the portrayal of religion as a symbol of patriotism played a crucial role in deciding how the Muslim community was perceived during the pandemic. Through these experiments, we reveal the existence of anti-Muslim rhetoric around COVID-19 in the Indian sub-continent.
R3L: Connecting Deep Reinforcement Learning To Recurrent Neural Networks For Image Denoising Via Residual Recovery

State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for restoring images with diverse or unknown corruptions. Though deep reinforcement learning can generate effective policy networks for operator selection or architecture search in image restoration, how it is connected to the classic deterministic training in solving inverse problems remains unclear. In this work, we propose a novel image denoising scheme via Residual Recovery using Reinforcement Learning, dubbed R3L. We show that R3L is equivalent to a deep recurrent neural network that is trained using a stochastic reward, in contrast to many popular denoisers using supervised learning with deterministic losses. To benchmark the effectiveness of reinforcement learning in R3L, we train a recurrent neural network with the same architecture for residual recovery using the deterministic loss, thus to analyze how the two different training strategies affect the denoising performance. With such a unified benchmarking system, we demonstrate that the proposed R3L has better generalizability and robustness in image denoising when the estimated noise level varies, comparing to its counterparts using deterministic training, as well as various state-of the-art image denoising algorithms.
DISCO : efficient unsupervised decoding for discrete natural language problems via convex relaxation

In this paper we study test time decoding; an ubiquitous step in almost all sequential text generation task spanning across a wide array of natural language processing (NLP) problems. Our main contribution is to develop a continuous relaxation framework for the combinatorial NP-hard decoding problem and propose Disco - an efficient algorithm based on standard first order gradient based. We provide tight analysis and show that our proposed algorithm linearly converges to within $\epsilon$ neighborhood of the optima. Finally, we perform preliminary experiments on the task of adversarial text generation and show superior performance of Disco over several popular decoding approaches.
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning

Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathogen is identified, organ damage may have already progressed to irreversible stages. Effective sepsis management is therefore highly time-sensitive. By systematically analysing trends in the plethora of clinical data available in the intensive care unit (ICU), an early prediction of sepsis could lead to earlier pathogen identification, resistance testing, and effective antibiotic and supportive treatment, and thereby become a life-saving measure. Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU. Our analysis represents the largest multi-national, multi-centre in-ICU study for sepsis prediction using ML to date. Our dataset contains $156,309$ unique ICU admissions, which represent a refined and harmonised subset of five large ICU databases originating from three countries. Using the international consensus definition Sepsis-3, we derived hourly-resolved sepsis label annotations, amounting to $26,734$ ($17.1\%$) septic stays. We compared our approach, a deep self-attention model, to several clinical baselines as well as ML baselines and performed an extensive internal and external validation within and across databases. On average, our model was able to predict sepsis with an AUROC of $0.847 \pm 0.050$ (internal out-of sample validation) and $0.761 \pm 0.052$ (external validation). For a harmonised prevalence of $17\%$, at $80\%$ recall our model detects septic patients with $39\%$ precision 3.7 hours in advance.
On the Nature of Code Cloning in Open-Source Java Projects

Code cloning plays a very important role in open-source software engineering. The presence of clones within a project may indicate a need for refactoring, and clones between projects are even more interesting, since code migration takes place and violations are possible. But how is code being copied? How prevalent is the process and on what level does it happen? In this general study, we attempt to shed some light on these questions by searching for clones in a large dataset of over 23 thousand Java projects on the level of both files and methods, and by studying the code fragments themselves and their clone pairs. We study the size and the age of code fragments, the prevalence of their clones, relationships between exact and non-exact clones, as well as between method-level and file-level clones. We also discover and describe various anomalies in the code clones that were detected in the dataset.Our research shows that the copying occurs all through the years of the Java code existence and that method-level copying is much more prevalent than file-level copying, with only 35.4% of methods having no clones at all. Additionally, some of the discovered anomalies can be useful for future large-scale cloning research as they can be used for removing auto-generated code.