Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the informatics-based self-concept of primary school children

Education should ensure that all children can move safely in the digital world, understand the phenomena and be able to play an active and self-determined role in shaping the future. A positive informatics-based self-concept (i.e. a self-concept regarding topics of informatics in school) is an important step towards this goal. In this study the informatics-based self-concept of primary school children is compared between children who had informatics1 lessons and children who did not. This study introduces a questionnaire that measures the informatics-based self-concept, which was filled out between May and June 2019 by primary school children from a project group (262 children) for informatics in the 3rd and 4th grade and a similar control group (61 children) without informatics. Results indicate that both groups have a rather high positive informatics-based self-concept, but that the children from the project group rated themselves more positively than the control group in all dimensions. The analysis of the gender-related differences revealed that the informatics lessons seemed unable to compensate for gender-related differences in self-concept. In detail, the girls in the project group assessed themselves lower in self-concept than the boys, whereas the boys and girls in the control group did not differ statistically significantly regarding their self-concept.
Forkhead Box M1 promotes the growth and tube formation of human malignant meningioma cells via the aryl hydrocarbon receptor signaling pathway.

INTRODUCTION Forkhead Box M1 (FOXM1) and aryl hydrocarbon receptor (AHR) signaling pathway participate in meningioma development, but their correlation was inadequately studied. The study is aimed to uncover their functions and correlation in malignant meningioma. MATERIAL AND METHODS Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect FOXM1 expression in malignant meningioma and adjacent tissues. The viability, proliferation, apoptosis and tube formation of meningioma IOMM-Lee and CH157-MN cells transfected with overexpressed FOXM1 were examined with MTT assay, clone formation assay, flow cytometry and tube formation assay, respectively. The expressions of AHR and cytochrome P450 family 1 subfamily A member 1 (CYP1A1) in meningioma and adjacent tissues were detected using qRT-PCR, and the correlation of AHR with FOXM1 was analyzed with Pearson's correlation analysis. Western blot was conducted for measuring the expressions of vascular endothelial growth factor A (VEGFA), AHR and CYP1A1. The cell viability, proliferation, apoptosis and tube formation capability were further determined after treatment with StemRegenin 1 (SR1) (an AHR signaling pathway inhibitor), and transfected with or without overexpressed FOXM1. RESULTS FOXM1, AHR and CYP1A1 expressions were upregulated in malignant meningioma tissues. Overexpressed FOXM1 promoted meningioma cell viability, proliferation, tube formation, upregulated expressions of AHR, CYP1A1 and VEGFA, and inhibited the cell apoptosis. AHR was positively correlated with FOXM1. SR1 suppressed meningioma cell growth and the AHR signaling pathway, and also reversed the active effect of FOXM1 on meningioma cells. CONCLUSIONS FOXM1 may promote malignant meningioma via the AHR signaling pathway, which improved the current understanding of the role of FOXM1 in meningioma.
Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction

This paper summarizes the contributions of the PRHLT-UPV team as a participant in the eRisk 2020 tasks on self-harm detection and prediction of depression levels from social media. Computational methods based on machine learning and natural language processing have a great potential to assist with early detection of mental disorders of social media users, based on their online activity. We use multi-dimensional representations of language, and compare various deep learning models’ performance, exploring rarely approached avenues in previous research, including hierarchical deep learning architectures and pre-trained trans-formers and language models.
Feature Vector Difference based Neural Network and Logistic Regression Models for Authorship Verification

This paper describes the approach we took to create a machine learning model for the PAN 2020 Authorship Veriﬁcation Task. For each document pair, we extracted stylometric features from the documents and used the absolute diﬀerence between the feature vectors as input to our classiﬁer. We created two models: a Logistic Regression Model trained on a small dataset
Xception Based Method for Bird Sound Recognition of BirdCLEF 2020

In this paper, we present an Xception based method for bird sound recognition of BirdCLEF2020. The goal of BirdCLEF2020 is to detect and classify 960 bird species within the provided soundscape recordings, it is more complex to diﬀerentiate such a large number of birds than BirdCLEF2019. In our approach, logmel or loglinear spectrograms are extracted as features, and some data augmentation techniques are utilized to improve the performance of detecting the bird sounds. Finally, we evaluate our system on BirdCLEF2020 test dataset and achieve a classiﬁcation mean average precision (c-mAP) score of 0.0421.
Overview of the Cross-Domain Authorship Verification Task at PAN 2021

Idiosyncrasies in human writing styles make it difficult to develop systems for authorship identification that scale well across individuals. In this year’s edition of PAN, the authorship identification track focused on open-set authorship verification, so that systems are applied to unknown documents by previously unseen authors in a new domain. As in the previous year, the sizable materials for this campaign were sampled from English-language fanfiction. The calibration materials handed out to the participants were the same as last year, but a new test set was compiled with authors and fandom domains not present in any of the previous datasets. The general setup of the task did not change, i.e., systems still had to estimate the probability of a pair of documents being authored by the same person. We attracted 13 submissions by 10 international teams, which were compared to three complementary baselines, us-ing five diverse evaluation metrics. Post-hoc analyses show that systems benefitted from the abundant calibration materials and were well-equipped to handle the open-set scenario: Both the top-performing approach and the highly competitive cohort of runner-ups presented surprisingly strong verifiers. We conclude that, at least within this specific text variety, (large-scale) open-set authorship verification is not necessarily or inherently more difficult than a closed-set setup, which offers encouraging perspec-tives for the future of the field.
AIML at VQA-Med 2020: Knowledge Inference via a Skeleton-based Sentence Mapping Approach for Medical Domain Visual Question Answering

In this paper, we describe our contribution to the 2020 ImageCLEF Medical Domain Visual Question Answering (VQA-Med) challenge. Our submissions scored ﬁrst place on the VQA challenge leader-board, and also the ﬁrst place on the associated Visual Question Generation (VQG) challenge leaderboard. Our VQA approach was developed using a knowledge inference methodology called Skeleton-based Sentence Mapping (SSM). Using all the questions and answers, we derived a set of classiﬁable tasks and inferred the corresponding labels. As a result, we were able to transform the VQA task into a multi-task image classiﬁcation problem which allowed us to focus on the image modelling aspect. We further propose a class-wise and task-wise normalization facilitating optimization of multiple tasks in a single network. This enabled us to apply a multi-scale and multi-architecture ensemble strategy for robust prediction. Lastly, we positioned the VQG task as a transfer learning problem using the VGA task trained models. The VQG task was also solved using classiﬁcation.
Know your Neighbors: Efficient Author Profiling via Follower Tweets

User proﬁling based on social media data is becoming an increasingly relevant task with applications in advertising, forensics, literary studies and soci-olinguistic research. Even though proﬁling of users based on their textual data is possible, social media such as Twitter offer also insight into the data of a given user’s followers. The purpose of this work was to explore how such fol-lower data can be used for proﬁling a given user, what are its limitations and whether performances, similar to the ones observed when considering a given user’s data directly can be achieved. In this work we present our approach, capable of extracting various feature types and, via sparse matrix factorization, learn a dense, low-dimensional representations of individual persons solely from their followers’ tweet streams. The proposed approach scored second in the PAN 2020 Celebrity proﬁling shared task, and is computationally non-demanding.
Generalised Hermite spectral methods for PDEs involving integral fractional Laplacian and Schr\"{o}dinger operators.

In this paper, we introduce two new families of generalised Hermite polynomials/functions (GHPs/GHFs) in arbitrary dimensions, and develop efficient and accurate generalised Hermite spectral algorithms for PDEs with integral fractional Laplacian (IFL) and/or Schr\"{o}dinger operators in $\mathbb R^d. $ As a generalisation of the G. Szeg\"{o}'s family in 1D (1939), the first family of GHPs (resp. GHFs) are orthogonal with respect to $|\bx|^{2\mu} \e^{-|\bx|^2}$ (resp. $|\bx |^{2\mu}$) in $\mathbb R^d$. We further define adjoint generalised Hermite functions (A-GHFs) which have an interwoven connection with the corresponding GHFs through the Fourier transform, and which are orthogonal with respect to the inner product $[u,v]_{H^s(\mathbb R^d)}=((-\Delta)^{s/ 2}u, (-\Delta)^{s/2} v )_{\mathbb R^d}$ associated with the IFL of order $s>0$. Thus, the spectral-Galerkin method using A-GHFs as basis functions leads to a diagonal stiffness matrix for the IFL (which is known to be notoriously difficult and expensive to discretise). The new basis also finds efficient and accurate in solving PDEs with the fractional Schr\"{o}dinger operator: $(-\Delta)^s +|\bs x|^{2\mu}$ with $s\in (0,1]$ and $\mu>-1/2.$ Following the same spirit, we construct the second family of GHFs, dubbed as M\"untz-type generalised Hermite functions (M-GHFs), which are orthogonal with respect to an inner product associated with the underlying Schr\"{o}dinger operator, and are tailored to the singularity of the solution at the origin. We demonstrate that the M\"untz-type GHF spectral method leads to sparse matrices and spectrally accurate to some Schr\"{o}dinger eigenvalue problems.
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms

We study the structure of regret-minimizing policies in the many-armed Bayesian multi-armed bandit problem: in particular, with k the number of arms and T the time horizon, we consider the case where k > \sqrt{T}. We first show that subsampling is a critical step for designing optimal policies. In particular, the standard UCB algorithm leads to sub-optimal regret bounds in this regime. However, a subsampled UCB (SS-UCB), which samples \sqrt{T} arms and executes UCB only on that subset, is rate-optimal. Despite theoretically optimal regret, even SS-UCB performs poorly due to excessive exploration of suboptimal arms. In fact, in numerical experiments SS-UCB performs worse than a simple greedy algorithm (and its subsampled version) that pulls the current empirical best arm at every time period. We show that these insights hold even in a contextual setting, using real-world data. These empirical results suggest a novel form of free exploration in the many-armed regime that benefits greedy algorithms. We theoretically study this new source of free exploration and find that it is deeply connected to the distribution of a certain tail event for the prior distribution of arm rewards. This is a fundamentally distinct phenomenon from free exploration as discussed in the recent literature on contextual bandits, where free exploration arises due to variation in contexts. We prove that the subsampled greedy algorithm is rate-optimal for Bernoulli bandits when k > \sqrt{T}, and achieves sublinear regret with more general distributions. This is a case where theoretical rate optimality does not tell the whole story: when complemented by the empirical observations of our paper, the power of greedy algorithms becomes quite evident. Taken together, from a practical standpoint, our results suggest that in applications it may be preferable to use a variant of the greedy algorithm in the many-armed regime.
Fake News Spreader Identification in Twitter using Ensemble Modeling. Notebook for PAN at CLEF 2020

In this paper, we describe our participation in the author profiling task at PAN 2020. The task consists of detecting fake news spreaders on Twitter, based on a hundred selected tweets from their profile. In our approach, we utilized TFIDF and word embeddings as text representation as well as taking advantage of statistical and implicit features. A combinational classification model is proposed to fuse the impact of all groups of features. The approach obtained highly competitive classification accuracies on both English (0.695) and Spanish (0.785) subsets of the task.
Ranking Arguments by Combining Claim Similarity and Argument Quality Dimensions

In this paper we describe our submissions to the CLEF lab Touch´e, which addresses argument retrieval from a focused debate collection. Our approach consists of a two-step retrieval. Step one ﬁnds the most similar claims to a query. Step two ranks the directly tied premises by the count of their convinc-ingness compared to other relevant premises, for which we aggregate the sum of three main argument quality dimensions. The ﬁnal ranking consists of the product of the two components which are expressed as probabilities.
Overview of the ImageCLEFmed 2020 Concept Prediction Task: Medical Image Understanding

This paper describes the ImageCLEFmed 2020 Concept Detection Task. After ﬁrst being proposed at ImageCLEF 2017, the medical task is in its 4th edition this year, as the automatic detection from medical images still remains a challenging task. In 2020, the format remained the same as in 2019, with a single sub-task. The concept detection task is part of the medical tasks, alongside the tuberculosis and visual question and answering tasks. Similar to the 2019 edition, the data set focuses on radiology images rather than biomedical images, however with an increased number of images. The distributed images were extracted from the biomedical open access literature (PubMed Central). The development data consists of 65,753 training and 15,970 validation images. Each image has corresponding Uniﬁed Medical Language System (UMLS R (cid:13) ) concepts, that were extracted from the original article image captions. In this edition, additional imaging acquisition technique labels were included in the distributed data, which were adopted for pre-ﬁltering steps, concept selection and ensemble algorithms. Most applied approaches for the automatic detection of concepts were deep learning based architectures. Long short-term memory (LSTM) recurrent neural networks (RNN), adversarial auto-encoder, convolutional neural networks (CNN) image encoders and transfer learning-based multi-label classiﬁcation models were adopted. The performances of the submitted models (best score 0.3940) were evaluated using F1-scores computed per image and averaged across all 3,534 test images.
Revisiting code smells in block based languages

Code smells in block based languages have received much attention in recent years. There is a concern that they should be targeted early so that learners develop good programming habits. This study revisits the code smells identified in earlier work and explores each with an in-depth analysis using large samples from actual Scratch projects in order to better identify the causes of the smells, reflect on which may be a consequence of the language or the lack of experience of beginner programmers, and to consider strategies and pedagogies that can be adopted to mitigate the impact.
Techniques for Medical Concept Detection from Multi-Modal Images

With the increasing availability of medical images coming from diﬀerent modalities (X-Ray, CT, PET, MRI, Ultrasound, etc.), the task of automatic medical image captioning is emerging as a key component in medical research. ImageCLEF 2020 is dedicated to extracting relevant concepts from a large corpus of radiology medical images with diﬀerent image modalities by learning the visual contents of the images. The variability between modalities and expertise required in interpreting radiology images often represents a bottleneck in clinical diagnosis pipelines. Therefore, we propose a reliable automatic classiﬁcation method which is highly desired as assistance for human radiologists in producing reports more accurately and eﬃciently. Throughout the exper-iment, we leveraged CNN Architectures, NLP, and clustering techniques to come up with our best system. In this paper, we introduce a novel technique of band classiﬁcation, where we ﬁrst cluster the vocabulary of concepts into bands and then build customized classiﬁcation architectures for each of the band. Predictions of one band are given as input to subsequent bands to aid the learning of associated concepts. Also, we systematically explored several pre-processing approaches to handle variations in contrasts, intensities across images of diﬀerent modalities. In the ﬁnal evaluation of ImageCLEF 2020, we submitted 9 runs out of which our best systems ranked 3rd, 4th, 5th. Overall our team ranked 2nd among 41 participants globally.
A Deep Learning Model to Predict Gender, Age and Occupation of the Celebrities based on Tweets Followers

This paper present the methods are used for detection of celebrity profiles on Twitter when participating in PAN @ CLEF 2020. We have tried to predict the age, gender and occupation of celebrities based on their tweets followers. Our method is based on the use of deep learning techniques to discriminate between authors. The results obtained by the team ''TUKSA20'' are encouraging, indeed we obtained the 1st rank for the prediction of the gender, the second for the prediction of the occupation, and for the age the results obtained are less good.
Shengyan at VQA-Med 2020: An Encoder-Decoder Model for Medical Domain Visual Question Answering Task

Intelligent learning and understanding of image and text information are important research directions for the successful application of deep learning in computer vision (CV) and natural language processing (NLP). This paper takes medical images and questions as the research objects, by extracting the feature information contained in the medical images and questions and combining with the attention mechanism makes the computer system to more accurately obtain the information expressed by the images. Then, the model predicts the answers to the questions about the images. This paper proposes a novel model for the ImageCLEF VQA-Med 2020 task [1]. In this model, we use the improved pre-trained VGG16 to extract image features, and GRU module to extract text features of the questions. Then the structure of Seq2seq, including encoding and decoding parts, is applied to obtain the predicted answers. Our team gets the seventh rank in the ImageCLEF VQA-Med 2020 challenge, and our model achieves accuracy score and BLEU score of 0.376 and 0.412 respectively, in the competition.
The effect of four microbial products on production performance and water quality in integrated culture of freshwater pearl mussel and fishes

An 80-day mesocosm experiment was conducted to evaluate the effect of four commercial microbial products on production performance and water quality in integrated culture of freshwater pearl mussel Hyriopsis cumingii, grass carp, gibel carp, silver carp and bighead carp. Five treatments were tested. One treatment with non-supplementation of microbial products served as control (C). In the other four treatments, Novozymes Pond Protect (NO), Bio-Form BZT-Water Reform (WR), Bacillus natto (BN) and Effective Microbes (EM) were added at the intervals of two weeks, respectively. Mussel yield declined in the tanks with supplementation of the microbial products. No significant differences were found in fish yield and chemical water quality among the treatments except total nitrogen (TN) was higher in tanks EM than in tanks C. Biomass of phytoplankton and Cyanophyta was higher in tanks NO, WR, BN and EM than in tanks C. Supplementation of the microbial products resulted in change in bacterial community in which Actinobacteria, Chlorobi, Verrucomicrobia and Proteobacteria dominated. Bacterial community in the tanks was significantly affected by TN, total phosphorus, chemical oxygen demand and Cyanophyta biomass. This study reveals that the function of these microbial products as probiotics is limited in H. cumingii farming.
Overview of the CLEF eHealth 2020 Task 2: Consumer Health Search with Ad Hoc and Spoken Queries

In this paper, we provide an overview of the CLEF eHealth Task 2 on Information Retrieval (IR), organized as part of the eighth annual edition of the CLEF eHealth evaluation lab by the Conference and Labs of the Evaluation Forum. Its aim was to address laypeople’s diﬃculties in retrieving and digesting valid and relevant information, in their preferred language, to make health-centred decisions. The task was a novel extension of the most popular and established task in CLEF eHealth on Consumer Health Search (CHS), which makes responses to spoken ad-hoc queries. In total, ﬁve submissions were made to
Fake News Spreaders Profiling Through Behavioural Analysis

The growth of social media and the people interconnection led to the digitalization of communication. Nowadays the most inﬂuential politicians or sci-entiﬁc communicators use the media to disseminate news or decisions. However, such communications media can be used maliciously to spread the so-called fake-news in order to polarise public opinion or to deny scientiﬁc theories. It is therefore important to develop intelligent and accurate techniques in order to identify the spreading of fake-news . In this paper, we describes the methodology regarding our participation in the PAN@CLEF Proﬁling Fake News Spreaders on Twitter competition. We propose a supervised Machine-Learning (ML) based framework to proﬁle fake-news spreaders . Our method relies on the combination of Big Five personality and stylometric features. Finally, we evaluate our framework detection capabilities and performance with different ML models on a tweeter dataset in both English and Spanish languages.
Deep Learning based TB Severity Prediction

Computer Aided Diagnosis (CAD) of diseases has undergone large developments with the application of deep learning algorithms to detect the presence of diseases. This paper presents an approach for predicting the presence of tuberculosis, caverns and pleurisy in a set of 3D CT scans of the chests of patients, which is the key task of the ImageCLEF 2020 Tuberculosis challenge. We used the masks provided by the ImageCLEF organizers to segment the 3D CT images, made 2D projections of the segmented 3D images, and augmented them in order to balance the images in the dataset. An AlexNet based model is used to predict the probability of the presence of tuberculosis, caverns and pleurisy from these 2D projections. We achieved the eighth place out of all the teams who made a submission in this task, achieving a mean Area Under the Curve (AUC) score of 0.601 and a minimum AUC score of 0.432. An analysis of the results obtained by the authors following this approach presented, exploring the role of the model’s complexity in reduction of the desired performance.