Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of an intensive intervention to improve lifestyles in people with intermediate cardiovascular risk (DATE study): Study protocol for a randomized controlled trial

AIM The aim of this study was to evaluate the effectiveness of an intensive intervention led by primary care nurses for lifestyle modification among people with intermediate cardiovascular risk. BACKGROUND Cardiovascular diseases may be prevented by adopting healthy lifestyles. Interventions focused on populations at risk are more efficient than those aimed at the general population. More than 50 per cent of cardiovascular events occur in people with intermediate cardiovascular risk, but only a few studies have targeted this population. DESIGN A randomized controlled trial approved in January 2017. METHODS We will recruit 208 participants aged 35-74 years who have intermediate cardiovascular risk. They will be selected by consecutive sampling and will be randomized into a control group or intervention group. Individual standardized brief counselling on healthy lifestyles will be provided to both groups. Additionally, individuals from the intervention group will receive four weekly group sessions focusing on cardiovascular risk, healthy diet, moderation in alcohol consumption, daily physical activity, stress management and smoking cessation and two motivational follow-up calls. The primary outcome will be the lifestyle modification measured by total steps recorded by a pedometer, total score on the Mediterranean Diet Adherence Screener and percentage of current smokers. DISCUSSION This study will allow us to investigate whether an intensive intervention based on a multifactorial group approach is more effective in lifestyle modification than individual standardized brief counseling among adults with intermediate cardiovascular risk. Our results could lead to the establishment of new strategies for cardiovascular risk management.
Hyper-Crosslinked Carbohydrate Polymer for Repair of Critical-Sized Bone Defects

Abstract This study evaluated the safety and efficacy of a novel hyper-crosslinked carbohydrate polymer (HCCP) for the repair of critical-sized bone defects in comparison to two alternative treatments: autologous bone and poly(lactide-co-glycolide) with hyaluronic acid (PLGA/HA). Bilateral critical-sized defects were created in the lateral femoral condyles of skeletally mature New Zealand White rabbits, and they were subsequently implanted with HCCP, PLGA/HA, or autologous bone in a randomized manner. Clinical and behavioral observations were made daily, and radiological and histopathological evaluations were performed at 4, 10, and 16 weeks postimplantation. Defects implanted with HCCP showed progressive bone regeneration and bridging of the defect without adverse histological events. No signs of infection or inflammation associated with the implant material were observed in all animals that received HCCP implantation. A radiographic assessment performed at 16 weeks post-implantation showed significantly higher bone density and volume in defects implanted with HCCP compared to PLGA/HA. No statistically significant difference was observed in bone density and volume between HCCP and autologous bone. These findings demonstrate that HCCP is biocompatible, osteoconductive, and capable of promoting bone regeneration in vivo; therefore, it is suitable for both tissue engineering and the repair of critical-sized bone defects.
BioInfo@UAVR at eRisk 2019: delving into Social Media Texts for the Early Detection of Mental and Food Disorders

This paper describes the participation of the Bioinformatics group of the Institute of Electronics and Engineering Informatics of University of Aveiro in the shared tasks of CLEF eRisk 2019. The objective of the eRisk initiative is to encourage research in the area of information retrieval for the automatic detection of risk situations on the internet. The challenge was organized in three tasks, focused on the early detection of anorexia (T1), self-harm (T2) and severity of depression (T3). We addressed these tasks using a mix approach that combines machine learning with psycholinguistics and behavioural patterns. The results obtained validate the use of such patterns in the context of social media mining and motivate future research into this field.
Multitask Models for Supervised Protest Detection in Texts

The CLEF 2019 ProtestNews Lab tasks participants to identify text relating to political protests within larger corpora of news data. Three tasks include article classification, sentence detection, and event extraction. I apply multitask neural networks capable of producing predictions for two and three of these tasks simultaneously. The multitask framework allows the model to learn relevant features from the training data of all three tasks. This paper demonstrates performance near or above the reported state-of-the-art for automated political event coding though noted differences in research design make direct comparisons difficult.
Celebrity Profiling with Transfer Learning

In this approach to the Celebrity Profiling task we implemented a system that evaluates each tweet of an incoming feed using four classifiers, one for each trait: fame, occupation, gender and birthyear. The overall result for the feed of one celebrity is then determined by the majority of the individual tweet results for each trait. The classifiers were trained using transfer learning on a language model, which itself had been created by unsupervised learning on the raw text of all the tweets in the training data.
Twitter Feeds Profiling with TF-IDF

Paper describes our approach in celebrity profiling task at CLEF 2019 conference. Our method is based on TF-IDF feature extraction method combined with random forest classifier. We were mainly focused on preprocessing phase, where we implemented multiple methods for a text normalization such as emoji transformation, lemmatization, URL replacing. The biggest problem was class imbalance, which we tried to resolve by using synthetic oversampling techniques.
A Data-Driven Approach for Measuring the Severity of the Signs of Depression using Reddit Posts

In response to the CLEF eRisk 2019 shared task on measuring the severity of the signs of depression from threads of user submissions on social media, our team has developed a data-driven, ensemble model approach. Our system leverages word polarities, token extraction via mutual information, keyword expansion and semantic similarities for classifying Reddit posts according to the Beck’s Depression Inventory (BDI). Individual models were combined at the post level by majority voting. The approach achieved a baseline performance for the assessed metrics, including Average Hit Rate and Depression Category Hit Rate, being equivalent to the median system in the limit of one standard deviation.
Bots and Gender Profiling using Character and Word N-Grams

Author profiling, a term used for analysing of text and identifying characteristics of a person based on stylistic and content-based features. In this paper, we describe the approach to detect bot and human (male or female) out of the authors of tweets as a submission for Bots and Gender Profiling shared task at PAN 2019. Our approach involves a combination of character and word n-grams as features for each class and trained Support Vector Machine (SVM). Our experiments show that this method gives good performance in detecting bot and gender (male or female).
DIVA: Domain Invariant Variational Autoencoder

We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the domain invariant VAE (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the class, one for the domain and one for the object itself. In addition, we highlight that due to the generative nature of our model we can also incorporate unlabeled data from known or previously unseen domains. This property is highly desirable in fields like medical imaging where labeled data is scarce. We experimentally evaluate our model on the rotated MNIST benchmark where we show that (i) the learned subspaces are indeed complementary to each other, (ii) we improve upon recent works on this task and (iii) incorporating unlabelled data can boost the performance even further. © Deep Generative Models for Highly Structured Data, DGS@ICLR 2019 Workshop.All right reserved.
Early Detection of Anorexia using RNN-LSTM and SVM Classifiers

Social Media text analysis has engendered a variety of applications in the medical domain, a major example being the detection and cure of deleterious mental disorders. Anorexia is a deadly, psychiatric eating disorder with typical characteristics of alarmingly low body weight conditions and distorted body image, with an unreasonable sense of being overweight. With developments in the field of Natural Language Processing, such highly lethal disorders can be identified and mitigated in their rudimentary stages, saving the victim a lot of mental and physical abuse. The Task 1 of CLEF 2019’s eRisk lab focuses mainly on the early prediction of anorexia, analysed by posts which are sourced from social media platforms. Our team, SSN-NLP has used variations of two major models for sentiment classification, a deep learning RNN-LSTM, and a traditional SGDC Classifier. User-specific data from consequent posts that were extracted from Reddit was released by CLEF eRisk, which was used in its entirety for our training, testing, evaluation and scoring process. With the help of RAKE (Automated keyword extraction), numeric scores were obtained to identify the level of anorexia/self-harm.SSN-NLP submitted 5 variant models to the server that repeatedly accepted submissions and gave user writings to the participating teams. According to the ERDE-50 and F1 scores, our 2-layer LSTM with normed-bahdanau attention, performed the best having scores of 0.07 and 0.33 respectively.
Identification Of Bot Accounts In Twitter Using 2D CNNs On User-generated Contents

The number of accounts that autonomously publish contents on the web is growing fast, and it is very common to encounter them, especially on social networks. They are mostly used to post ads, false information, and scams that a user might run into. Such an account is called bot, an abbreviation of robot (a.k.a. social bots, or sybil accounts). In order to support the end user in deciding where a social network post comes from, bot or a real user, it is essential to automatically identify these accounts accurately and notify the end user in time. In this work, we present a model of classification of social network accounts in humans or bots starting from a set of one hundred textual contents that the account has published, in particular on Twitter platform. When an account of a real user has been identified, we performed an additional step of classification to carry out its gender. The model was realized through a combination of convolutional and dense neural networks on textual data represented by word embedding vectors. Our architecture was trained and evaluated on the data made available by the PAN Bots and Gender Profiling challenge at CLEF 2019, which provided annotated data in both English and Spanish. Considered as the evaluation metric the accuracy of the system, we obtained a score of 0.9182 for the classification Bot vs. Humans, 0.7973 for Male vs. Female on the English language. Concerning the Spanish language, similar results were obtained. A score of 0.9156 for the classification Bot vs. Humans, 0.7417 for Male vs. Female, has been earned. We consider these results encouraging, and this allows us to propose our model as a good starting point for future researches about the topic when no other descriptive details about the account are available. In order to support future development and the replicability of results, the source code of the proposed model is available on the following GitHub repository: https://github.com/marcopoli/Identification-ofTwitter-bots-using-CNN
DEMIR at CLEF eHealth 2019: Information Retrieval based Classification of Animal Experiments Summaries

Information retrieval searching systems recently become powerful for retrieving full text results according to a particular query (or else a document query). Elastic search is an open source information retrieval searching system that is built on Apache Lucene, and works as a distributed search and analytics engine at the same time. Therefore, this engine can also be used as one of machine learnings’ approaches to solve some challenges such as document classification problem. This study is published as working-notes paper for CLEF eHealth 2019 Task 1 on Multilingual Information Extraction and it proposes a k-nearest neighbor (k-NN) and Threshold (t-NN) approaches to classify animal experiment summaries into its correct ICD-10 codes. After that, another two methods are proposed to control and adjust the retrieved labels of the documents results to assign ICD-10 codes for the issued query document. These approaches register high precision, recall and f-measure after we experiment it with the development dataset.
Overview of the CLEF-2019 CheckThat! Lab: Automatic Identification and Verification of Claims. Task 1: Check-Worthiness

We present an overview of the 2nd edition of the CheckThat! Lab, part of CLEF 2019, with focus on Task 1: Check-Worthiness in political debates. The task asks to predict which claims in a political debate should be prioritized for fact-checking. In particular, given a debate or a political speech, the goal is to produce a ranked list of its sentences based on their worthiness for fact-checking. This year, we extended the 2018 dataset with 16 more debates and speeches. A total of 47 teams registered to participate in the lab, and eleven of them actually submitted runs for Task 1 (compared to seven last year). The evaluation results show that the most successful approaches to Task 1 used various neural networks and logistic regression. The best system achieved mean average precision of 0.166 (0.250 on the speeches, and 0.054 on the debates). This leaves large room for improvement, and thus we release all datasets and scoring scripts, which should enable further research in check-worthiness estimation.
A Novel Online Incremental Learning Intrusion Prevention System

Attack vectors are continuously evolving in order to evade Intrusion Detection systems. Internet of Things (IoT) environments, while beneficial for the IT ecosystem, suffer from inherent hardware limitations, which restrict their ability to implement comprehensive security measures and increase their exposure to vulnerability attacks. This paper proposes a novel Network Intrusion Prevention System that utilises a Self-Organizing Incremental Neural Network along with a Support Vector Machine. Due to its structure, the proposed system provides a security solution that does not rely on signatures or rules and is capable to mitigate known and unknown attacks in real-time with high accuracy. Based on our experimental results with the NSL KDD dataset, the proposed framework can achieve on-line updated incremental learning, making it suitable for efficient and scalable industrial applications.
Image Steganalysis with Very Deep Convolutional Neural Networks

Steganography is a technique that embeds secret messages into commonplace data. In contrast, steganalysis is a technique to identify steganography-applied data and to recover hidden message in that data. Effective steganalysis methods are in demand for it is suspected that steganography is made use of by antisocial groups or persons to hide messages from police or intelligence agencies. In this situation, ImageCLEF 2019 Security is held to showcase image steganalysis methods. In the competition track 2: stego image discovery we used natural image classification deep learning models to tackle the problem and get F1 score 0.660, precision score 0.508 and recall score 0.944 to win the third place.
Overview of the Cross-domain Authorship Attribution Task at PAN 2019

Authorship identiﬁcation remains a highly topical research problem in computational text analysis, with many relevant applications in contemporary society and industry. In this edition of PAN, we focus on authorship attribution, where the task is to attribute an unknown text to a previously seen candidate author. Like in the previous edition we continue to work with fanﬁction texts (in four Indo-European languages), written by non-professional authors in a cross-domain setting: the unknown texts belong to a different domain than the training material that is available for the candidate authors. An important novelty of this year’s setup is the focus on open-set attribution, meaning that the test texts contain writing samples by previously unseen authors. For these, systems must consequently refrain from an attribution. We received altogether 12 submissions for this task, which we critically assess in this paper. We provide a detailed comparison of these approaches, including three generic baselines.
Robust Visual-Aided Autonomous Takeoff, Tracking, and Landing of a Small UAV on a Moving Landing Platform for Life-Long Operation

Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life-long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual cues, follow it, and robustly land on it. The system relies on a finite state machine, which together with a novel re-localization module allows the system to operate robustly for extended periods of time and to recover from potential failed landing maneuvers. Two approaches for tracking and landing are developed, implemented, and tested. The first variant is based on a novel height-adaptive PID controller that uses the current position of the landing platform as the target. The second one combines this height-adaptive PID controller with a Kalman filter in order to predict the future positions of the platform and provide them as input to the PID controller. This facilitates tracking and, mainly, landing. Both the system as a whole and the re-localization module in particular have been tested extensively in a simulated environment (Gazebo). We also present a qualitative evaluation of the system on the real robotic platforms, demonstrating that our system can also be deployed on real robotic platforms. For the benefit of the community, we make our software open source.
An Xception-GRU Model for Visual Question Answering in the Medical Domain

This paper introduces an Xception-GRU model for ImageCLEF 2019 Medical Domain Visual Question Answering (VQA-Med) Task. First, we enhance the images and remove extraneous words from the questions and convert the questions to vectors. Then, we employ pretrained Xception model to extract image features and use GRU model to encode the questions. To generate the output, we combine these two models with the attention mechanism. Our Xception-GRU model achieves the accuracy score of 0.21 and BLEU score of 0.393.
Analysing Author Name Mentions in Citation Contexts of Highly Cited Publications

In this paper, we are analysing author name mentions in citation contexts of highly cited articles in a PLOS ONE corpus. First, we have identified author mentions in our corpus of citation contexts. Then, we examined frequent nouns and verbs in the neighbourhood of the identified author mentions using n-grams and utilized these top nouns and verbs to identify the most frequent patterns. We observed that most frequent patterns are associated with the methods which are proposed in the corresponding highly cited references.
AI600 Lab at ImageCLEF 2019 Concept Detection Task

In this paper we describe the participation of AI600 Lab in the ImageCLEF 2019 Concept Detection task. We adopted an approach based on bagof-visual-words model and logistic regression, using different SIFT descriptors as visual features. The classifiers were trained with different features respectively and weighted results were presented. Our best result ranked 26th among 58 runs and 7th out of 11 participant teams.
INAOE-CIMAT at eRisk 2019: Detecting Signs of Anorexia using Fine-Grained Emotions

In this paper, we present our approach to the detection of anorexia at eRisk 2019. The main objective of this shared task is to identify as soon as possible if a user shows signs of anorexia by using their posts on Reddit. For this, we evaluate a representation called Bag of SubEmotions (BoSE), a new technique that represents user posts by building a set of fine-grained emotions. At the beginning, emotions are defined according to categories in a given lexical resource, then fine-grained emotions are discovered by clustering word vectors in each category. For our participation, we chose to evaluate different strategies based on the temporal stability that a user presents and perform early predictions using this representation. The proposed approach shows better performance than the average results of other participants; in addition, due to its interpretability and simplicity, it offers an excellent opportunity for the analysis and detection of mental disorders in social media.
Brain Peak Width of Skeletonized Mean Diffusivity (PSMD) and Cognitive Function in Later Life

It is suggested that the brain’s peak width of skeletonized water mean diffusivity (PSMD) is a neuro-biomarker of processing speed, an important aspect of cognitive aging. We tested whether PSMD is more strongly correlated with processing speed than with other cognitive domains, and more strongly than other structural brain MRI indices. Participants were 731 Lothian Birth Cohort 1936 members, mean age = 73 years (SD = 0.7); analytical sample was 656–680. Cognitive domains tested were as follows: processing speed (5 tests), visuospatial (3), memory (3), and verbal (3). Brain-imaging variables included PSMD, white matter diffusion parameters, hyperintensity volumes, gray and white matter volumes, and perivascular spaces. PSMD was significantly associated with processing speed (−0.27), visuospatial ability (−0.23), memory ability (−0.17), and general cognitive ability (−0.25); comparable correlations were found with other brain-imaging measures. In a multivariable model with the other imaging variables, PSMD provided independent prediction of visuospatial ability and general cognitive ability. This incremental prediction, coupled with its ease to compute and possibly better tractability, might make PSMD a useful brain biomarker in studies of cognitive aging.
Profiling Twitter Users Using Autogenerated Features Invariant to Data Distribution

With the diffusion of Web and Social Media, automatic user profiling classifiers applied to digital contents have become extremely important in application contexts related to social and forensic studies. In many research papers on this topic, an important part of the work is devoted to a costly manual "feature engineering" phase, where the semantic, syntactic, and often languagedependent features need to be accurately chosen to be relevant for profilation task. Differently from this approach, in this work we propose a Twitter user profiling classifier which exploits deep learning techniques to automatically generate user features being a) optimal for user profilation task, and b) able to fight covariance shift problem due to data distribution differences in training and test sets. In the best configuration found, the built system is able to achieve very interesting accuracy results on both English and Spanish languages, with an average final accuracy of more than 0.83.