Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Domain Authorship Attribution with Federales

This paper describes the system with which I participated in the CrossDomain Authorship Attribution task at PAN2019, which entailed attributing fanfiction texts from a specific “fandom” (texts in a given fictional world) on the basis of training material from other fandoms. As underlying system I used a combination of 5 or 3 (depending on language) feature sets and two author verification methods. In reaction to the genre differences, I added a second round of attribution, this time in-genre, for those authors for whom enough target fandom texts could be identified in the first round. On the training dataset, attribution quality was well over the baseline scores, but with ample space for further improvement. On the test dataset, gain over the baseline scores was lower, indicating some kind of overtraining. In general, performance showed that more work is needed on (automated) hyperparameter selection.
UA.PT Bioinformatics at ImageCLEF 2019: Lifelog Moment Retrieval based on Image Annotation and Natural Language Processing

The increasing number of mobile and wearable devices is dramatically changing the way we can collect data about a person’s life. These devices allow recording our daily activities and behavior in the form of images, video, biometric data, location and other data. This paper describes the participation of the Bioinformatics group of the Institute of Electronics and Engineering Informatics of University of Aveiro in the ImageCLEF lifelog task, more speciﬁcally in the Lifelog Moment Retrieval sub-task. The approach to solve this sub-task is divided into three stages. The ﬁrst one is the pre-processing of the lifelog dataset for a selection of the images that contain relevant information in order to reduce the amount of images to be processed and obtain additional visual information and concepts from the ones to be considered. In the second step, the query topics are analyzed using Natural Languages Processing tools to extract relevant words to retrieve the desired moment. This words are compared with the visual concepts words, obtained in the pre-processing step using a pre-trained word2vec model, to compute a conﬁdence score for each processed image. An additional step is used in the last two runs, in order to include the images not processed in the ﬁrst step and improve the results of our approach. A total of 6 runs were submitted and the results obtained show an evolution with each submission. Although the results are not yet competitive with other teams, this challenge is a good starting point for our research work. We pretend to continue the development of a lifelogging application in the context of a research project, so we expect to participate in the next year in the ImageCLEFlifelog task.
Estimating Severity from CT Scans of Tuberculosis Patients using 3D Convolutional Nets and Slice Selection

In this work, we propose a 16-layer 3D convolutional neural network with a slice selection technique employed in the task of 3D Computed Tomography Image data of Tuberculosis(TB) patients which attained 10 th place in the ImageCLEF 2019 Tuberculosis Severity scoring challenge. The goal is aimed at estimating TB severity based on the CT image. The best result reported in this work is Area Under the ROC Curve (AUC) of 0.61 and a binary accuracy of 61.5%. Codes for this work can be found at URL.
Bots and Gender Profiling using a Multi-layer Architecture

In this paper, we introduce the architecture used for our PAN@CLEF-2019 author proﬁling participation. In this task, we had to predict if the author of 100 tweets was a bot, a female human, or a male human user. This task is proposed from a multilingual perspective, for English and Spanish. We handled this task in two steps, using different feature extraction techniques and machine learning algorithms. In the ﬁrst step, we used random forest classiﬁer with different features in order to predict if the users were bots or humans. In the second step, we recovered all the users predicted as humans. We then used a 2-layers architecture to predict the gender of the users detected as humans.
Developing Web Applications with Apache, MySQL, memcached, and Perl

The only book to address using cache to enhance and speed up Web application development Developers use Apache, MySQL, memcached, and Perl to build dynamic Web sites that store information within the MySQL database; this is the only book to address using these technologies together to alleviate the database load in Web development Covers each of the four systems and shows how to install, set up, and administer them; then shows the reader how to put the parts together to start building applications Explains the benefits of a base perl library for code re-use, and provides sample applications that demonstrate in a practical way the information covered in the previous chapters Examines monitoring, performance, and security, with a problem-solving chapter that walks the reader through solving real-world issues
An Evolutionary Approach to Build User Representations for Profiling of Bots and Humans in Twitter

In this work, we describe a novel weighting scheme of terms to produce document representations of Twitter posts for the Author Profiling task at PAN 2019. The purpose of this task is to predict the type and gender of the author, bot/human, and bot/female/male, respectively. The novelty of our approach resides in that we use an evolutionary approach to successfully combine traditional statistics features, e.g. tf, idf, generating, then an improved weighted scheme. Results suggest that our proposal outperforms the baseline, which uses the mean of the word embeddings as the weight, for up to 6% in accuracy when predicting author type in the English language.
Location-Based Species Recommendation - GeoLifeCLEF 2019 Challenge

In this paper, it is presented a system built with the aim to predict plant species given their location in the context of the GeoLifeCLEF 2019 challenge. There are developed several prediction models on the basis of a representation in the environmental space (a feature vector composed of climatic variables and other variables such as soil type, land cover, distance to water) and using the spatial occurrences of species (latitude and longitude): Random Forest, KNearest Neighbors, Artificial Neural Networks and XGBoost.
Naive-Bayesian Classification for Bot Detection in Twitter

This article describes a system that participated in the Bots and Gender Profiling shared task at PAN 2019. The first objective of the task is to detect whether the author of a Twitter account is a bot or a human; and in case of human, the second objective is to identify the gender of the user account. For this purpose, we present a bayesian strategy based on features including specific content of tweets and automatically built lexicons. The best configuration of features reached 0.88 accuracy in the official Spanish test dataset and 0.81 in the English one for the bot/human classification. For gender profiling, the scores we obtained were lower, around 0.70.
Non-local DenseNet for Plant CLEF 2019 Contest

Image-based plant identification is a promising tool constituting the automation of agriculture and environmental conservation as stated in. As an attempt to tackle the data deficient challenge in PlantCLEF 2019, the DenseNet architecture with competitive performance and relatively low number of parameters is augmented with a non-local block. A variety of data sampling schemes are also evaluated as a part of the work. The evaluation of the model and the methods is detailed in the content of the paper.
Check That! Automatic Identification and Verification of Claims: IIT(ISM) @CLEF'19 Check Worthiness

This paper describes the work that we did at Indian Institute of Technology (ISM) towards CheckThat! : Automatic Identification and Verification of Claims for CLEF 2019. As per requirement of CLEF-2019 we submit the only one run in its Check Worthiness Task. Two-fold crossvalidation is used to select a model for submission to CheckThat Lab at CLEF 2019. For our run, we use SVM and LSTM method. Overall, our performance is not satisfactory. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.
A Hierarchical Attention Network for Bots and Gender Profiling

Author profiling represents the task of detecting various author aspects, for instance age, gender or personality, by analyzing written text. The bot identification issue is particularly important in today’s society given the increase in social media usage and the effect of opinion influencing bots on the public. This paper describes our solution for the Bots and Gender Profiling problem, introduced at PAN 2019. The PAN challenge is a two part multilingual problem, namely for the English and Spanish languages. The first task has the goal of identifying if the author is a human or a bot. For the second task, the system has to detect the gender of human authors. Our solution uses a deep learning model based on Hierarchical Attention Networks (HAN) as well as pretrained word embeddings for text representation. For the first task, the official results show that the model achieves an accuracy score of 0.8943 for English and 0.8483 for Spanish. For the second task, our model obtains 0.7485 accuracy for English and 0.6711 for Spanish.
Using Hashtags and POS-Tags for Author Profiling

This paper investigates automatic methods to separate human created from bot created Tweets, and in the case of human, determine the gender of an author. The novel contribution is the investigation of 2 research questions, firstly the usability of part of speech tags and secondly the usability of hashtags as additional features. It therefore extends the models presented by Daneshvar et al. and Basile et al. in the course of past Author Profiling Tasks @ Pan. The results are evaluated as part of the Author Profiling Task @ Pan 2019. It will be shown that the segmentation of hashtags as well as using POS-Tags n-grams can increase the accuracy when classifying bot and gender on the PAN Twitter-dataset. By adding these features and combining them in an ensemble classifier, it was possible to achieve accuracies of 94% for bots and 84% for gender for the English language on the official test set. However, with 79% for bots and 71% for gender, the performance on the Spanish part of the dataset differs significantly. Possible reasons for this shall be examined in the evaluation of the system.
Stacked Bots and Gender Prediction from Twitter Feeds

This paper describes an approach of predicting bots and gender from twitter feeds in English only. Given an author Twitter feed, the task is to identify whether it is a bot or human and in case of human to profile the gender of the author. The submitted system is a stack of two models. In the first model, the feeds are represented as TF–IDF vectors. The second model is based on Doc2Vec feeds representations. Both models use the same pre-processing pipeline and multilayer perceptron neural network in order to classify the tweets.
Identification and Elucidation of Expression Quantitative Trait Loci (eQTL) and their regulating mechanisms using Decodive Deep Learning

Motivation: Identification and elucidation of eQTL has long been an active area of research. Finding cis-eQTL has been a manageable problem because of the limited number of candidates. Finding transeQTL has on the other hand been much more challenging because of the issue of multiple hypothesis testing. It has been suggested that additional information might alleviate this problem and although there has been some success using such methods no comprehensive data integration strategy has been developed. Approach: In order to comprehensively solve the issue of multiple hypothesis testing in the context of trans-eQTL discovery this research introduces MASSQTL: A comprehensive data integration method that makes use of a deep neural network (DNN) to prune the transeQTL candidate space to a desired size with the objective of finding more significantly associated trans-eQTL. Results: With MASSQTL many more trans-eQTL were found using a deep neural network filtering approach. The deep neural network outperformed other machine learning models showing that deep learning by use of complex hierarchical representations is able to model a diverse and sparse set of biological data. In addition to that the method provided new insight into the mechanisms underlying the regulatory architecture of gene expression.
Bot Or Not: A Two-Level Approach In Author Profiling

In this notebook, we summarize our work process of preparing a software for the PAN 2019 Bots and Gender Profiling task. We propose a Machine Learning approach to determine whether an unknown Twitter user is a bot or a human, and if the latter, their gender. We use logistic regressions to identify whether the author is a bot or a human and we use neural networks to attribute their gender. We were able to achieve an accuracy of 91%, 83% for bot/human and 75%, 69% for gender in English and Spanish respectively
Author Profiling: Bot and Gender Prediction using a Multi-Aspect Ensemble Approach

Author Profiling is one of the most important tasks in authorship analysis. In PAN 2019 shared tasks, the gender identification of the author is the main focus. Compared to the previous year the author profiling task is expended by having documents written by bots. In order to tackle this new challenge we propose a two phase approach. In the first phase we exploit the TF-IDF features of the documents to train a model that learns to detect documents generated by bots. Next, we train three models on character-level and word-level representations of the documents and aggregate their results using majority voting. Finally, we empirically show the effectiveness of our proposed approach on the PAN 2019 development dataset for author profiling.
ImageCLEF2019: Tuberculosis - Severity Scoring and CT Report with Neural Networks, Transfer Learning and Ensembling

The diagnosis of tuberculosis is challenging. We present our approach for classifying whether a patient has high or low severity tuberculosis and for detecting which lung is involved, if there is decreased capacity, and if there are pleurisies, calcifications or cavities present. Our best results for the CT report task were obtained by converting volume images into an 8x4 montage of sagittal or coronal images and ensembling the results of separate networks trained separately on sagittal and coronal montage images. The best results for the severity scoring were obtained by ensembling the results from the CT report with the provided metadata.
Well, interpreters… a corpus-based study of a pragmatic particle used by simultaneous interpreters

The aim of the present paper is to investigate simultaneous interpreters’ use of pragmatic markers and, more particularly, to determine what motivates interpreters to use pragmatic markers: the norm-driven ambition to render pragmatic markers used by speakers accurately, the desire to deliver a target language adequate expression of the speaker’s mental model (Jacobsen 2002) or a natural tendency to position themselves with regard to what they hear, in spite of a sharp awareness of norms to the contrary. The pragmatic marker well is chosen as a test case for the research, as its various uses in spontaneous varieties of English are well documented, and because it has functional properties that make it likely to be used by interpreters to position themselves. A small set of instances of well was examined, drawn from several sub-corpora: a corpus of English-speaking Members of the European Parliament (MEPs) and several corpora of interpretations performed by interpreters in the EP’s English booth. A limited quantitative study shows that interpreters do not significantly underuse the marker well and that the use of the marker is rarely triggered by the presence of a marker in the source text. Interpreters add a significant number of instances and thus compensate for the lack of source text triggering. As far as the untriggered occurrences are concerned, a majority turns out to be motivated by the interpreters’ ambition to represent the speakers’ mental model. The examples show that interpreters clearly apply discourse routines also used by English-speaking MEPs, inserting well at the beginning of the speech, between a reported question and an answer, to mark the start of a stretch of reported speech and to mark disagreement with points of view expressed in the previous discourse. A minority of untriggered occurrences cannot be explained on that basis but are rather interpreter-based: in these cases, well is inserted as a repair marker, discarding previous formulations in the target texts or as a rhetorical device allowing the interpreter to reduce cognitive load. In one interesting case, well occurs as a kind of signpost for the widening semantic gap between the speaker’s and the interpreter’s versions.
Trajectory Anonymization: Balancing Usefulness about Position Information and Timestamp

Movement trajectories can provide useful information for all fields. However, they may have high privacy parameters, sharing trajectory data with other operators without anonymization carries the risk of linking movement trajectories to individuals. Therefore, it is necessary to consider applying privacy protection to trajectory data. Anonymization indicators, such as k—anonymity are generally adopted for anonymization of trajectory data. Some studies modify position information to achieve anonymity. This modification sometimes produces inaccuracies in data sets. In this study, to reduce the modification distance of the position, we propose an algorithm that allows the mismatch of time when the position information is acquired within a certain range. Further, we define indicators that represent distortions of position and time information. As a result of comparing the proposed method and the existing method, the usefulness of the proposed method is shown.
A Hybrid Model to Rank Sentences for Check-worthiness

In this paper we describe a system submitted to the CLEF 2019 CheckThat Shared Task. We implement an ensemble of a logistic regression model and an LSTM-based neural network model to predict the worthiness of a sentence for fact checking. Our key idea is to train two separate models with high precision and high recall on binary classification task, and then use the binary class probability as a check-worthiness score. Our system achieves a reciprocal rank 0.4419 and mean average precision of 0.1162 for ranking sentences according to their check-worthiness.
Concept Detection based on Multi-label Classification and Image Captioning Approach - DAMO at ImageCLEF 2019

Medical image captioning is an important and challenging task, which covers computer vision and natural language processing. This ImageCLEF 2019 [6] Caption competition is dedicated to research this ﬁeld. The purpose of this year challenge is using radiological images to detect the concepts representing the key information. In this paper, we illustrate the proposed method to address the issue, based on multi-label classiﬁcation model and CNN-LSTM architecture with attention mechanism. We also perform a detailed analysis and processing for the overall dataset and demonstrate performance with the baseline in the caption prediction task. In ﬁnal evaluation, we completed 9 submissions and ranked second among 12 participants with our best mean F1-score.
Towards Principled Compilation of Ethereum Smart Contracts (SoK)

A blockchain is a tamper-proof distributed transaction registry; first popularized by Bitcoin [1], it has now been extended to support storage of arbitrary state and computations in-ledger. Ethereum [2] and its smart contract model have proven to be a very popular choice for this task, routinely managing assets valued in the billions. However, development of such contracts has been anything but easy. While formally specified, the Ethereum execution platform is based on a low-level machine, quite similar to assembly; semantics for contract operations such as call are quite complex, and the need for resource management creates unanticipated modes of failure. The dominant day-to-day programming platform for Ethereum is Solidity [3], an Object- Oriented language that identifies contracts with objects. While reasoning about Solidity programs is much easier than for their bytecode counterparts, it is not extent of challenges either, and moreover, Solidity lacks a source-level semantics, which forces developers to reason over output bytecode again. In this short paper we explore the main barriers to lift in order to achieve a principled compilation strategy for Solidity. We will review the standard concepts on verified and secure compilation, and frame them in the context of the Ethereum platform.
A Comparative Study on Generalizability of Information Extraction Models on Protest News

Information Extraction applications can help social scientists to obtain necessary information to understand the reasons behind certain social dynamics. Many recent state-of-the-art information extraction approaches are based on supervised machine learning which can recognize information that has similar patterns with previously shown ones. Recognizing relevant information with never-shown patterns, however, is still a challenging task. In this study, we design a Recurrent Neural Network (RNN) architecture employing ELMo embeddings and Residual Bidirectional Long-Short Term Memory layers to overcome this challenge in the context of CLEF 2019 ProtestNews shared task. Furthermore, we train a classical Conditional Random Fields (CRF) model as our strong baseline to display a contrast between a state-of-the-art classical machine learning approach and a recent neural network method both in performance and in generalizability. We show that RNN model outperforms classical CRF model and shows a better promise on generalizability.
Computer Modeling in Philosophy of Religion

Abstract How might philosophy of religion be impacted by developments in computational modeling and social simulation? After briefly describing some of the content and context biases that have shaped traditional philosophy of religion, this article provides examples of computational models that illustrate the explanatory power of conceptually clear and empirically validated causal architectures informed by the bio-cultural sciences. It also outlines some of the material implications of these developments for broader metaphysical and metaethical discussions in philosophy. Computer modeling and simulation can contribute to the reformation of the philosophy of religion in at least three ways: by facilitating conceptual clarity about the role of biases in the emergence and maintenance of phenomena commonly deemed “religious,” by supplying tools that enhance our capacity to link philosophical analysis and synthesis to empirical data in the psychological and social sciences, and by providing material insights for metaphysical hypotheses and metaethical proposals that rely solely on immanent resources.