Miracle-dz/UniFAR-FTUs huggingface dataset:
MLT-DFKI at CLEF eHealth 2019: Multi-label Classification of ICD-10 Codes with BERT

With the adoption of electronic health record (EHR) systems, hospitals and clinical institutes have access to large amounts of heterogeneous patient data. Such data consists of structured (insurance details, billing data, lab results etc.) and unstructured (doctor notes, admission and discharge details, medication steps etc.) documents, of which, latter is of great significance to apply natural language processing (NLP) techniques. In parallel, recent advancements in transfer learning for NLP has pushed the state-of-the-art to new limits on many language understanding tasks. Therefore, in this paper, we present team DFKIMLT’s participation at CLEF eHealth 2019 Task 1 of automatically assigning ICD-10 codes to non-technical summaries (NTSs) of animal experiments where we use various architectures in multi-label classification setting and demonstrate the effectiveness of transfer learning with pre-trained language representation model BERT (Bidirectional Encoder Representations from Transformers) and its recent variant BioBERT. We first translate task documents from German to English using automatic translation system and then use BioBERT which achieves an F1-micro of 73.02% on submitted run as evaluated by the challenge.
A two-staged Approach for Localization and Classification of Coral Reef Structures and Compositions

In this paper we present the approaches that achieved the first place in this years ImageCLEFcoral challenge. The task of the challenge was the localization and classification of corals within images of sea ground. Therefore we had to extract bounding boxes for each coral and labeling them with the specific type of substrate. We applied a state-of-the-art deep learning approach (YOLO) and also developed a two-staged approach, using a grid along with two classifiers. One that classifies the tiles of the grid, the other that classifies the found boxes. We had moderate results using YOLO and discovered that locating the corals is the most challenging part. Furthermore class imbalance and intersecting boxes, made the problem even harder.
Microgrid Stability Definitions, Analysis, and Examples

This document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid Stability Definitions, Analysis, and Modeling, IEEE Power and Energy Society, Piscataway, NJ, USA, Tech. Rep. PES-TR66, Apr. 2018, which defines concepts and identifies relevant issues related to stability in microgrids. In this paper, definitions and classification of microgrid stability are presented and discussed, considering pertinent microgrid features such as voltage-frequency dependence, unbalancing, low inertia, and generation intermittency. A few examples are also presented, highlighting some of the stability classes defined in this paper. Further examples, along with discussions on microgrid components modeling and stability analysis tools can be found in the TF report.
A Novel Discrete Wire-Driven Continuum Robot Arm with Passive Sliding Disc: Design, Kinematics and Passive Tension Control

Wire-driven continuum manipulators are gaining more attention due to their flexibility and dexterity features. In comparison with traditional manipulators, the continuum structure is compliant and safe for human tissue and is able to easily adapt to the unstructured environment. Despite its advantages, wire-driven mechanisms have a serious problem with tension. While pushing and pulling, the wire loses tension, which leads to an ineffective way of driving the pulleys. Therefore, in this research, we propose a novel discrete continuum robot arm with a passive pre-tension mechanism that avoids the wire tension problem. Moreover, this paper will describe the backbone design of the discrete continuum arm and pre-tension mechanism structure, as well as forward and inverse kinematics and kinetic solutions, with simulation results.
Detecting Bot Accounts on Twitter by Measuring Message Predictability

We present a method for deciding whether a given Twitter user is a bot or a human based on the textual content of their feed. Since messages published from the same bot account frequently follow simple, repetitive patterns, we propose to recognise such accounts by measuring message predictability. This is performed using two language modelling solutions: based on a linear model operating on hand-crafted features or a pre-trained neural language model (BERT). When evaluated within PAN 2019 bot detection shared task, our solution reaches an accuracy of 91% for tweets in English and 88% for tweets in Spanish.
UDE at eRisk 2019: Early Risk Prediction on the Internet

In this paper, we describe our participation in CLEF eRisk workshop. eRisk 2019 is the third edition of this track 3 , which was ﬁrst introduced in 2017. In the current edition, the organizers are targeting Social Media users, namely Reddit, who may be under the risk of Anorexia, self-harm, and depression. We participated in both tasks of early risk detection of Anorexia and self-harm. Our predictions are based on Natural Language Processing using supervised machine learning with Support Vector Machines (SVM) and neural networks. SVM gave the best results among our ﬁve submitted models with latency-weighted F1 of 0.58 and ERDE 5 of 0.08 and ERDE 50 of 0.04 for Anorexia detection task, while our more complicated neural network models did not show the desired performances.
Assessment of Psychopathological Comorbidities in Children and Adolescents With Autism Spectrum Disorder Using the Child Behavior Checklist

Autism spectrum disorder (ASD) is characterized by psychiatric and behavioral comorbidities. The Child Behavior Checklist (CBCL) provides valid and well-established measures of emotional, behavioral, and social problems in children and adolescents. The aim of the present study was to verify whether emotional, behavioral, and social problems were modulated by ASD symptom severity, cognitive development, gender, and age by analyzing the CBCL in a large group of children and adolescents with ASD. The results show that around 30% of participants with ASD exhibited internalizing problems and only 6% externalizing problems, with males exhibiting more internalizing problems than females. No correlation was found between CBCL scores and indices of ASD severity. However, higher CBCL Total Problems scores were found in older children and in children with lower cognitive abilities. The detection of behavioral and emotional problems allows children with ASD to undergo specific and individualized treatment that takes into account their psychopathological problems.
Insights into the morphology and molecular characterisation of glacial relict Eurytemoralacustris (Poppe, 1887) (Crustacea, Copepoda, Calanoida, Temoridae)

Abstract Eurytemoralacustris (Poppe, 1887) is a stenothermic glacial relict whose narrow environmental requirements make it an indicator species for good ecological conditions. The primary threats to this species are eutrophication and global warming. Many authors have described E.lacustris in taxonomic keys; however, its morphological description is unsatisfactory. Therefore, in this study, we aimed to review morphological characteristics of E.lacustris that were previously undescribed in the literature and to provide the molecular characteristics based on the two conservative mitochondrial genes: cytochrome c oxidase I (COI) and cytochrome b (cytb). The new record of E.lacustris indicates that it is a more widespread species than previously hypothesized. Width-to-length ratio of the last female endopod segment of legs indicates variation among the widely distributed species of the genus in Europe (i.e., E.lacustris, E.velox (Lilljeborg, 1853), and E.affinis (Poppe, 1880)). We also found variability of number of setae on the second segment of male endopod. Furthermore, our analysis confirms the occurrence of species in different than exclusively freshwater habitats.
Attentive Multi-stage Learning for Early Risk Detection of Signs of Anorexia and Self-harm on Social Media

Three tasks are proposed at CLEF eRisk-2019 for predicting mental disorder using users posts on Reddit. Two tasks (T1 and T2) focus on early risk detection of signs of anorexia and self-harm respectively. The other one (T3) focus on estimation of the severity level of depression from a thread of user submissions. In this paper, we present the participation of LIRMM (Laboratoire d’Informatique, de Robotique et de Micro´electronique de Montpellier) in both tasks on early detection (T1 and T2). The proposed model addresses this problem by modeling the temporal mood variation detected from user posts through multi-stage learning phases. The proposed architectures use only textual information without any hand-crafted features or dictionaries. The basic architecture uses two learning phases through exploration of state-of-the-art deep language models. The proposed models perform comparably to other contributions.
Full Training versus Fine Tuning for Radiology Images Concept Detection Task for the ImageCLEF 2019 Challenge

Concept detection from medical images remains a challenging task that limits implementation of clinical ML/AI pipelines because of the scarcity of the highly trained experts to annotate images. There is a need for automated processes that can extract concrete textual information from image data. ImageCLEF 2019 provided us a set of images with labels as UMLS concepts. We participated for the first time for the concept detection task using transfer learning. Our approach involved an experiment of layerwise fine tuning (full training) versus fine tuning based on previous reported recommendations for training classification, detection and segmentation tasks for medical imaging. We ranked number 9 in this year’s challenge, with an F1 result of 0.05 after three entries. We had a poor result from performing layerwise tuning (F1 score of 0.014) which is consistent with previous authors who have described the benefit of full training for transfer learning. However when looking at the results by a radiologist, the terms do not make clinical sense and we hypothesize that we can achieve better performance when using medical pretrained image models for example PathNet and utilizing a hierarchical training approach which is the basis of our future work on this dataset.
Statistical inference problems with applications to computational structural biology

In this data pervasive world, the efficient and accurate modelling of data is crucial to support reliable analyses and to improve the solution to related problems. In order to describe the given data, the problem of selecting a suitable model has to be carefully addressed. Traditional approaches to the problem of optimal model selection have relied predominantly on the number of model parameters rather than the actual parameters themselves. This limits the ability of traditional methods to correctly distinguish among models that, while being of different type, have the same number of model parameters. In order to address the problem of model selection satisfactorily, this thesis explores the Bayesian information-theoretic principle of minimum message length (MML). The inference framework based on the MML principle enables the optimal selection of models by using the constituent parameters to better balance the trade-off between the model’s complexity and its goodness-of-fit to the data. The core of this thesis explores the MML-based inference of some of the commonly used probability distributions whose parameters have not yet been characterized and of mixtures of these probability distributions. The models of these probability distributions allow for accurate modelling of data in the Euclidean space and data that is directional in nature. These probabilistic models and their mixtures have widespread uses in statistical machine learning tasks. In this context, we have developed a general purpose search method to determine the optimal number of mixture components and their parameters that describe the given data in a completely unsupervised setting. The use of the MML modelling paradigm and our proposed search method is explored in detail on a variety of real-world data, specifically on directional text data and on the spatial orientation data of protein three-dimensional structures. Further, mixtures of directional probability distributions have facilitated the design of reliable computational models for protein structural data. Furthermore, the inference framework has been used for concise representations of protein folding patterns using a combination of non-linear parametric curves. The results of this work have a wide-variety of important uses including direct applications in protein structural biology.
Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning

We propose a study of the stability of several few-shot learning algorithms subject 1 to variations in the hyper-parameters and optimization schemes while controlling 2 the random seed. We propose a methodology for testing for statistical differences 3 in model performances under several replications. To study this specific design, 4 we attempt to reproduce results from three prominent papers: Matching Nets, 5 Prototypical Networks, and TADAM. We analyze on the miniImagenet dataset on 6 the standard classification task in the 5-ways, 5-shots learning setting at test time. 7 We find that the selected implementations exhibit stability across random seed, and 8 repeats. 9
A Method for Identification of Multisynaptic Boutons in Electron Microscopy Image Stack of Mouse Cortex

Recent electron microscopy (EM) imaging techniques make the automatic acquisition of a large number of serial sections from brain samples possible. On the other hand, it has been proven that the multisynaptic bouton (MSB), a structure that consists of one presynaptic bouton and multiple postsynaptic spines, is closely related to sensory deprivation, brain trauma, and learning. Nevertheless, it is still a challenging task to analyze this essential structure from EM images due to factors such as imaging artifacts and the presence of complicated subcellular structures. In this paper, we present an effective way to identify the MSBs on EM images. Using normalized images as training data, two convolutional neural networks (CNNs) are trained to obtain the segmentation of synapses and the probability map of the neuronal membrane, respectively. Then, a series of follow-up operations are employed to obtain rectified segmentation of synapses and segmentation of neurons. By incorporating this information, the MSBs can be reasonably identified. The dataset in this study is an image stack of mouse cortex that contains 178 serial images with a size of 6004 pixels × 5174 pixels and a voxel resolution of 2 nm × 2 nm × 50 nm. The precision and recall on MSB detection are 68.57% and 94.12%, respectively. Experimental results demonstrate that our method is conducive to biologists’ research on MSBs’ properties.
Unsupervised Pretraining for Text Classification using Siamese Transfer Learning

When training neural networks, huge amounts of training data typically lead to better results. When only a small amount of training data is available, it has been proven useful to initialize a network with pretrained layers. For NLP tasks, networks are usually only given pretrained word embeddings, the rest of the network is not pretrained since pretraining recurrent networks for NLP tasks is difficult. In this article, we present a siamese architecture for pretraining recurrent networks on textual data. The network has to map pairs of sentences onto a vector representation. When a sentence pair is appearing coherently in our corpus, the vector representations should be similar, if not, the representations should be dissimilar. After having pretrained that network, we enhance it and train it on a smaller dataset in order to have it classify textual data. We show that using this kind of approach for pretraining results in better results comparing to doing no pretraining or only using pretrained embeddings when doing text classification for a task with only a small amount of training data. For evaluation, we are using the bots and gender profiling dataset provided by PAN 2019.
Extracting and Matching Patent In-text References to Scientific Publications

References in patent texts to scientific publications are valuable for studying the links between science and technology but are difficult to extract. This paper tackles this challenge, specifically, we extract references embedded in USPTO patent full texts and match them to Web of Science (WoS) publications. We approach the reference extraction problem as a sequence labelling task, training CRF and Flair models. We then match references to the WoS using regular expression patterns. We train and evaluate the reference extraction models using cross validation on a sample of 22 patents with 1,952 manually annotated in-text references. Then we apply the models to a large collection of 33,338 biotech patents. We find that CRF obtains better results on citation extraction than Flair, with precision scores of around 90% and recall of around 85%. However, Flair extracts much more references from the large collection than CRF, and more of those can be matched to WoS publications. We find that 88% of the extracted in-text references are not listed on patent front page, suggesting distinct roles played by in-text and front-page references. CRF and Flair collectively extract 603,457 references to WoS publications that are not listed on the front page. In addition to the 1.17 Million front-page references in the collection, this is a 51% increase in identified patent–publication links compared with only relying on frontpage references.
Overview of eRisk at CLEF 2019: Early Risk Prediction on the Internet (extended overview)

This paper provides an overview of eRisk 2019, the third edition of this lab under the CLEF conference. The main purpose of eRisk is to explore issues of evaluation methodology, effectiveness metrics and other processes related to early risk detection. Early detection technologies can be employed in different areas, particularly those related to health and safety. This edition of eRisk had three tasks. Two of them shared the same format and focused on early detecting signs of depression (T1) or self-harm (T2). The third task (T3) focused on an innovative challenge related to automatically ﬁlling a depression questionnaire based on user interactions in social media.
Big Data For Lifelog Moments Retrieval Improvement

The ImageCLEF lifelog Moment Retrieval Task promotes research for lifelogging retrieval by providing a benchmark with an evaluation process that allows a comparative analysis of lifelog methods, approaches and tools. In this paper, we describe our participation at the ImageCLEF lifelog LMRT 2019. Findings from our initial experiments in the LMRT sub-task in ImageCLEFlifelog2018 have motivated us to improve our deep learning-based processing for lifelog image retrieval approach using NoSQL database. The new version employs a distributed database and framework for storing and processing large volumes of data. We try to reduce user involvement during the ne-tuning phase by using the ground truth for the development dataset. We implement our architecture using Matlab, Cassandra, and Spark. The best results were given by the rst run with precision@10=0.28. This run is based on ne-tuning Googlenet with the weights freeze of the 110 rst layers.
Bots and Gender Profiling of Tweets using Word and Character N-Grams

Author profiling deals with the identification of various details about the author of the text (e.g., age and gender). In this paper, we describe the participation of our team (hacohenkerner19) in the PAN 2019 shared task on Bots and Gender Profiling in two languages: English and Spanish. Given a Twitter feed, we should determine whether its author is a bot or a human. In the case of human, we should identify her/his gender. In this paper, we describe our preprocessing methods, feature sets, five applied machine learning methods, and accuracy results. The best accuracy result for the English dataset (84.8%) was obtained by LinearSVC using 2,000 word unigrams. The same result (84.8%) was also obtained by LR by using four preprocessing methods, 2,000 word unigrams, and 1,000 word bigrams with maximal skips of 2 words. The best accuracy result (75,54%) for the Spanish dataset was achieved using LinearSVC with only the HTML tag removal preprocessing method and a combination of 1,000 word unigrams, 1,000 word bigrams, and 3,000 character trigrams.
Identifying the Conceptual Space of Citation Contexts using Coreferences

The study of citation contexts is an important element in understanding the function of citations and categorizing the relationships between works. One of the problems in this ﬁeld is deﬁning the size of citation contexts. In this paper we propose the deﬁnition of citation blocks (CB) that are citation contexts composed of one or more sentences that are linked by coreference clusters. We describe the methodology for the automatic processing and determining the boundaries of CB and observe the diﬀerent sizes of CB in the diﬀerent sections of the IMRaD structure of articles. The results are obtained from a sample of 70,000 citation contexts extracted from the PLOS dataset.
An Xception Convolutional Neural Network for Malware Classification with Transfer Learning

In this work, we applied a deep Convolutional Neural Network (CNN) with Xception model to perform malware image classification. The Xception model is a recently developed special CNN architecture that is more powerful with less over- fitting problems than the current popular CNN models such as VGG16. However only a few use cases of the Xception model can be found in literature, and it has never been used to solve the malware classification problem. The performance of our approach was compared with other methods including KNN, SVM, VGG16 etc. The experiments on two datasets (Malimg and Microsoft Malware Dataset) demonstrated that the Xception model can achieve the highest training accuracy than all other approaches including the champion approach, and highest validation accuracy than all other approaches including VGG16 model which are using image-based malware classification (except the champion solution as this information was not provided). Additionally, we proposed a novel ensemble model to combine the predictions from . bytes files and . asm files, showing that a lower logloss can be achieved. Although the champion on the Microsoft Malware Dataset achieved a bit lower logloss, our approach does not require any features engineering, making it more effective to adapt to any future evolution in malware, and very much less time consuming than the champion's solution.
Mandarin Classroom Materials for Malay-speaking Students——A Case in UNIMAS

The elementary Mandarin class( Level 1) of Universiti Malaysia Sarawak( UNIMAS) targets at students who are zero beginners with Malay as their mother tongue. The course objective is to help students develop their oral communicative skills besides providing them a platform in learning the Chinese culture. Level 1 textbook comprises of basic units namely greetings,numbers,time,daily expressions, self- introduction,food and drinks,common classifiers and shopping. With specially- tailored curricu- lum and localization in mind,neither books from China nor Malaysia can be utilized without modification. Efforts on localization can be seen in the vocabularies used( place names,objects,race,food,expres- sions) and cultural component. It has been found that these vocabularies have not only increased students' interest but also improved their performance. The grammatical aspects covered in the textbook can be seen obviously target at the learners. Also listing out several common students errors and discusses how they are dealt with,this paper concludes by acknowledging that while materials write- up can be hardly perfect,practicality must be taken care of. Localization is important but it must be done without neglecting the connection with the outside world.