Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models

In this paper, we propose to use a sequence to sequence model for Named Entity Recognition (NER) and we explore the effectiveness of such model in a progressive NER setting – a Transfer Learning (TL) setting. We train an initial model on source data and transfer it to a model that can recognize new NE categories in the target data during a subsequent step, when the source data is no longer available. Our solution consists in: (i) to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs; (ii) to leave the rest of the architecture unchanged, such that it is initialized with parameters transferred from the initial model; and (iii) to fine tune the network on the target data. Most importantly, we design a new NER approach based on sequence to sequence (Seq2Seq) models, which can intuitively work better in our progressive setting. We compare our approach with a Bidirectional LSTM, which is a strong neural NER model. Our experiments show that the Seq2Seq model performs very well on the standard NER setting and it is more robust in the progressive setting. Our approach can recognize previously unseen NE categories while preserving the knowledge of the seen data.
Flexible Requirement Development through User Objectives in an Agile-UCD Hybrid Approach

Agile and User-Centered Design (UCD) integration allows the advantages of both approaches to be taken in a hybrid method. The requirements will evolve and gradually adapt to the needs of users and customers, and UCD techniques will improve usability and User eXperience (UX) of the developed software. However, there has been much discussion about the challenges of this integration, because of the differing philosophies. User stories and use cases are the typical artifacts for specifying requirements in agile methodologies. On the other hand, prototypes, Persona, scenarios, task analysis and usability testing are UCD techniques, frequently employed in Requirements Engineering. Numerous authors have studied the best way for these techniques and artifacts to be compatible in hybrid methods. However, the different approaches of Agile and UCD make it difficult to jointly apply and manage an agile-UCD project. This article proposes User Objectives for the collection and development of requirements in an agile and user-centered project. The UOs collect the functional and non-functional requirements of the user's wishes. Completing a UO implies the orderly realization of three activities: Specification of Requirements, Presentation and Functionality. This enables, the flexible planning of an order in which to carry out activities, according to agile and UCD criteria. Moreover, the UOs are organized in different categories according to both their development and their relationship with the user. The UOs diagrams enable both the progress of the project to be visualized, thereby facilitating the modularization, prioritization and planning; and also the monitoring of the UOs evaluated with UCD techniques, as well as those that still need to be evaluated.
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System

In this paper, a new deep reinforcement learning based augmented general tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence labeling model and a deep reinforcement learning (DRL) based augmented tagger. The augmented tagger helps improve system performance by modeling the data with minority tags. The new system is evaluated on SLU and NLU sequence labeling tasks using ATIS and CoNLL-2003 benchmark datasets, to demonstrate the new system’s outstanding performance on general tagging tasks. Evaluated by F1 scores, it shows that the new system outperforms the current state-of-the-art model on ATIS dataset by 1.9% and that on CoNLL-2003 dataset by 1.4%.
Interruptions to Intensive Care Nurses and Clinical Errors and Procedural Failures: A Controlled Study of Causal Connection

Objectives Interruptions occur frequently in the intensive care unit (ICU) and are associated with errors. To date, no causal connection has been established between interruptions and errors in healthcare. It is important to know whether interruptions directly cause errors before implementing interventions designed to reduce interruptions in ICUs. The aim of the study was to investigate whether ICU nurses who receive a higher number of workplace interruptions commit more clinical errors and procedural failures than those who receive a lower number of interruptions. Methods We conducted a prospective controlled trial in a high-fidelity ICU simulator. A volunteer sample of ICU nurses from a single unit prepared and administered intravenous medications for a patient manikin. Nurses received either 3 (n = 35) or 12 (n = 35) scenario-relevant interruptions and were allocated to either condition in an alternating fashion. Primary outcomes were the number of clinical errors and procedural failures committed by each nurse. Results The rate ratio of clinical errors committed by nurses who received 12 interruptions compared with nurses who received 3 interruptions was 2.0 (95% confidence interval = 1.41–2.83, P < 0.001). The rate ratio of procedural failures committed by nurses who received 12 interruptions compared with nurses who were interrupted 3 times was 1.2 (95% confidence interval = 1.05–1.37, P = 0.006). Conclusions More workplace interruptions during medication preparation and administration lead to more clinical errors and procedural failures. Reducing the frequency of interruptions may reduce the number of errors committed; however, this should be balanced against important information that interruptions communicate.
Evaluating the text quality, human likeness and tailoring component of PASS: A Dutch data-to-text system for soccer

We present an evaluation of PASS, a data-to-text system that generates Dutch soccer reports from match statistics which are automatically tailored towards fans of one club or the other. The evaluation in this paper consists of two studies. An intrinsic human-based evaluation of the system’s output is described in the first study. In this study it was found that compared to human-written texts, computer-generated texts were rated slightly lower on style-related text components (fluency and clarity) and slightly higher in terms of the correctness of given information. Furthermore, results from the first study showed that tailoring was accurately recognized in most cases, and that participants struggled with correctly identifying whether a text was written by a human or computer. The second study investigated if tailoring affects perceived text quality, for which no results were garnered. This lack of results might be due to negative preconceptions about computer-generated texts which were found in the first study.
Intrauterine Malnutrition Reduced Long Leptin Receptor Isoform Expression and Proinflammatory Cytokine Production in Male Rat Pulmonary Endothelial Cells Stimulated by Lipopolysaccharide

Background/Aims We have previously shown that low birth weight (LBW) rats exposed to intrauterine malnutrition have an impaired lung inflammatory response and reduced levels of inflammatory mediators; however, circulating leptin levels were not increased. We evaluated long leptin receptor isoform (ObRb) expression in lung endothelial cells from low birth weight rats and examined its role in the production of lipid mediators and cytokines. Methods Lung endothelial cells were obtained from normal birth weight (NBW) rats or LBW rats subjected to intrauterine malnutrition. These cells were stimulated with leptin (10 ng/mL), LPS (lipopolysaccharide, 1 μg/mL), or leptin plus LPS. Six hours after stimulation, the production of inflammatory mediators (PGE2, LTB4, IL-1β, and IL-6) was evaluated using commercial ELISA kits, and Western blotting was performed to investigate p38MAPK, NF-κB, and ObRb expression. Results Leptin increased IL-1β levels in only cells from the NBW group, whereas LPS increased PGE2 and LTB4 levels in cells from both groups; leptin addition potentiated lipid mediator production induced by LPS in the NBW group. LPS enhanced the production of IL-1β and IL-6 in only endothelial cells from NBW rats. Leptin receptor expression was decreased (63%) in endothelial cells from LBW rats. None of the stimuli increased NF-κB or p38 signaling pathway expression in cells from LBW rats. Conclusion These results suggest that intrauterine malnutrition compromises leptin receptor expression and cytokine production in pulmonary endothelial cells stimulated by LPS; these effects seem to involve the NF-κB and p38MAPK signaling pathways.
Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation

Within this thesis we propose a platform for combining Augmented Reality (AR) hardware with machine learning in a user-oriented pipeline, offering to the medical staff an intuitive 3D visualization of volumetric Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) medical image segmentations inside the AR headset, that does not need human intervention for loading, processing and segmentation of medical images. The AR visualization, based on Microsoft HoloLens, employs a modular and thus scalable frontend-backend architecture for real-time visualizations on multiple AR headsets. As Convolutional Neural Networks (CNNs) have lastly demonstrated superior performance for the machine learning task of image semantic segmentation, the pipeline also includes a fully automated CNN algorithm for the segmentation of the liver from CT scans. The model is based on the Deep Retinal Image Understanding (DRIU) model which is a Fully Convolutional Network with side outputs from feature maps with different resolution, extracted at different stages of the network. The algorithm is 2.5D which means that the input is a set of consecutive scan slices. The experiments have been performed on the Liver Tumor Segmentation Challenge (LiTS) dataset for liver segmentation and demonstrated good results and flexibility. While multiple approaches exist in the domain, only few of them have focused on overcoming the practical aspects which still largely hold this technology away from the operating rooms. In line with this, we also are next planning an evaluation from medical doctors and radiologists in a real-world environment.
Endoscopic Dilation with Bougies versus Balloon Dilation in Esophageal Benign Strictures: Systematic Review and Meta-Analysis

Background The use of bougies and balloons to dilate benign esophageal strictures (BES) is a consolidated procedure. However, the amount of evidence available in scientific literature supporting which is the best technique is very low, despite the great prevalence and importance of such pathology. This systematic review with meta-analysis aims at comparing both techniques, providing good quality of evidence. Methods We searched for randomized clinical trials (RCTs) published from insertion to November 2017, using MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, LILACS, and grey literature. After the data extraction, a meta-analysis was performed. The main outcomes were symptomatic relief and recurrence rate. The secondary outcomes were bleeding, perforation, and postprocedure pain. Results We included 5 randomized clinical trials (RCTs), totalizing 461 patients. Among them, 151 were treated with bougie dilation and 225 underwent balloon dilation. Regarding symptomatic relief, recurrence, bleeding, and perforation rates, there were no differences between the methods. Concerning postprocedure pain, patients submitted to balloon dilation had less intense pain (RD 0.27, 95% IC −0.42 to −0.07, P = 0.007). Conclusion We conclude that there is no difference between bougie and balloon dilation of BESs regarding symptomatic relief, recurrence rate at 12 months, bleeding, and perforation. Patients undergoing balloon dilation present less severe postprocedure pain.
Analysis of serum alpha-fetoprotein (AFP) and AFP-L3 levels by protein microarray

Objectives This study aimed to examine a simple, effective, time-saving, and low-cost protein microarray method for detecting serum alpha-fetoprotein (AFP) and AFP-L3 levels. Methods Serum samples from patients with hepatocellular carcinoma (HCC) (n = 33) and control subjects (n = 39) were collected and evaluated for the presence of AFP using a novel protein microarray. Glycoprotein (including AFP-L3) was enriched from crude samples by a Hotgen Biotech glycosyl capture spin column and then detected by protein microarray. An electrochemiluminescence immunoassay (ECLIA) was used to validate the measured values. Results Neither AFP levels lower than 20 ng/mL in the HCC group nor AFP levels higher than 20 ng/mL in the control group were found when tested by the ECLIA and protein microarray. The kappa test showed good consistency in the diagnostic performance of measuring serum AFP levels and the percentage of AFP-L3 in total AFP by the ECLIA and protein microarray. Protein microarray had advantages of smaller sample size required, low cost, and convenience compared with the ECLIA. Conclusion The protein microarray assay that was developed in the present study shows potential as an economic and convenient technique for detecting AFP and AFP-L3 levels in serum samples from patients with HCC.
Proyecto TAGFACT: Del texto al conocimiento. Factualidad y grados de certeza en español

El objetivo general de este proyecto es crear una herramienta para la anotacion de la factualidad expresada en textos en espanol a traves del procesamiento automatico. Pretendemos que dicha representacion sea muy rica, por lo que se llevara a cabo desde tres ejes distintos: multinivel, multidimensional y multitextual. El analisis multinivel da cuenta de las distintas marcas linguisticas que expresan el grado de certeza de un evento a nivel morfologico y sintactico, pero tambien discursivo; el analisis multidimensional, de un numero variado de las voces que evaluan dicho evento; y el analisis multitextual, de distintos textos sobre un mismo evento, siendo este ultimo uno de los aspectos mas innovadores de la propuesta.
AMR Beyond the Sentence: the Multi-sentence AMR corpus

There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering.
Understanding Users’ Vaping Experiences from Social Media: Initial Study Using Sentiment Opinion Summarization Techniques

Background E-liquid is one of the main components in electronic nicotine delivery systems (ENDS). ENDS review comments could serve as an early warning on use patterns and even function to serve as an indicator of problems or adverse events pertaining to the use of specific e-liquids—much like types of responses tracked by the Food and Drug Administration (FDA) regarding medications. Objective This study aimed to understand users’ “vaping” experience using sentiment opinion summarization techniques, which can help characterize how consumers think about specific e-liquids and their characteristics (eg, flavor, throat hit, and vapor production). Methods We collected e-liquid reviews on JuiceDB from June 27, 2013 to December 31, 2017 using its public application programming interface. The dataset contains 27,070 reviews for 8058 e-liquid products. Each review is accompanied by an overall rating and a set of 4 aspect ratings of an e-liquid, each on a scale of 1-5: flavor accuracy, throat hit, value, and cloud production. An iterative dichotomiser 3 (ID3)-based influential aspect analysis model was adopted to learn the key elements that impact e-liquid use. Then, fine-grained sentiment analysis was employed to mine opinions on various aspects of vaping experience related to e-liquids. Results We found that flavor accuracy and value were the two most important aspects that affected users’ sentiments toward e-liquids. Of reviews in JuiceDB, 67.83% (18,362/27,070) were positive, while 12.67% (3430/27,070) were negative. This indicates that users generally hold positive attitudes toward e-liquids. Among the 9 flavors, fruity and sweet were the two most popular. Great and sweet tastes, reasonable value, and strong throat hit made users satisfied with fruity and sweet flavors, whereas “strange” tastes made users dislike those flavors. Meanwhile, users complained about some e-liquids’ steep or expensive prices, bad quality, and harsh throat hit. There were 2342 fruity e-liquids and 2049 sweet e-liquids. There were 55.81% (1307/2342) and 59.83% (1226/2049) positive sentiments and 13.62% (319/2342) and 12.88% (264/2049) negative sentiments toward fruity e-liquids and sweet e-liquids, respectively. Great flavors and good vapors contributed to positive reviews of fruity and sweet products. However, bad tastes such as “sour” or “bitter” resulted in negative reviews. These findings can help businesses and policy makers to further improve product quality and formulate effective policy. Conclusions This study provides an effective mechanism for analyzing users’ ENDS vaping experience based on sentiment opinion summarization techniques. Sentiment opinions on aspect and products can be found using our method, which is of great importance to monitor e-liquid products and improve work efficiency.
Using Word Embeddings for Unsupervised Acronym Disambiguation

Scientific papers from all disciplines contain many abbreviations and acronyms. In many cases these acronyms are ambiguous. We present a method to choose the contextual correct definition of an acronym that does not require training for each acronym and thus can be applied to a large number of different acronyms with only few instances. We constructed a set of 19,954 examples of 4,365 ambiguous acronyms from image captions in scientific papers along with their contextually correct definition from different domains. We learn word embeddings for all words in the corpus and compare the averaged context vector of the words in the expansion of an acronym with the weighted average vector of the words in the context of the acronym. We show that this method clearly outperforms (classical) cosine similarity. Furthermore, we show that word embeddings learned from a 1 billion word corpus of scientific texts outperform word embeddings learned on much large general corpora.
Polynomial-Time Approximation Schemes for k-center, k-median, and Capacitated Vehicle Routing in Bounded Highway Dimension

The concept of bounded highway dimension was developed to capture observed properties of road networks. We show that a graph of bounded highway dimension with a distinguished root vertex can be embedded into a graph of bounded treewidth in such a way that u-to-v distance is preserved up to an additive error of epsilon times the u-to-root plus v-to-root distances. We show that this embedding yields a PTAS for Bounded-Capacity Vehicle Routing in graphs of bounded highway dimension. In this problem, the input specifies a depot and a set of clients, each with a location and demand; the output is a set of depot-to-depot tours, where each client is visited by some tour and each tour covers at most Q units of client demand. Our PTAS can be extended to handle penalties for unvisited clients. We extend this embedding result to handle a set S of root vertices. This result implies a PTAS for Multiple Depot Bounded-Capacity Vehicle Routing: the tours can go from one depot to another. The embedding result also implies that, for fixed k, there is a PTAS for k-Center in graphs of bounded highway dimension. In this problem, the goal is to minimize d so that there exist k vertices (the centers) such that every vertex is within distance d of some center. Similarly, for fixed k, there is a PTAS for k-Median in graphs of bounded highway dimension. In this problem, the goal is to minimize the sum of distances to the k centers.
User engagement and system support in the search process: User dominated, system dominated and balanced search tactics

Both user engagement and system support play important roles in applying search tactics. To apply search tactics in the information retrieval (IR) processes, users engage intellectually with the system, while the system assists them by providing different system features. By analyzing 63 subjects' information searching diaries and questionnaires, the authors identified various types of user engagement and system support, and classified search tactics into three groups: user dominated, system dominated, and balanced tactics. The authors further explored types of user engagement and types of system support in applying user dominated tactics (e.g. evaluating tactic), system dominated tactics (e.g. accessing tactic) and balanced tactics (e.g. learning tactic). The findings show that users and systems play major roles in applying user dominated and system dominated tactics respectively. While applying balanced tactics, users and systems have to be equally engaged.
Trauma-Focused Art Therapy in the Treatment of Posttraumatic Stress Disorder: A Pilot Study

ABSTRACT Research showed that more than 30% of patients with Posttraumatic Stress Disorder (PTSD) do not benefit from evidence-based treatments: Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) or Eye Movement Desensitization and Reprocessing (EMDR). These are patients with prolonged and multiple traumatization, with poor verbal memory, and patients with emotional over-modulation. Retelling traumatic experiences in detail is poorly tolerated by these patients and might be a reason for not starting or not completing the recommended treatments. Due to lack of evidence, no alternative treatments are recommended yet. Art therapy may offer an alternative and suitable treatment, because the nonverbal and experiential character of art therapy appears to be an appropriate approach to the often wordless and visual nature of traumatic memories. The objective of this pilot study was to test the acceptability, feasibility, and applicability of trauma-focused art therapy for adults with PTSD due to multiple and prolonged traumatization (patients with early childhood traumatization and refugees from different cultures). Another objective was to identify the preliminary effectiveness of art therapy. Results showed willingness to participate and adherence to treatment of patients. Therapists considered trauma-focused art therapy feasible and applicable and patients reported beneficial effects, such as more relaxation, externalization of memories and emotions into artwork, less intrusive thoughts of traumatic experiences and more confidence in the future. The preliminary findings on PTSD symptom severity showed a decrease of symptoms in some participants, and an increase of symptoms in other participants. Further research into the effectiveness of art therapy and PTSD is needed.