Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-antigen Vaccination With Simultaneous Engagement of the OX40 Receptor Delays Malignant Mesothelioma Growth and Increases Survival in Animal Models

Malignant Mesothelioma (MM) is a rare and highly aggressive cancer that develops from mesothelial cells lining the pleura and other internal cavities, and is often associated with asbestos exposure. To date, no effective treatments have been made available for this pathology. Herein, we propose a novel immunotherapeutic approach based on a unique vaccine targeting a series of antigens that we found expressed in different MM tumors, but largely undetectable in normal tissues. This vaccine, that we term p-Tvax, is comprised of a series of immunogenic peptides presented by both MHC-I and -II to generate robust immune responses. The peptides were designed using in silico algorithms that discriminate between highly immunogenic T cell epitopes and other harmful epitopes, such as suppressive regulatory T cell epitopes and autoimmune epitopes. Vaccination of mice with p-Tvax led to antigen-specific immune responses that involved both CD8+ and CD4+ T cells, which exhibited cytolytic activity against MM cells in vitro. In mice carrying MM tumors, p-Tvax increased tumor infiltration of CD4+ T cells. Moreover, combining p-Tvax with an OX40 agonist led to decreased tumor growth and increased survival. Mice treated with this combination immunotherapy displayed higher numbers of tumor-infiltrating CD8+ and CD4+ T cells and reduced T regulatory cells in tumors. Collectively, these data suggest that the combination of p-Tvax with an OX40 agonist could be an effective strategy for MM treatment.
Cloning and Expression of B. mellitensis bp26 Gene in Lactococcus lactis as a Food Grade Vaccine

Background: Brucellosis is still an important health problem in under developing countries and researches for finding efficient vaccine are going on. Brucella melitensis (B. mellitensis) bp26 gene is a good candidate for brucellosis vaccine and investigations showed that Lactococcus lactis (L. lactis) with several positive characteristic are attractive for protein expression as a live delivery vectors. These fast growing bacteria need no aeration, are easy to handle, have no exotoxin, endotoxin and protease, so the cost of culturing is inexpensive. Methods: B. mellitensis bp26 gene was cloned in food grade pNZ 8149 vector and expressed in L. lactis NZ 3900. Results: Results showed that we can produce a food-grade recombinant L. lactis producing the B. melitensis BP26 protein. Conclusion: In this study, for Future evaluation about ability of L. lactis as a live delivery vector, a food-grade recombinant L. lactis producing the B. melitensis BP26 protein was produced.
Studying the impact of young age on prognosis and treatment in laryngeal squamous cell carcinomas using the SEER database

Background Laryngeal squamous cell carcinoma (LSCC) in young patients was reported to be more aggressive and associated with poorer survival than in older patients. However, very few studies contained sufficient cases to permit meaningful statistical analysis. It is still unknown whether less aggressive method like radical radiotherapy (RT) is comparable to total laryngectomy (TL) in survival rate among young patients. Methods This study extracted patient data from the surveillance, epidemiology, and end results database from 2004 to 2015. The findings for 304 patients (1.2%) below the age of 40 were compared with those for 24,827 patients (98.8%) aged 40 or older. Results The younger cohorts contained a higher proportion of female patients (33.6% vs. 19.1%, respectively), had more cases of glottic involvement (64.5% vs. 53.1%), and were less likely to have distant metastasis (0.7% vs.3.2%). A total of 5-year overall survival and cancer-specific survival rates (CSS) in the younger patients were 86.3% and 88.8%, respectively, significantly better than for older patients (53.8% and 67.6%). Significant differences were still observed when stratified for tumor stage (stage I–IV). The negative independent prognostic factors in younger patients were advanced tumor stage, degree of nodal involvement, and status of distant metastasis. Treatment with surgery and/or RT all produced excellent outcomes in stage I–IV diseases, and radical RT resulted in survival rates equal to those for TL in locally advanced LSCC among young patients (5-year CSS: 90% vs. 91.5%, p = 0.99). Conclusion LSCC is less aggressive and has significantly better survival in younger patients. For younger patients, advanced nodal involvement is the most important independent prognostic factor, and larynx preservation is comparable to TL in survival rate.
Lexicon Guided Attentive Neural Network Model for Argument Mining

Identification of argumentative components is an important stage of argument mining. Lexicon information is reported as one of the most frequently used features in the argument mining research. In this paper, we propose a methodology to integrate lexicon information into a neural network model by attention mechanism. We conduct experiments on the UKP dataset, which is collected from heterogeneous sources and contains several text types, e.g., microblog, Wikipedia, and news. We explore lexicons from various application scenarios such as sentiment analysis and emotion detection. We also compare the experimental results of leveraging different lexicons.
Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference

Natural Language inference is the task of identifying relation between two sentences as entailment, contradiction or neutrality. MedNLI is a biomedical flavour of NLI for clinical domain. This paper explores the use of Bidirectional Encoder Representation from Transformer (BERT) for solving MedNLI. The proposed model, BERT pre-trained on PMC, PubMed and fine-tuned on MIMICIII v1.4, achieves state of the art results on MedNLI (83.45%) and an accuracy of 78.5% in MEDIQA challenge. The authors present an analysis of the attention patterns that emerged as a result of training BERT on MedNLI using a visualization tool, bertviz.
Shared Memory High Throughput Computing with Apache Arrow™

As the barriers to entry in scientific computing have lowered with languages like Python and their libraries, the demand for ever more sophisticated and capable frameworks that provide almost turnkey functionality has grown proportionally (see keras). 1 For researchers who use the pilot job,2 many-task3 design pattern for high-throughput computing4 on modern systems, the Apache Arrow project [1] with its now included Plasma in-memory object store provides a high-level interface for sharing data structures between processes in a way that requires no serialization or copying of that data. This poster outlines a common scenario in which a constraint is induced by the way memory is managed. The direct sharing of memory by a common middle data layer allows for accelerated workflows that can operate at a more rapid cadence. A real-world example is provided; the goal is to raise awareness in facilitators at other major research computing centers who work with users to architect similar such high-throughput data pipelines.
JHU System Description for the MADAR Arabic Dialect Identification Shared Task

Our submission to the MADAR shared task on Arabic dialect identification employed a language modeling technique called Prediction by Partial Matching, an ensemble of neural architectures, and sources of additional data for training word embeddings and auxiliary language models. We found several of these techniques provided small boosts in performance, though a simple character-level language model was a strong baseline, and a lower-order LM achieved best performance on Subtask 2. Interestingly, word embeddings provided no consistent benefit, and ensembling struggled to outperform the best component submodel. This suggests the variety of architectures are learning redundant information, and future work may focus on encouraging decorrelated learning.
Revisiting Online Video Popularity: A Sentimental Analysis

Abstract This research deploys sentiment analysis to investigate the influence of comments and construct a video popularity model. We selected eight channels on YouTube to examine the proposed model in terms of 306 videos and 59,103 comments and we collected 200 samples. The results showed high number of views may not result in high positive comments. The influence of own factors of videos such as title of videos, video thumbnail, and content was higher than objective factors such as number of views, like or dislike, and sentiment analysis. Finally, we discovered sentiment indirectly influences the decision of watching behavior.
ChiMed: A Chinese Medical Corpus for Question Answering

Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains. While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch. Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain. In this study, we first collect a large-scale Chinese medical QA corpus called ChiMed; second we annotate a small fraction of the corpus to check the quality of the answers; third, we extract two datasets from the corpus and use them for the relevancy prediction task and the adoption prediction task. Several benchmark models are applied to the datasets, producing good results for both tasks.
(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus

We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation. We verified our GEC system through experiments on a low resource track of the shared task at BEA2019. As a result, we achieved an F0.5 score of 28.31 points with the test data.
Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian

For morphologically rich languages, word embeddings provide less consistent semantic representations due to higher variance in word forms. Moreover, these languages often allow for less constrained word order, which further increases variance. For the highly agglutinative Hungarian, semantic accuracy of word embeddings measured on word analogy tasks drops by 50-75% compared to English. We observed that embeddings learn morphosyntax quite well instead. Therefore, we explore and evaluate several sub-word unit based embedding strategies – character n-grams, lemmatization provided by an NLP-pipeline, and segments obtained in unsupervised learning (morfessor) – to boost semantic consistency in Hungarian word vectors. The effect of changing embedding dimension and context window size have also been considered. Morphological analysis based lemmatization was found to be the best strategy to improve embeddings’ semantic accuracy, whereas adding character n-grams was found consistently counterproductive in this regard.
A comparison of statistical association measures for identifying dependency-based collocations in various languages.

This paper presents an exploration of different statistical association measures to automatically identify collocations from corpora in English, Portuguese, and Spanish. To evaluate the impact of the association metrics we manually annotated corpora with three different syntactic patterns of collocations (adjective-noun, verb-object and nominal compounds). We took advantage of the PARSEME 1.1 Shared Task corpora by selecting a subset of 155k tokens in the three referred languages, in which we annotated 1,526 collocations with the corresponding Lexical Functions according to the Meaning-Text Theory. Using the resulting gold-standard, we have carried out a comparison between frequency data and several well-known association measures, both symmetric and asymmetric. The results show that the combination of dependency triples with raw frequency information is as powerful as the best association measures in most syntactic patterns and languages. Furthermore, and despite the asymmetric behaviour of collocations, directional approaches perform worse than the symmetric ones in the extraction of these phraseological combinations.
Without lexicons, multiword expression identification will never fly: A position statement

Because most multiword expressions (MWEs), especially verbal ones, are semantically non-compositional, their automatic identification in running text is a prerequisite for semantically-oriented downstream applications. However, recent developments, driven notably by the PARSEME shared task on automatic identification of verbal MWEs, show that this task is harder than related tasks, despite recent contributions both in multilingual corpus annotation and in computational models. In this paper, we analyse possible reasons for this state of affairs. They lie in the nature of the MWE phenomenon, as well as in its distributional properties. We also offer a comparative analysis of the state-of-the-art systems, which exhibit particularly strong sensitivity to unseen data. On this basis, we claim that, in order to make strong headway in MWE identification, the community should bend its mind into coupling identification of MWEs with their discovery, via syntactic MWE lexicons. Such lexicons need not necessarily achieve a linguistically complete modelling of MWEs’ behavior, but they should provide minimal morphosyntactic information to cover some potential uses, so as to complement existing MWE-annotated corpora. We define requirements for such minimal NLP-oriented lexicon, and we propose a roadmap for the MWE community driven by these requirements.
Supporting content evaluation of student summaries by Idea Unit embedding

This paper discusses the computer-assisted content evaluation of summaries. We propose a method to make a correspondence between the segments of the source text and its summary. As a unit of the segment, we adopt “Idea Unit (IU)” which is proposed in Applied Linguistics. Introducing IUs enables us to make a correspondence even for the sentences that contain multiple ideas. The IU correspondence is made based on the similarity between vector representations of IU. An evaluation experiment with two source texts and 20 summaries showed that the proposed method is more robust against rephrased expressions than the conventional ROUGE-based baselines. Also, the proposed method outperformed the baselines in recall. We im-plemented the proposed method in a GUI tool“Segment Matcher” that aids teachers to estab-lish a link between corresponding IUs acrossthe summary and source text.
IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting Classifier

Official System Description paper of Team IIT-KGP ranked 1st in the Development phase and 3rd in Testing Phase in MEDIQA 2019 - Recognizing Question Entailment (RQE) Shared Task of BioNLP workshop - ACL 2019. The number of people turning to the Internet to search for a diverse range of health-related subjects continues to grow and with this multitude of information available, duplicate questions are becoming more frequent and finding the most appropriate answers becomes problematic. This issue is important for question answering platforms as it complicates the retrieval of all information relevant to the same topic, particularly when questions similar in essence are expressed differently, and answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution. In this paper, we present our novel approach to detect question entailment by determining the type of question asked rather than focusing on the type of the ailment given. This unique methodology makes the approach robust towards examples which have different ailment names but are synonyms of each other. Also, it enables us to check entailment at a much more fine-grained level. QSpider is a staged system consisting of state-of-the-art model Sci-BERT used as a multi-class classifier aimed at capturing both question types and semantic relations stacked with a Gradient Boosting Classifier which checks for entailment. QSpider achieves an accuracy score of 68.4% on the Test set which outperforms the baseline model (54.1%) by an accuracy score of 14.3%.
Hierarchical Deep Learning for Arabic Dialect Identification

In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F1 score of 63.02 % on the subtask evaluation dataset.
DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering

In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks. AMTN utilizes a pre-trained BioBERT model and an Interactive Transformer to learn the shared semantic representations across different task through parameter sharing mechanism. Meanwhile, an adversarial training strategy is introduced to separate the private features of each task from the shared representations. Experiments on BioNLP 2019 RQE and QA Shared Task datasets show that our model benefits from the shared representations of both tasks provided by multi-task learning and adversarial training, and obtains significant improvements upon the single-task models.
CHURP: A Lightweight CLI Framework to Enable Novice Users to Analyze Sequencing Datasets in Parallel

Progressive decreases in the cost of DNA sequencing have contributed to a decades-long exponential increase in the production of new sequencing datasets. The processing of these datasets has in turn led biology, a field that has traditionally relied on local "lab" servers to address its computational needs, to become increasingly reliant on High Performance Computing (HPC) resources. Though many operations on sequencing datasets are trivially parallelizable on multiple levels, the lack of an HPC tradition in biological research has hampered fully parallelized deployments. Here we present a lightweight flexible framework for performing parallelized processing of raw gene expression data. The framework uses a Python3 based frontend for specifying analysis options, data paths, and reference datasets. This frontend sanitizes and resolves the options, providing verbose error checking before writing a human readable configuration file and basic scripts for batch submission. The submission scripts leverage the scheduler to implement a scatter-gather approach, submitting potentially hundreds of individual jobs via a job array, each small enough to take advantage of backfill in a high contention HPC environment. The gather component is handled through a script submitted with an "after-okay" dependency.
Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian

We present a new concept prerequisite learning method for Learning Object (LO) ordering that exploits only linguistic features extracted from textual educational resources. The method was tested in a cross- and in- domain scenario both for Italian and English. Additionally, we performed experiments based on a incremental training strategy to study the impact of the training set size on the classifier performances. The paper also introduces ITA-PREREQ, to the best of our knowledge the first Italian dataset annotated with prerequisite relations between pairs of educational concepts, and describe the automatic strategy devised to build it.
Application-Level Unsupervised Outlier-Based Intrusion Detection and Prevention

As cyber threats are permanently jeopardizing individuals privacy and organizations’ security, there have been several efforts to empower software applications with built-in immunity. In this paper, we present our approach to immune applications through application-level, unsupervised, outlier-based intrusion detection and prevention. Our framework allows tracking application domain objects all along the processing lifecycle. It also leverages the application business context and learns from production data, without creating any training burden on the application owner. Moreover, as our framework uses runtime application instrumentation, it incurs no additional cost on the application provider. We build a fine-grained and rich-feature application behavioral model that gets down to the method level and its invocation context. We define features to be independent from the variable structure of method invocation parameters and returned values, while preserving security-relevant information. We implemented our framework in a Java environment and evaluated it on a widely-used, enterprise-grade, and open-source ERP. We tested several unsupervised outlier detection algorithms and distance functions. Our framework achieved the best results in terms of effectiveness using the Local Outlier Factor algorithm and the Clark distance, while the average instrumentation overhead per intercepted call remains acceptable.
Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection

Stance detection plays a pivot role in fake news detection. The task involves determining the point of view or stance – for or against – a text takes towards a claim. One very important stage in employing stance detection for fake news detection is the aggregation of multiple stance labels from different text sources in order to compute a prediction for the veracity of a claim. Typically, aggregation is treated as a credibility-weighted average of stance predictions. In this work, we take the novel approach of applying, for aggregation, a gradual argumentation semantics to bipolar argumentation frameworks mined using stance detection. Our empirical evaluation shows that our method results in more accurate veracity predictions.
A Cascade Model for Proposition Extraction in Argumentation

We present a model to tackle a fundamental but understudied problem in computational argumentation: proposition extraction. Propositions are the basic units of an argument and the primary building blocks of most argument mining systems. However, they are usually substituted by argumentative discourse units obtained via surface-level text segmentation, which may yield text segments that lack semantic information necessary for subsequent argument mining processes. In contrast, our cascade model aims to extract complete propositions by handling anaphora resolution, text segmentation, reported speech, questions, imperatives, missing subjects, and revision. We formulate each task as a computational problem and test various models using a corpus of the 2016 U.S. presidential debates. We show promising performance for some tasks and discuss main challenges in proposition extraction.
No Army, No Navy: BERT Semi-Supervised Learning of Arabic Dialects

We present our deep leaning system submitted to MADAR shared task 2 focused on twitter user dialect identification. We develop tweet-level identification models based on GRUs and BERT in supervised and semi-supervised set-tings. We then introduce a simple, yet effective, method of porting tweet-level labels at the level of users. Our system ranks top 1 in the competition, with 71.70% macro F1 score and 77.40% accuracy.