Miracle-dz/UniFAR-FTUs huggingface dataset:
Enterprise Cybersecurity: Investigating and Detecting Ransomware Infections Using Digital Forensic Techniques

As the world continues to grow and embrace technology ransomware is growing problem. When ransomware encrypts storage sytems, systems shutdown, productivity grinds to a halt, and serious long-term damage takes place. As this is a known problem many firms have developed functionality to address ransomware issues in key security technologies such as intrusion protection systems. Many firms, especially smaller ones, may not have access to these technologies or perhaps the integration of these technologies might not yet be possible due ot varying circumstances. Regardless, ransomware must still be addressed as cyber miscreants actually target weak and unprotected environment. Even without tools that automate and aggregrate security capability, systems administrators can use systems utilities, applications, and digital forensic techniques to detect ransomware and defend their environemnts. This paper explores the literature regarding ransomware attacks, discusses current issues on how ransomware might be addressed, and presents recommendations to detect and investigate ransomware infection.
TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track

We introduce our system that is submitted to the restricted track of the BEA 2019 shared task on grammatical error correction1 (GEC). It is essential to select an appropriate hypothesis sentence from the candidates list generated by the GEC model. A re-ranker can evaluate the naturalness of a corrected sentence using language models trained on large corpora. On the other hand, these language models and language representations do not explicitly take into account the grammatical errors written by learners. Thus, it is not straightforward to utilize language representations trained from a large corpus, such as Bidirectional Encoder Representations from Transformers (BERT), in a form suitable for the learner’s grammatical errors. Therefore, we propose to fine-tune BERT on learner corpora with grammatical errors for re-ranking. The experimental results of the W&I+LOCNESS development dataset demonstrate that re-ranking using BERT can effectively improve the correction performance.
MagmaDNN: Accelerated Deep Learning Using MAGMA

MagmaDNN [17] is a deep learning framework driven using the highly optimized MAGMA dense linear algebra package. The library offers comparable performance to other popular frameworks, such as TensorFlow, PyTorch, and Theano. C++ is used to implement the framework providing fast memory operations, direct cuda access, and compile time errors. Common neural network layers such as Fully Connected, Convolutional, Pooling, Flatten, and Dropout are included. Hyperparameter tuning is performed with a parallel grid search engine. MagmaDNN uses several techniques to accelerate network training. For instance, convolutions are performed using the Winograd algorithm and FFTs. Other techniques include MagmaDNNs custom memory manager, which is used to reduce expensive memory transfers, and accelerated training by distributing batches across GPU nodes. This paper provides an overview of the MagmaDNN framework and how it leverages the MAGMA library to attain speed increases. This paper also addresses how deep networks are accelerated by training in parallel and further challenges with parallelization.
Unsupervised Compositional Translation of Multiword Expressions

This article describes a dependency-based strategy that uses compositional distributional semantics and cross-lingual word embeddings to translate multiword expressions (MWEs). Our unsupervised approach performs translation as a process of word contextualization by taking into account lexico-syntactic contexts and selectional preferences. This strategy is suited to translate phraseological combinations and phrases whose constituent words are lexically restricted by each other. Several experiments in adjective-noun and verb-object compounds show that mutual contextualization (co-compositionality) clearly outperforms other compositional methods. The paper also contributes with a new freely available dataset of English-Spanish MWEs used to validate the proposed compositional strategy.
Detecting Argumentative Discourse Acts with Linguistic Alignment

We report the results of preliminary investigations into the relationship between linguistic alignment and dialogical argumentation at the level of discourse acts. We annotated a proof of concept dataset with illocutions and transitions at the comment level based on Inference Anchoring Theory. We estimated linguistic alignment across discourse acts and found significant variation. Alignment features calculated at the dyad level are found to be useful for detecting a range of argumentative discourse acts.
Content Customization for Micro Learning using Human Augmented AI Techniques

Visual content has been proven to be effective for micro-learning compared to other media. In this paper, we discuss leveraging this observation in our efforts to build audio-visual content for young learners’ vocabulary learning. We attempt to tackle two major issues in the process of traditional visual curation tasks. Generic learning videos do not necessarily satisfy the unique context of a learner and/or an educator, and hence may not result in maximal learning outcomes. Also, manual video curation by educators is a highly labor-intensive process. To this end, we present a customizable micro-learning audio-visual content curation tool that is designed to reduce the human (educator) effort in creating just-in-time learning videos from a textual description (learning script). This provides educators with control of the content while preparing the learning scripts, and in turn can also be customized to capture the desired learning objectives and outcomes. As a use case, we automatically generate learning videos with British National Corpus’ (BNC) frequently spoken vocabulary words and evaluate them with experts. They positively recommended the generated learning videos with an average rating of 4.25 on a Likert scale of 5 points. The inter-annotator agreement between the experts for the video quality was substantial (Fleiss Kappa=0.62) with an overall agreement of 81%.
Improving Precision of Grammatical Error Correction with a Cheat Sheet

In this paper, we explore two approaches of generating error-focused phrases and examine whether these phrases can lead to better performance in grammatical error correction for the restricted track of BEA 2019 Shared Task on GEC. Our results show that phrases directly extracted from GEC corpora outperform phrases from statistical machine translation phrase table by a large margin. Appending error+context phrases to the original GEC corpora yields comparably high precision. We also explore the generation of artificial syntactic error sentences using error+context phrases for the unrestricted track. The additional training data greatly facilitates syntactic error correction (e.g., verb form) and contributes to better overall performance.
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning

The availability of large-scale and real-time data on social media has motivated research into adverse drug reactions (ADRs). ADR classification helps to identify negative effects of drugs, which can guide health professionals and pharmaceutical companies in making medications safer and advocating patients’ safety. Based on the observation that in social media, negative sentiment is frequently expressed towards ADRs, this study presents a neural model that combines sentiment analysis with transfer learning techniques to improve ADR detection in social media postings. Our system is firstly trained to classify sentiment in tweets concerning current affairs, using the SemEval17-task4A corpus. We then apply transfer learning to adapt the model to the task of detecting ADRs in social media postings. We show that, in combination with rich representations of words and their contexts, transfer learning is beneficial, especially given the large degree of vocabulary overlap between the current affairs posts in the SemEval17-task4A corpus and posts about ADRs. We compare our results with previous approaches, and show that our model can outperform them by up to 3% F-score.
UU_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain

This article describes the participation of the UU_TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP. The challenge consists of 3 tasks: medical language inference (NLI), recognizing textual entailment (RQE) and question answering (QA). Our team participated in tasks 1 and 2 and our best runs achieved a performance accuracy of 0.852 and 0.584 respectively for the test sets. The models proposed for task 1 relied on BERT embeddings and different ensemble techniques. For the RQE task, we trained a traditional multilayer perceptron network based on embeddings generated by the universal sentence encoder.
Simplification-induced transformations: typology and some characteristics

The purpose of automatic text simplification is to transform technical or difficult to understand texts into a more friendly version. The semantics must be preserved during this transformation. Automatic text simplification can be done at different levels (lexical, syntactic, semantic, stylistic.) and relies on the corresponding knowledge and resources (lexicon, rules.). Our objective is to propose methods and material for the creation of transformation rules from a small set of parallel sentences differentiated by their technicity. We also propose a typology of transformations and quantify them. We work with French-language data related to the medical domain, although we assume that the method can be exploited on texts in any language and from any domain.
An Empirical Study on Pre-trained Embeddings and Language Models for Bot Detection

Fine-tuning pre-trained language models has significantly advanced the state of art in a wide range of NLP downstream tasks. Usually, such language models are learned from large and well-formed text corpora from e.g. encyclopedic resources, books or news. However, a significant amount of the text to be analyzed nowadays is Web data, often from social media. In this paper we consider the research question: How do standard pre-trained language models generalize and capture the peculiarities of rather short, informal and frequently automatically generated text found in social media? To answer this question, we focus on bot detection in Twitter as our evaluation task and test the performance of fine-tuning approaches based on language models against popular neural architectures such as LSTM and CNN combined with pre-trained and contextualized embeddings. Our results also show strong performance variations among the different language model approaches, which suggest further research.
The SMarT Classifier for Arabic Fine-Grained Dialect Identification

This paper describes the approach adopted by the SMarT research group to build a dialect identification system in the framework of the Madar shared task on Arabic fine-grained dialect identification. We experimented several approaches, but we finally decided to use a Multinomial Naive Bayes classifier based on word and character ngrams in addition to the language model probabilities. We achieved a score of 67.73% in terms of Macro accuracy and a macro-averaged F1-score of 67.31%
Anglicized Words and Misspelled Cognates in Native Language Identification

In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second language (L2). In particular, we look at misspelled words that indicate lexical uncertainty on the part of the author, and separate them into three categories: misspelled cognates, “L2-ed” (in our case, anglicized) words, and all other spelling errors. We test the assumption that such errors contain clues about the native language of an essay’s author through the task of native language identification. The results of the experiments show that the information brought by each of these categories is complementary. We also note that while the distribution of such features changes with the proficiency level of the writer, their contribution towards native language identification remains significant at all levels.
Microwheels on microroads: Enhanced translation on topographic surfaces

Matching textured surfaces to microwheel sizes and symmetries may lead to more efficient propulsion of microdevices. Microbot locomotion is challenging because of the reversible nature of microscale fluid flow, a limitation that can be overcome by breaking flowfield symmetry with a nearby surface. We have used this strategy with rotating wheel-shaped microbots, microwheels (μwheels), that roll on surfaces leading to enhanced propulsion and fast translation speeds. Despite this, studies to date on flat surfaces show that μwheels roll inefficiently with substantial slip. Taking inspiration from the mathematics of roads and wheels, we demonstrate that μwheel velocities can be significantly enhanced by changing microroad topography. Here, we observe that periodic bumps in the road can be used to enhance the traction between μwheels and nearby walls. Whereas continuous μwheel rotation with slip is observed on flat surfaces, a combination of rotation with slip and nonslip flip occurs when μwheels roll on surfaces with periodic features, resulting in up to fourfold enhancement in translation velocity. The unexpectedly fast rolling speed of μwheels on bumpy roads can be attributed to the hydrodynamic coupling between μwheels and road surface features, allowing nonslip rotation of entire wheels along one of their stationary edges. This road-wheel coupling can also be used to enhance μwheel sorting and separation where the gravitational potential energy barrier induced by topographic surfaces can lead to motion in only one direction and to different rolling speeds between isomeric wheels, allowing one to separate them not based on size but on symmetry.
3D bioprinting of mature bacterial biofilms for antimicrobial resistance drug testing

The potential to bioprint and study 3D bacterial biofilm constructs could have great clinical significance at a time when antimicrobial resistance is rising to dangerously high levels worldwide. In this study, clinically relevant bacterial species including Escherichia coli, Staphylococcus aureus (MSSA), Methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa were 3D bioprinted using a double-crosslinked alginate bioink to form mature bacteria biofilms, characterized by confocal laser scanning microscopy (CLSM) and fluorescent staining. Solid and porous bacteria-laden constructs were reproducibly bioprinted with thicknesses ranging from 0.25 to 4 mm. We demonstrated 3D bioprinting of thicker biofilms (>4 mm) than found in currently available in vitro models. Bacterial viability was excellent in the bioprinted constructs, with CLSM observation of bacterial biofilm production and maturation possible for at least 28 d in culture. Importantly, we observed the complete five-step biofilm life cycle in vitro following 3D bioprinting for the first time, suggesting the formation of mature 3D bioprinted biofilms. Bacterial growth was faster in thinner, more porous constructs whilst constructs crosslinked with BaCl2 concentrations of above 10 mM had denser biofilm formation. 3D MRSA and MSSA biofilm constructs were found to show greater resistance to antimicrobials than corresponding two-dimensional (2D) cultures. Thicker 3D E. coli biofilms had greater resistance to tetracycline than thinner constructs over 7 d of treatment. Our methodology allowed for the precise 3D bioprinting of self-supporting 3D bacterial biofilm structures that developed biofilms during extended culture. 3D biofilm constructs containing bacterial biofilms produce a model with much greater clinical relevance compared to 2D culture models and we have demonstrated their use in antimicrobial testing.
Perturbations of urea cycle enzymes during post-hepatectomy rat liver failure.

Post-hepatectomy liver failure (PHLF) may occur after extended partial hepatectomy (PH). If malignancy is widespread in the liver, the size of PH and hence the size of the future liver remnant (FLR) may limit curability. We aimed to characterize differences in protein expression between different sizes of FLRs and to identify proteins specific to the regenerative process of minimal-size FLR (MSFLR), with special focus on postoperative day (POD) 1 when PHLF is present.A total of 104 male Wistar rats were subjected to 30%, 70%, or 90% PH (MSFLR in rats); sham or no operation. Blood and liver tissue harvested at POD1, 3, and 5 (n=8 per group). Protein expression was assessed by proteomic profiling by unsupervised2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) liquid chromatography tandem mass spectrometry (LC-MS/MS) followed by supervisedselected reaction monitoring (SRM)-MS/MS. In all,1,035 protein spots were detected of which 54 were significantly differentially expressed between groups and identifiable. During PHLF following PH(90%) at POD1, urea cycle and related proteins showed significant perturbations counting the urea cycle flux regulating enzyme of carbamoyl phosphate synthase-1, the ornithine transcarbamylase, and arginase-1 as well as the ornithine aminotransferase and propionyl-CoA carboxylase alpha chain. Plasma-ammonia increased significantly at POD1 after PH(90%) followed by a prompt decrease. Conclusion:At the protein level, we found perturbations of urea cycle and related enzymes in the MSFLR during PHLF. Our results suggest that these perturbations may augment urea cycle function which may be pivotal for increased ammonia elimination after extensive PHs and potential PHLF.
Assessment of Antibiotic and Pesticides Residues in Breast Milk of Syrian Refugee Lactating Mothers

Occupational exposures and current diet are both sources of environmental contaminants that can be transferred in the mother’s body. These chemicals can definitely penetrate to the developing foetus and the nursing infant from contaminated breast milk during the lactation period. Nowadays, one of the special interests is the exposure of new-borns to toxic chemicals such as pesticides and antibiotics reported in human milk due to their potential harms, especially developmental deficits in early childhood. The aim of our current study was to assess the occurrence of pesticide residues and antibiotic residues contamination in breast milk collected from Syrian refugee lactating mothers residing in North Lebanon Camps. A total of 120 breast milk samples (40 in triplicate) were collected from camps in Akkar, North Lebanon using an electrical pump. A survey was administrated to determine socio-demographic characteristics, dietary and smoking habits and medical history of participating lactating mothers. The milk samples were analysed for the presence of antibiotic residues and pesticide residues using liquid and gas chromatography tandem mass spectroscopy (LC-MS/MS) and Gas Chromatography-Tandem Mass Spectrometry (GC-MS/MS). This study reported the absence of antibiotic residues in 96.66% of our samples (n = 120) and the presence of pesticides residues in only 5% of our total breast milk sample. Our results considered the breast milk collected from Syrian refugee lactating mothers as safe from chemical contamination. It is worth conducting more studies on other Syrian refugee camps to test the effect of camp living conditions on breast milk safety.
A Cost-Effectiveness Analysis of Rooming-in and Breastfeeding in Neonatal Opioid Withdrawal

Abstract Objective Our cost-effectiveness analysis investigated rooming-in versus not rooming-in to determine optimal management of neonates with neonatal opioid withdrawal (NOW). Study Design A decision-analytic model was constructed using TreeAge to compare rooming-in versus not rooming-in in a theoretical cohort of 23,200 newborns, the estimated annual number affected by NOW in the United States. Additional considerations included the effect of breast milk versus formula milk in evaluating the need for pharmacotherapy. Primary outcomes were needed for pharmacotherapy and neurodevelopment. We assumed a societal perspective in evaluating costs and maternal-neonatal quality-adjusted life years (QALYs) using a willingness-to-pay threshold of $100,000/QALY. Model inputs were derived from literature and varied in sensitivity analyses. Results Rooming-in resulted in fewer neonates requiring pharmacotherapy when compared with not rooming-in. The rooming-in group had more neonates with intact/mild neurodevelopmental impairment and fewer cases of moderate to severe impairment. Rooming-in resulted in cost savings of $509,652,728 and 12,333 additional QALYs per annual cohort. When the risk ratio of need for pharmacotherapy in rooming-in was varied across a clinically plausible range, rooming-in remained the cost-effective strategy. Conclusion Maternal rooming-in with newborns affected by NOW leads to reduced costs and increased effectiveness. Management strategies should optimize nonpharmacological interventions as first-line treatment.
Machine learning applications for the prediction of surgical site infection in neurological operations.

OBJECTIVE Surgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations. METHODS A retrospective cohort study was conducted on patients who had undergone neurosurgical operations at tertiary care hospitals between 2010 and 2017. Supervised ML algorithms, which included decision tree, naive Bayes with Laplace correction, k-nearest neighbors, and artificial neural networks, were trained and tested as binary classifiers (infection or no infection). To evaluate the ML models from the testing data set, their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their accuracy, receiver operating characteristic curve, and area under the receiver operating characteristic curve (AUC) were analyzed. RESULTS Data were available for 1471 patients in the study period. The SSI rate was 4.6%, and the type of SSI was superficial, deep, and organ/space in 1.2%, 0.8%, and 2.6% of cases, respectively. Using the backward stepwise method, the authors determined that the significant predictors of SSI in the multivariable Cox regression analysis were postoperative CSF leakage/subgaleal collection (HR 4.24, p < 0.001) and postoperative fever (HR 1.67, p = 0.04). Compared with other ML algorithms, the naive Bayes had the highest performance with sensitivity at 63%, specificity at 87%, PPV at 29%, NPV at 96%, and AUC at 76%. CONCLUSIONS The naive Bayes algorithm is highlighted as an accurate ML method for predicting SSI after neurosurgical operations because of its reasonable accuracy. Thus, it can be used to effectively predict SSI in individual neurosurgical patients. Therefore, close monitoring and allocation of treatment strategies can be informed by ML predictions in general practice.