Miracle-dz/UniFAR-FTUs huggingface dataset:
Named Entity Recognition and Linking in a Multilingual Biomedical Setting

Information analysis is an essential process for all researchers and physicians. However, the amount of biomedical literature that we currently have available and the format in which it is found make this process difficult. Therefore, it is essential to apply text mining tools to automatically obtain information from these documents. The problem is that most of these tools are not designed to deal with non-English languages, which is critical in the biomedical literature, since many of these documents are written in the authors’ native language. Although there have been organized several shared tasks where text mining tools were developed for the Spanish language, the same does not happen for the Portuguese language. However, due to the lexical similarity between the two languages, it is possible to hypothesize that the tools for the two languages may be similar and that there is an annotation transfer between Portuguese and Spanish. To contribute to the development of text mining tools for Portuguese and Spanish, this dissertation presents the ICERL (Iberian Cancer-related Entity Recognition and Linking) system, a NERL (Named Entity Recognition and Linking) system that uses deep learning and it is composed of two similar pipelines for each language, and the parallel corpus ICR (Iberian Cancer-related) corpus. Both these tools are focused on the oncology domain. The application of the ICERL system on the ICR corpus resulted in 3,999 annotations in Spanish and 3,287 in Portuguese. The similarities between the annotations of the two languages and the F1-score of 0.858 that resulted from the comparison of the Portuguese annotations with the Spanish ones confirm the hypothesis initially presented.
Character-Level Encoding with S4 for QA

We propose a question answering model that encodes text inputs at character level to utilize subword structures and mitigate the out-of-vocabulary problem. Character-level inputs are quite long compared to word sequences, so the model encoding them should be able to handle long-range dependencies effectively and efficiently. S4 (Structured State Space sequence model) is such a model that fulfills this requirement. We build our model based on QANet, replacing all the CNN and self-attention layers with S4 layers, and feeding it with character embeddings. To make the model benefit both from character-level information and word-level information, our model first encodes inputs at character-level, and then aggregates the character-level hidden states to word-level hidden states by a max pooling operation, so that word-level modeling can be applied in the deeper stages of the network. Experiments on SQuAD 2.0 show that both S4 and character-level encoding improve the model performance on the question answering task. Case studies attribute part of this improvement to the out-of-vocabulary problem.
Literature Review Reveals a Global Access Inequity to Urban Green Spaces

: Differences in the accessibility to urban resources between different racial and socioeconomic groups have exerted pressure on effective planning and management for sustainable city development. However, few studies have examined the multiple factors that may inﬂuence the mitigation of urban green spaces (UGS) inequity. This study reports the results of a systematic mapping of access inequity research through correspondence analysis (CA) to reveal critical trends, knowledge gaps, and clusters based on a sample of 49 empirical studies screened from 563 selected papers. Our ﬁndings suggest that although the scale of cities with UGS access inequity varies between countries, large cities (more than 1,000,000 population), especially in low- and middle-income countries (LMICs), are particularly affected. Moreover, the number of cities in which high socioeconomic status (high-SES) groups (e.g., young, rich, or employed) are at an advantage concerning access to UGS is substantially higher than the number of cities showing better accessibility for low-SES groups. Across the reviewed papers, analyses on mitigating interventions are sparse, and among the few studies that touch upon this, we found different central issues in local mitigating strategies between high-income countries (HICs) and LMICs. An explanatory framework is offered, explaining the interaction between UGS access inequity and local mitigating measures.
SUSTAINING ARABIC LANGUAGE LEARNING STRATEGIES THROUGH EFFECTIVE TEACHING AND MOTIVATION

Arabic language is a challenging subject yet worth to learn it. As for non-Arab students, it is difficult for them to learn Arabic language without support such as self-determination and effective instruction especially those who are not a native speaker. The study aimed to determine the way to sustain Arabic language learning among secondary school students through motivation and teaching effectiveness. A simple model consisted teaching effectiveness, motivational beliefs and self-regulated learning also shown to explained the result. This study uses quantitative design through survey method. A total of 542 non-Arab students from several secondary schools in Malaysia were selected using stratified random sampling techniques. Students’ Evaluation of Teaching Effectiveness Rating Scale (SETERS) and Motivational Strategies for Learning Questionnaire (MSLQ) through Motivational Beliefs Scale and Self-Regulated Learning Strategies Scale were used as an instruments. A multiple and hierarchical regression was conducted for data analysis. Furthermore, a structural equation model also derived to strengthen the result by showing an indexes fitting the model. The findings showed that teaching effectiveness and motivational beliefs significantly influence self-regulated learning in Arabic language learning. The best predictor for self-regulated learning is the intrinsic value, test anxiety, self-efficacy and teachers’ delivery of subject information. The findings also show how self-regulated learning can improve and empower the Arabic language learning in Malaysia. This study has indicated an important implication for teachers to improvising teaching methods and for students to motivate them-self in order to catalyze self-regulated learning to improve Arabic language performance.
Mediating Effects of Workplace Learning and Self-efficacy on the Relationship between Technostress and Job Satisfaction of Convalescent Hospital Nurses

This study was tried to explore the mediating effect of workplace learning and self-efficacy on the relationship between technostress and job satisfaction in convalescent hospital nurses. Data were collected from 149 nurses working at one of 10 convalescent hospitals located in Korea’s D region and between July 20 and August 12, 2019 and analyzed using SPSS 24.0. The mediating effects of workplace learning and self-efficacy in the relationship between technostress and job satisfaction were investigated by conducting hierarchical regression analysis and testing for significance based on bootstrapping p values. We found that workplace learning had a complete mediating effect, and self-efficacy a partial mediating effect, in the relationship between technostress and job satisfaction in convalescent hospital nurses. Exploring diverse factors and environmental features affecting job satisfaction in convalescent hospital nurses is highly relevant to clinicians, especially given the gradually increasing number of convalescent hospitals, changes during the era of technology fusion, and the strategic demands arising from an aging society.
Assessment of grid-side filters for three-phase Current-Source Inverter PV systems

In this paper, three grid-side filter topologies for grid-connected three-phase photovoltaic current-source inverters are designed and assessed based on the Total Harmonic Distortion (THD) of the grid currents, on the power factor in the connection to the grid, and on the filter losses. A second-order CL filter with passive damping resistors in parallel with the filter inductors is first sized and evaluated. It is then improved by removing the resistors and using active damping, with the virtual resistor method and feedback of the inductor voltages requiring no additional measurements. The analysis is conducted taking into account the delays introduced in the control circuit and the cross-terms in the state-space equations in the synchronous dq frame. A fourth-order CLCL filter is also sized and considered as an alternative grid-side filter topology to reduce the switching harmonics. Simulation and experimental results obtained with a direct control approach confirm the proper sizing and operation of the filters. Based on the THD of the grid currents and filter losses, the second-order filter with active damping presents the best results with a THD lower than 5 % and nearly negligible losses.
Hydrometeorological analysis of the 12 and 13 September 2019 widespread flash flooding in eastern Spain

On 12 and 13 September 2019, a long-lasting heavy precipitation episode (HPE) affected the València, Murcia and Almería regions in eastern Spain. Observed rainfall amounts were close to 500 mm in 48 h, being the highest cumulative precipitation registered in some rain gauges for the last century. Subsequent widespread flash flooding caused seven fatalities and estimated economical losses above EUR 425 million. High-resolution precipitation estimates from weather radar observations and flood response from stream gauges are used in combination with a fully distributed hydrological model to examine the main hydrometeorological processes within the HyMeX programme. This HPE was characterized by successive, well-organized convective structures that impacted a spatial extent of 7500 km2, with rainfall amounts equal to or larger than 200 mm. The main factors driving the flood response were quasistationarity of heavy precipitation, very dry initial soil moisture conditions and large storage capacities. Most of the examined catchments exhibited a dampened and delayed hydrological response to cumulative precipitation: until runoff thresholds were exceeded, infiltration-excess runoff generation did not start. This threshold-based hydrological behaviour may impact the shape of flood peak distributions, hindering strict flood frequency statistical analysis due to the generally limited lengths of data records in arid and semiarid catchments. As an alternative, a power-law relationship is analytically derived to specifically link flood magnitude to rainfall amount, with the parameters depending on basin and precipitation features. This power law could aid in developing empirical envelope curves so as to assess hydrologic risk in arid and semi-arid catchments with a threshold-based hydrological behaviour.
Incorporating Attitude towards Islamic Banking in a Customer Loyalty Model

: The objective of the study is to examine the mediating effect of switching costs between the relationship of customer satisfaction on customer loyalty in islamic banking. 283 respondents were used as samples using purposive sampling. By structural equation model (SEM) the ﬁndings show that of the six hypotheses, ﬁve hypotheses are supported, one hypothesis is not supported. The results of the study show, customer satisfaction affects customer loyalty both directly and indirectly through switching costs. These results are supported by previous ﬁndings, and at the same time, they reveal that switching costs do not fully mediate the inﬂuence of customer satisfaction on customer loyalty. The direct inﬂuence of customer satisfaction on customer loyalty shows the evidence.
BILINGUALISM AND CULTURAL IDENTITY: EXPLORING TEACHER AND STUDENTS’ USE OF CODE-SWITCHING IN CULTURAL EXPRESSIONS

This qualitative research was conducted to investigate how the teacher and students’ cultural identities are discursively constructed and revealed by code-switching in an English language classroom. It made use of the critical ethnographic approach to studying language use guided by Fairclough’s (1992) Critical Discourse Analysis. The findings of the study revealed that both the teacher and students utilized code-switching during class interactions not only to express their linguistic backgrounds but their cultural identities as well. Transcripts of the class discussion also revealed that the teacher and students’ use of code-switching during class interactions was related to relevance of the topic discussed or question asked, familiarity of context presented, and one’s perceived attachment to a particular cultural group. Furthermore, emotion, context, and group membership were the factors that strongly influenced the teacher and students to use code-switching in the expression of their cultural identities during the class interactions. study how speakers code-switch may help understand how they establish their specific identities locally within their interactions.
You Just Want Attention

Question Answering is a major domain of interest in the intersection of information retrieval and natural language processing, mainly due to its wide application in search engines, chat systems, robotics, medical and literature. In this project, we build a QA model based on one of the most popular NLP datasets, Stanford Question Answering Dataset (SQuAD) 2.0. We implement a robust QANet model from scratch, and experiment with its parameters and architecture. We analyze the effect of different embeddings, and compare the efficiency of attention-based QANet and BiDAF (Bi-Directional Attention Flow) based models. Finally, we will develop a weighted majority voting-based ensemble model to improve the overall performance of different base model variations. We achieve a F1/EM score of 71.63/68.85 in the dev set (Rank 5), and 69.42/66.46 in the test set (Rank 6) of the CS224N IID SQuAD Track leaderboard.
18 F-FES PET-CT Scan in Patients of Histopathologically Proven Estrogen Receptor Positive Breast Carcinoma at a Tertiary Care Facility

This work was carried out in collaboration among all authors. Author SA designed the study, performed the statistical analysis, wrote the protocol and author MNY and author TY did the literature survey, prepared the manuscript, supervised and interpreted the scans. Author IUK performed the preparation and quality control testing of FES and author AF designed the study acquisition protocol and author MM did the patient selection and helped in discussion writing. Author AS helped in obtaining ethical approval, analysis of results and literature searches. All authors read and approved the final manuscript. ABSTRACT Aims: To ascertain successful labeling and image performance of 18 F-FES PET-CT scan in patients who have histopathologically diagnosed estrogen positive breast cancer. Study Design: Histopathologically proven estrogen receptor positive patients were enrolled for 18 F-FES PET-CT scan. Methodology: by radio-fluorination of 3-O-Case methoxymethyl-16,17 O sulfuryl-16-epiestriol followed by acidic hydrolysis at INMOL. After quality control tests, 18F-FES was injected to the patients. At 60 minutes post injection, each patient underwent PET-CT imaging in supine position as per standard protocol given in the published literature. Image interpretation and processing was performed. Results: Study was performed on 2 patients. The first patient had estrogen receptor positive breast tumour on trucut biopsy and the second patient presented after excision biopsy. The second patient demonstrated skeletal, nodal and pulmonary metastases. 18 F-FES uptake was observed in the primary tumour as well as in all metastatic sites. Conclusion: 18 F-FES can successfully be labeled and 18 -FES PET-CT can be performed in estrogen receptor positive breast cancer patients to take appropriate treatment decisions.
Saved You A Click: Automatically Answering Clickbait Titles

Often clickbait articles have a title that is phrased as a question or vague teaser that entices the user to click on the link and read the article to find the explanation. We developed a system that will automatically find the answer or explanation of the clickbait hook from the website text so that the user does not need to read through the text themselves. We fine-tune an extractive question and answering model (RoBERTa) and an abstractive one (T5), using data scraped from the 'StopClickbait' Facebook pages and Reddit's 'SavedYouAClick' subforum. We find that both extractive and abstractive models improve significantly after finetuning. We find that the extractive model performs slightly better according to ROUGE scores, while the abstractive one has a slight edge in terms of BERTscores.
The voice-specific process revealed by neuromagnetic responses

Neuroimaging studies may provide evidence for perceptual specificity elicited by human voice. We tried to iden-tify the voice specific activities with high spatial and temporal resolution using whole-head magnetoencephalo-graphy (MEG). Volunteers were instructed to listen to sung and corresponding instrumental sounds matched in fundamental-frequency. The stimuli were 16 acoustically different sounds, comprising eight types, which consist-ing of sounds produced by four singers and four musical instruments at each of two fundamental frequencies: 220 Hz (musical note A3) and 261.9 Hz (C3). In both types of stimuli, two components of evoked responses were recorded at approximately 100 and 400 ms after the stimulus onset, respectively. The source locations of equivalent current dipoles (ECDs) for both components were estimated around the superior temporal sulcus in both hemi-spheres. Compared with the instrument, the RMS and source strength for the voice were significantly larger at approximately 100 ms (p<0.05 ). The operation of a gating system directing human voice stimuli might be proc-essed differently as compared with other auditory stimuli.
Soft Contextual Data Augmentation for Robust Question Answering

Question answering using state-of-the-art Transformer models shows brittleness to domain transfer. One contributing factor is changes in phrasing patterns between different domains. We attempt to train models that are robust to changes in phrasing patterns using soft contextual data augmentation (SCA), a data augmentation strategy in which token embeddings are replaced by “soft token” embeddings, which are weighted averages of token embeddings based on a distribution over the vocabulary produced by a language model conditioned on the rest of the sequence. We use pre-trained DistilBERT in the Masked Language Model setting to generate distributions for soft tokens and then use pre-trained DistilBERT in the Question Answering setting for fine-tuning on the QA task. Our results demonstrate that SCA improves performance specifically on the out-of-domain QA task and analysis suggests SCA is particularly helpful for improving performance on question-context pairs that involve paraphrasing.
MEASURING GAME IMMERSION AND FLOW WITH ELECTROENCEPHALOGRAPHY

Immersion and Flow are important parameters when it comes to an enjoyable experience of video games. To measure these mental states usually self-assessment questionnaires, answered by the subject after the experience, are used. Because of the shortcomings of this method, the ultimate goal is to establish an alternative measuring method through correlations of physiological sensor data. While there are many physiological data sources to be looked at, such as recording the heartrate or skin conductance, measuring brain activity with electroencephalography (EEG) is one of the most promising candidates towards a way of objectively measuring Immersion and Flow. In this paper, an experiment to record EEG data during and questionnaire data after game play is presented. As a proof of concept, the experiment is tested with an initial test study on three subjects and the recorded data is used for analysis and a first correlation attempt. Although the results are not yet generalizable, they indicate that increased overall beta and frontal theta activity may be related to Flow and Immersion. This remains to be proven by conducting further studies in the future to achieve statistical significance.
Using Character Embedding and QANet to Improve Perfomance on Question-Answering Task (SQuAD)

Given a baseline model BiDAF without character embeddings, we aim to build an NLP model that performs better than baseline. The two main approaches we take are to (1) create a character level embedding and (2) build a QANet Model. We perform an ablation study to understand how these model architecture features influence performance. Results show that the model with QANet and character embedding performs the best, with a score (evaluated on the test set) EM = 60.592, F1 = 64.353. Further analysis compares context to query attention between the baseline and the top performing models
The State of Man-in-the-Middle TLS Proxies: Prevalence and User Attitudes

The State of Man-in-the-Middle TLS Proxies: Prevalence and User Attitudes Mark Thomas O’Neill Department of Computer Science, BYU Master of Science We measure the prevalence and uses of Man-in-the-Middle TLS proxies using a Flash tool deployed with a Google AdWords campaign. We generate 15.2 million certificate tests across two large-scale measurement studies and find that 1 in 250 TLS connections are intercepted by proxies. The majority of these proxies appear to be benevolent, however we identify over 3,600 cases where eight malware products are using this technology nefariously. We also find thousands of instances of negligent, duplicitous, and suspicious behavior, some of which degrade security for users without their knowledge. Distinguishing these types of practices is challenging in practice, indicating a need for transparency and user awareness. We also report the results of a survey of 1,976 individuals regarding their opinions of TLS proxies. Responses indicate that participants hold nuanced opinions on security and privacy trade-offs, with most recognizing legitimate uses for the practice, but also concerned about threats from hackers or government surveillance. There is strong support for notification and consent when a system is intercepting their encrypted traffic, although this support varies depending on the situation. A significant concern about malicious uses of TLS inspection is identity theft, and many would react negatively and some would change their behavior if they discovered inspection occurring without their knowledge. We also find that a small but significant number of participants are jaded by the current state of affairs and have lost any expectation of privacy.
Adrenal lnsuff Carcinoma iciency due to Metastatic Hepatocellular

A 73-year-old man with general malaise and nausea following a common cold diagnosed by a local physician was found to have multiple hepatocellular carcinomas with enlarged bilateral adrenal glands, combined with adrenal insufficiency. Hydrocortisone replacement improved the symptoms and laboratory findings. Autopsy findings revealed that each adrenal gland was completely replaced by the tumor measuring 11 cm in diameter, and no adrenal tissue was recognized. Histologically, the adrenal tumors, as well as the liver tumors, were moderately differentiated Edmondson type II hepatocellular carcinomas. This is a second report of adrenal insufficiency due to hepatocellular carcinoma as a primary site of metastatic adrenal tumor.