Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenotypic Pattern of Early Versus Later-Onset Pediatric Inflammatory Bowel Disease in a Eurasian Country

Objectives: It is not clear whether the characteristics of pediatric inflammatory bowel disease (IBD) differ between Eastern and Western countries. The aim of this study was to analyze the characteristics of PIBD in Turkey, according to the age at diagnosis. Methods: The data of 176 children with IBD who were followed in our center were analyzed. Patients were divided into early (EO-IBD, onset at 2 to <10 years) and later-onset (LO-IBD, 10 to ≤17 years) IBD according to the age at diagnosis. Patients’ data with ulcerative colitis (UC) and Crohn’s disease (CD) were compared. Results: Of 176 patients, 47 (26.7%) were diagnosed with EO-IBD. Patients with early-onset ulcerative colitis (EO-UC) had the highest rate of family history of IBD (17.6%). Pancolitis was the most common form of UC regardless of the age at onset. The rate of moderate-severe disease activity in later-onset UC (62.5%) was higher than in EO-UC (37.5%). A higher rate of extraintestinal manifestations was observed in EO-IBD patients, particularly in EO-UC (38.2%) than in LO-IBD patients. Patients with early-onset CD (EO-CD) had predominantly colonic involvement and nonstricturing, nonpenetrating disease behavior. The rate of perianal disease in patients with later-onset CD (LO-CD) (64.5%) was noticeably higher than those with EO-CD (23%). Conclusions: Our results suggest that patients with EO-UC represented a distinct phenotype with a mild disease activity, high rate of extraintestinal symptoms, and a high proportion of family history. The analysis of our IBD cohort also demonstrated remarkably high rate of perianal disease, particularly in patients with LO-CD.
Platyfish bypass the constraint of the caudal fin ventral identity in teleosts

Background: The caudal fin of teleosts is characterized by dorsoventral symmetry. Despite this external morphology, the principal rays of this appendage connect to bones below the notochord, indicating the ventral (hypochordal) identity of this organ. Results: Here, we report that this typical architecture of the caudal fin is not fully conserved in the platyfish (Xiphophorus maculatus) and the guppy (Poecilia reticulata), representatives of the Poeciliidae family. We show that in these species, 3–4 principal rays connect to bones above the notochord, suggesting an epichordal contribution. Consistently, as examined in platyfish, dorsal identity genes zic1/4 were highly expressed in these rays, providing molecular evidence of their epichordal origin. Developmental analysis revealed that the earliest rays above the notochord emerge at the 10‐ray stage of fin morphogenesis. In contrast to zebrafish and medaka, platyfish and guppies display a mirrored shape of dorsal and ventral processes of the caudal endoskeleton. Our study suggests that an ancestral bauplan expanded in poeciliids by advancing its symmetrical pattern. Conclusion: The platyfish evolved a fin architecture with the epichordal origin of its upper principal rays and a high level of symmetry in the caudal endoskeleton. This innovative architecture highlights the adaptation of the teleost skeleton.
Application of SAND Technology in DASH-Enabled Content Delivery Networks and Server Environments

MPEG Dynamic Adaptive Streaming over HTTP (DASH) provides formats that are suitable to stream segmented media content over HTTP. DASH clients follow a client-pull paradigm by adapting their requests based on the available bandwidth and other local resources. This has proven to be easier to deploy over content delivery network infrastructure than server-push technologies. However, this decentralized nature introduces new challenges such as offering a consistent and higher quality of service for premium users. MPEG addresses this issue in the recently published MPEG-DASH part 5, Server and Network-Assisted DASH (SAND). The key features of SAND are asynchronous network-to-client and network-to-network communication of quality-related assisting information. This way, the quality of services can be improved by the DASH clients reacting realtime to the assistance and recommendation sent by the service provider. In addition, DASH-Industry Forum (DASH-IF) is further defining interoperable guidelines to optimize SAND deployments in a variety of environments: home network, over-the-top, and so on. DASH-IF is expected to publish this new interoperable guideline by the end of 2017.
Modulation, control, and performance analysis of asymmetric modular multilevel converter (A-MMC)

Modular multilevel converters (MMC) have proven to be a viable candidate for high-voltage direct current transmission systems. Here, asymmetric modular multilevel converters (A-MMC) are introduced wherein each series connected module has two half-bridge (HB) submodules, with asymmetric voltage rating. Compared to conventional symmetric MMCs, such systems offer the following benefits: (i) generation of four distinct voltage levels using one module; (ii) 33% lesser semiconductor and gate drive requirement; (iii) higher system efficiency; (iv) reduction in overall cost and size. A hybrid pulse-width modulation technique generates optimised switching pulses of the A-MMC. A novel voltage balancing algorithm is proposed to stabilise each asymmetric HB submodule at the rated voltage. Circulating current control is implemented to suppress the dominant second harmonic in the arm currents of the A-MMC. Detailed simulations in MATLAB/Simulink are performed to validate the operation and control. A quantitative and qualitative comparison with conventional MMCs is also presented in terms of the converter losses, output voltage/current total harmonic distortion, capacitor ripple voltages, circulating currents etc.
The automation of the procedure of the electrohydraulic complex power harmonic analysis

A possibility of application of the method for the harmonic analysis of power signals in the non-stationary modes of electrohydraulic complexes operation is demonstrated. To automate the procedure of power harmonic analysis an energy analyzer is created. It enables the research of the electrohydraulic complex power processes when they change in real time with preservation of all information about the initial power-forming signals. It is obtained that the wave processes in the pipeline in non-stationary modes are accompanied by increase of the power signals variable components, especially, their low-frequency component. It is proposed to use the indices based on determination of the power root-mean-square values to assess the energy conversion processes in the electrohydraulic complex. Streszczenie. W pracy zademonstrowano możliwość aplikacji metody analizy harmonicznych sygnałów mocy w niestacjonarnych modach układów elektrohydraulicznych. Do zautomatyzowania procesu stworzono procedurę harmonicznej analizy mocy w analizatorach energii. Umożliwia to badanie procesów w układach elektrohydraulicznych w czasie realnym, podczas ich zmiany z zachowaniem wszystkich informacji o początkowych sygnałach. Otrzymano, że niestacjonarne procesy falowe w rurociągu stowarzyszone są ze wzrostem składników sygnałów mocy, specjalnie tych o niskiej częstotliwości. Zaproponowano wykorzystanie indeksów bazujących na określeniu wartości skutecznej w celu oszacowania procesów przemiany energetycznej w układzie elektrohydraulicznym. (Automatyzacja procedur analiz harmonicznych układu elektrohydraulicznego)
The Effect of Perceptions of Usefulness, Perceptions of Ease of Use, And Perceptions of Security in Using E-Filling on Individual Taxpayer Compliance

The purpose of this study is to analyze the effect of three variables, which consist of perceived usefulness, perceived convenience, and perceived security in the use of e-Filing on individual taxpayer compliance which will be implemented in 2021. The method and object of this paper uses a quantitative approach with a sample of 139 respondent's who already have a NPWP, have used the e-Filing application, and are domiciled in West Jakarta. The result of the research shows that perceived usefulness has a significant effect on individual taxpayer compliance. Meanwhile, perception of convenience and perception of security have no significant effect on individual taxpayer compliance. The conclusion of this study as a whole of the three variables have a significant influence on individual taxpayer compliance.
Forecasting Cryptocurrency Volatility Using GARCH and ARCH Model

This research aims to analyze the calculation of volatility stage from five cryptocurrency products, which are Bitcoin, Ethereum, Binance Coin, Dashcoin, and Litecoin from 1st January 2018 to 1st April 2021 where it consists of calculation of each of the cryptocurrency products' volatility. The research method is a quantitative method by gaining data from Investing.com. Then, analyzing the data using Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. This research aims to know whether ARCH and GARCH models apply to daily life situations in the field. The result shows that the data from ARCH and GARCH models are not suitable on daily basis. Further research should calculate cryptocurrency products to use differentiated GARCH models, such as GJR-GARCH or GARCH-MIDAS. It is also better to calculate the volatility of cryptocurrency products annually. According to some thesis, the volatility cryptocurrency products are more suitable to calculate annually than daily.
Factors Affecting the Implementation of Remote Audit in the Audit Process in the COVID-19 Pandemic

This study aims to analyze the factors that affect the implementation of a remote audit with the UTAUT model. This study uses a survey method for data collection and uses SmartPLS 3 for data processing. The population of this study is auditors who work at a Public Accounting Firm in DKI Jakarta and use remote auditing while working. A sample of 90 respondents was obtained by purposive sampling. The results showed that the convenience of online access, performance expectations, and social influences significantly affected behavioral intentions. However, effort expectations had no significant impact on behavioral intentions. This study also shows that behavioral intentions significantly affect usage behavior, while the facilitation condition has no significant effect on the behavior of using remote audits.
Analysis on the Impact of Recommender Systems on Consumer Decision: Making on China's Online Shopping Platforms

With the popularization of online shopping platforms across China and in order to maximize profits, online shopping platforms developed various recommender systems (RS), which played an important role in consumers’ decision-making process. Therefore, the impact of RS on consumers’ decision-making process is worthy of studying. In this paper, the RS of online shopping platforms and two examples of them were introduced. This paper firstly analyzed the ethical challenges related to the RS, including algorithmic opacity, privacy concerns, and bias and behavior manipulation and provided solutions accordingly. Besides, the paper through a method of online questionnaire and data analysis, illustrated RS’ ability to influence consumers’ decision making by providing them with personalized recommendations during online shopping. In addition, with the help of an online questionnaire filled out by more than 128 Chinese consumers across different ages and occupations, the author also analyzed people's attitudes towards the RS. According to the research results, this overall finding implies that recommender systems in contemporary electronic commerce are not merely decision aids for lowering search costs, but may also play a substantial part in economic decision making. A randomised trial strategy was used in this work to support the idea that internet recommendations may influence willingness-to-pay judgements, even when the two are tested on distinct scales.
Adaptive Empathy: A Model for Learning Empathic Responses in Response to Feedback

Empathy is usually deployed in social interactions. Nevertheless, common measures and examinations of empathy study this construct in isolation from the person in distress. In this article we seek to extend the field of examination to include both empathizer and target to determine whether and how empathic responses are affected by feedback and learned through interaction. Building on computational approaches in feedback-based adaptations (e.g., no feedback, model-free and model-based learning), we propose a framework for understanding how empathic responses are learned on the basis of feedback. In this framework, adaptive empathy, defined as the ability to adapt one’s empathic responses, is a central aspect of empathic skills and can provide a new dimension to the evaluation and investigation of empathy. By extending existing neural models of empathy, we suggest that adaptive empathy may be mediated by interactions between the neural circuits associated with valuation, shared distress, observation-execution, and mentalizing. Finally, we propose that adaptive empathy should be considered a prominent facet of empathic capabilities with the potential to explain empathic behavior in health and psychopathology.
The effect of stress levels of nurses on performance during the COVID-19 pandemic: the mediating role of motivation

Background During the COVID-19 pandemic, it is thought that nurses working at more intensive levels and in high-risk areas may increase their stress and decrease their motivations and performance. Aims This study aimed to determine the effects of stress and work stress on nurses’ motivations and performances. Methods An online, cross-sectional survey, which used self-assessment scales to measure perceived stress, perceived work stress, motivation, and work performance was used as a data collection tool. The target population was all working nurses in a hospital. Results One hundred and fourteen nurses completed the questionnaires. The results of the study show that the participants' perceived motivation level (3.131 ± 0.685), perceived stress level (2.885 ± 0.547), and perceived job stress level (3.202 ± 1.067) were at a medium level, while their perceived performance level (3.845 ± 0.783) was at a high level. According to correlation analyses results, increases in perceived stress levels of nurses decrease their motivations (r = −0.502) and performances (r = −0.603). Similarly, increases in perceived work stress levels of nurses decrease their motivations (r = −0.441) and performances (r = −0.534). According to the Structural Equation Modelling (SEM) analysis, motivation has a mediating role in the negative effect of perceived stresses on performance (β = −0.694) and augments such negative effect (β = −0.169). Similarly, motivation has a mediating role in the negative effect of perceived work stresses on performance (β = −0.295) and increases the negative effect of perceived work stress on performance (β = −0.097). Conclusions Nurses’ perceived stress and perceived work stress levels were not high, but an increase in these variables may decrease nurses’ motivation and performance. Thus, controlling nurses’ stress levels and identifying possible stress sources related to the COVID-19 pandemic are important to support nurses in their work.
CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification

This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans. The basic pre-trained models we choose are XLM-RoBERTa and mT5. The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board.
TeamOtter at SemEval-2022 Task 5: Detecting Misogynistic Content in Multimodal Memes

We describe our system for the SemEval 2022 task on detecting misogynous content in memes. This is a pressing problem and we explore various methods ranging from traditional machine learning to deep learning models such as multimodal transformers. We propose a multimodal BERT architecture that uses information from both image and text. We further incorporate common world knowledge from pretrained CLIP and Urban dictionary. We also provide qualitative analysis to support out model. Our best performing model achieves an F1 score of 0.679 on Task A (Rank 5) and 0.680 on Task B (Rank 13) of the hidden test set. Our code is available at https://github.com/paridhimaheshwari2708/MAMI.
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction

Multi-triple extraction is a challenging task due to the existence of informative inter-triple correlations, and consequently rich interactions across the constituent entities and relations.While existing works only explore entity representations, we propose to explicitly introduce relation representation, jointly represent it with entities, and novelly align them to identify valid triples.We perform comprehensive experiments on document-level relation extraction and joint entity and relation extraction along with ablations to demonstrate the advantage of the proposed method.
Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss

In document-level event argument extraction, an argument is likely to appear multiple times in different expressions in the document. The redundancy of arguments underlying multiple sentences is beneficial but is often overlooked. In addition, in event argument extraction, most entities are regarded as class “others”, i.e. Universum class, which is defined as a collection of samples that do not belong to any class of interest. Universum class is composed of heterogeneous entities without typical common features. Classifiers trained by cross entropy loss could easily misclassify the Universum class because of their open decision boundary. In this paper, to make use of redundant event information underlying a document, we build an entity coreference graph with the graph2token module to produce a comprehensive and coreference-aware representation for every entity and then build an entity summary graph to merge the multiple extraction results. To better classify Universum class, we propose a new loss function to build classifiers with closed boundaries. Experimental results show that our model outperforms the previous state-of-the-art models by 3.35% in F1-score.
Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

Social platforms such as Gab and Parler, branded as ‘free-speech’ networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit.In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make 16.1% of Parler active users, and that they have distinct characteristics comparing to other user groups. We further complement our analysis by comparing the trends observed in Parler to those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level.
A Taxonomy of Bias-Causing Ambiguities in Machine Translation

This paper introduces a taxonomy of phenomena which cause bias in machine translation, covering gender bias (people being male and/or female), number bias (singular you versus plural you) and formality bias (informal you versus formal you). Our taxonomy is a formalism for describing situations in machine translation when the source text leaves some of these properties unspecified (eg. does not say whether doctor is male or female) but the target language requires the property to be specified (eg. because it does not have a gender-neutral word for doctor). The formalism described here is used internally by a web-based tool we have built for detecting and correcting bias in the output of any machine translator.
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection

We present our systems and findings for the iSarcasmEval: Intended Sarcasm Detection In English and Arabic at SEMEVAL 2022. Specifically we take part in Subtask A for the English language. The task aims to determine whether a text from social media (a tweet) is sarcastic or not. We model the problem using knowledge sources, a pre-trained language model on sentiment/emotion data and a dataset focused on intended sarcasm. Our submission ranked third place among 43 teams. In addition, we show a brief error analysis of our best model to investigate challenging examples for detecting sarcasm.
Identifying stable speech-language markers of autism in children: Preliminary evidence from a longitudinal telephony-based study

This study examined differences in linguistic features produced by autistic and neurotypical (NT) children during brief picture descriptions, and assessed feature stability over time. Weekly speech samples from well-characterized participants were collected using a telephony system designed to improve access for geographically isolated and historically marginalized communities. Results showed stable group differences in certain acoustic features, some of which may potentially serve as key outcome measures in future treatment studies. These results highlight the importance of eliciting semi-structured speech samples in a variety of contexts over time, and adds to a growing body of research showing that fine-grained naturalistic communication features hold promise for intervention research.
NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning

The intended sarcasm cannot be understood until the listener observes that the text’s literal meaning violates truthfulness. Consequently, words and meanings play an essential role in specifying sarcasm. Enriched feature extraction techniques were proposed to capture both words and meanings in the contexts. Due to the overlapping features in sarcastic and non-sarcastic texts, a CNN model extracts local features from the combined class-dependent statistical embedding of sarcastic texts with contextualized embedding. Another component BiLSTM extracts long dependencies from combined non-sarcastic statistical and contextualized embeddings. This work combines a classifier that uses the combined high-level features of CNN and BiLSTM for sarcasm detection to produce the final predictions. The experimental analysis presented in this paper shows the effectiveness of the proposed method.