Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived Teacher Responses to Bullying Influence Students’ Social Cognitions

Teachers’ responses to bullying incidents are key in bullying intervention at school. Scholars have suggested that teacher responses can predict student cognitions that are associated with their bullying behaviors. However, little is known about whether and how teacher responses affect these cognitions. Therefore, the current study investigated the effects of four immediate teacher responses on four bullying-related student cognitions, using an experimental vignette design. Additionally, it was examined whether students’ own participant role behaviors in actual bullying moderated these effects. The investigated teacher responses were non-response, comforting the victim, correcting the bully, and a combination of comforting the victim and correcting the bully. The investigated student cognitions were perceived teacher attitudes toward bullying, perceived teacher moral disengagement regarding bullying, student willingness to report bullying to the teacher and student expectations regarding bullying participant role behaviors in the classroom. Fourth-to-sixth grade students (N = 910; 47% boys; Mage = 11.04 years, SD = 0.91) read a vignette describing a hypothetical teacher’s response to a same bullying incident, following random assignment to one of eight conditions (i.e., four teacher responses × two genders of bully and victim in the vignette). Afterward, students completed questionnaires about their social cognitions and manipulation checks. ANOVA demonstrated that students perceived stronger teacher anti-bullying attitudes and less teacher moral disengagement when the hypothetical teacher displayed an active response. These effects were even stronger when the teacher corrected the bully compared to when only the victim was comforted. Further, students were more willing to report bullying when the teacher corrected the bully than when the teacher only comforted the victim. Finally, students expected less pro-bullying behaviors, more defending and less victimization in the vignette’s classroom following active teacher response compared to non-response. The effects of teacher responses on student cognitions were not moderated by students’ own participant roles in bullying. Taken together, these findings emphasize the importance of active teacher responses to bullying, and especially, responses that clearly show that bullying is not tolerated. Teachers are encouraged to be aware that students can deduce beliefs from teacher responses which can, in turn, affect bullying processes in the classroom.
Covid-19: US faces “surge upon a surge”

The US is likely to experience “a surge upon a surge” of covid-19 cases as a result of millions of people traveling over the Thanksgiving holiday weekend (26-29 November) to celebrate with family and friends, the leading US expert on the disease has predicted.1 Although the Centers for Disease Control and Prevention (CDC) asked people to stay home for the holiday, the Transportation Security Administration screened more than eight million Americans flying between 20 and 28 November. Anthony Fauci, director of the National Institute for Allergy and Infectious Diseases, urged travelers to quarantine and be tested after returning home. If the virus was spread during family gatherings cases would begin to rise in a week or two, he said. Fauci also expressed concern that cases would continue to increase as people traveled and got together during the December holidays. President …
Role of Arabidopsis Splicing factor SF1 in Temperature-Responsive Alternative Splicing of FLM pre-mRNA

Small changes in temperature affect plant ecological and physiological factors that impact agricultural production. Hence, understanding how temperature affects flowering is crucial for decreasing the effects of climate change on crop yields. Recent reports have shown that FLM-β, the major spliced isoform of FLOWERING LOCUS M (FLM)—a flowering time gene, contributes to temperature-responsive flowering in Arabidopsis thaliana. However, the molecular mechanism linking pre-mRNA processing and temperature-responsive flowering is not well understood. Genetic and molecular analyses identified the role of an Arabidopsis splicing factor SF1 homolog, AtSF1, in regulating temperature-responsive flowering. The loss-of-function AtSF1 mutant shows temperature insensitivity at different temperatures and very low levels of FLM-β transcript, but a significantly increased transcript level of the alternative splicing (AS) isoform, FLM-δ. An RNA immunoprecipitation (RIP) assay revealed that AtSF1 is responsible for ambient temperature-dependent AS of FLM pre-mRNA, resulting in the temperature-dependent production of functional FLM-β transcripts. Moreover, alterations in other splicing factors such as ABA HYPERSENSITIVE1/CBP80 (ABH1/CBP80) and STABILIZED1 (STA1) did not impact the FLM-β/FLM-δ ratio at different temperatures. Taken together, our data suggest that a temperature-dependent interaction between AtSF1 and FLM pre-mRNA controls flowering time in response to temperature fluctuations.
Combined Effects of Allelopathic Polyphenols on Microcystis aeruginosa and Response of Different Chlorophyll Fluorescence Parameters

Polyphenols are allelochemicals secreted by aquatic plants that effectively control cyanobacteria blooms. In this study, sensitive response parameters (including CFPs) of Microcystis aeruginosa were explored under the stress of different polyphenols individually and their combination. The combined effects on M. aeruginosa were investigated based on the most sensitive parameter and cell densities. For pyrogallic acid (PA) and gallic acid (GA), the sensitivity order of parameters based on the EC50 values (from 0.73 to 3.40 mg L–1 for PA and from 1.05 to 2.68 mg L–1 for GA) and the results of the hierarchical cluster analysis showed that non-photochemical quenching parameters [NPQ, qN, qN(rel) and qCN] > photochemical quenching parameters [YII, qP, qP(rel) and qL] or others [Fv/Fm, F’v/F’m, qTQ and UQF(rel)] > cell densities. CFPs were not sensitive to ellagic acid (EA) and (+)-catechin (CA). The sensitivity order of parameters for M. aeruginosa with PA-GA mixture was similar to that under PA and GA stress. The quantitative (Toxicity Index, TI) and qualitative (Isobologram representation) methods were employed to evaluate the combined effects of PA, GA, and CA on M. aeruginosa based on cell densities and NPQ. TI values based on the EC50 cells suggested the additive effects of binary and multiple polyphenols, but synergistic and additive effects according to the EC50 NPQ (varied from 0.16 to 1.94). In terms of NPQ of M. aeruginosa, the binary polyphenols exhibited synergistic effects when the proportion of high toxic polyphenols PA or GA was lower than 40%, and the three polyphenols showed a synergistic effect only at the ratio of 1:1:1. Similar results were also found by isobologram representation. The results showed that increasing the ratio of high toxic polyphenols would not enhance the allelopathic effects, and the property, proportion and concentrations of polyphenols played an important role in the combined effects. Compared with cell densities, NPQ was a more suitable parameter as evaluating indicators in the combined effects of polyphenols on M. aeruginosa. These results could provide a method to screen the allelochemicals of polyphenols inhibiting cyanobacteria and improve the inhibitory effects by different polyphenols combined modes.
Antioxidant and Antiproliferative Activity of Allium ursinum and Their Associated Microbiota During Simulated in vitro Digestion in the Presence of Food Matrix

In this study, for the first time, the comprehensive analysis of antiproliferative and antioxidant activities of ramson, followed by the analysis of its associated microbiota and health-promoting effects of lactic acid bacteria (LAB), was performed. Ramson (Allium ursinum) is recognized as a medicinal plant with a long history of use in traditional medicine due to its antimicrobial and antioxidant activity. In this study the influence of in vitro gastrointestinal digestion on the cytotoxic activity of A. ursinum extracts against human malignant cell lines was demonstrated. Seven sulfur compounds, the degradation products of thiosulfinates, including diallyl disulfide were shown to inhibit proliferation of malignant cells by inducing accumulation within G2/M phase as well as to induce apoptosis through activation of caspase-3 and mitochondrial signaling pathway. Further, the A. ursinum microbiota, particularly LAB with potential probiotic effects, was analyzed by culture-dependent method and culture-independent method [denaturing gradient gel electrophoresis (DGGE)]. The obtained results revealed that the most abundant genera were Streptococcus, Lactobacillus, and Bacillus. The Lactobacillus genus was mainly represented by L. fermentum. The pulsed-field gel electrophoresis (PFGE) analysis revealed the presence of two PFGE pulsotypes. The probiotic potential of the strain L. fermentum BGSR163 belonging to PFGE pulsotype 1 and the strain L. fermentum BGSR227 belonging to the PFGE pulsotype 2 was characterized. The results revealed that both strains are safe for human use, successfully survive the simulated gastrointestinal conditions, have potential to transiently colonize the gastrointestinal tract (GIT) and have a protective immunomodulatory effect, inducing the production of proinflammatory cytokine IL17 and regulatory cytokine IL10, while decreasing the production of proinflammatory cytokine IFN-γ. In conclusion, the results of this study suggest that consumption of A. ursinum might have health-promoting properties, including anticancer effects, while L. fermentum strains isolated from A. ursinum leaves could be used as probiotics for human consumption.
Dynamic Topic Tracker for KB-to-Text Generation

Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences. However, most of the existing models suffer from the off-topic problem, namely, the models are prone to generate some unrelated clauses that are somehow involved with certain input terms regardless of the given input data. This problem seriously degrades the quality of the generation results. In this paper, we propose a novel dynamic topic tracker for solving this problem. Different from existing models, our proposed model learns a global hidden representation for topics and recognizes the corresponding topic during each generation step. The recognized topic is used as additional information to guide the generation process and thus alleviates the off-topic problem. The experimental results show that our proposed model can enhance the performance of sentence generation and the off-topic problem is significantly mitigated.
An analysis of language models for metaphor recognition

We conduct a linguistic analysis of recent metaphor recognition systems, all of which are based on language models. We show that their performance, although reaching high F-scores, has considerable gaps from a linguistic perspective. First, they perform substantially worse on unconventional metaphors than on conventional ones. Second, they struggle with handling rarer word types. These two findings together suggest that a large part of the systems’ success is due to optimising the disambiguation of conventionalised, metaphoric word senses for specific words instead of modelling general properties of metaphors. As a positive result, the systems show increasing capabilities to recognise metaphoric readings of unseen words if synonyms or morphological variations of these words have been seen before, leading to enhanced generalisation beyond word sense disambiguation.
A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection

Ensuring smooth communication is essential in a chat-oriented dialogue system, so that a user can obtain meaningful responses through interactions with the system. Most prior work on dialogue research does not focus on preventing dialogue breakdown. One of the major challenges is that a dialogue system may generate an undesired utterance leading to a dialogue breakdown, which degrades the overall interaction quality. Hence, it is crucial for a machine to detect dialogue breakdowns in an ongoing conversation. In this paper, we propose a novel dialogue breakdown detection model that jointly incorporates a pretrained cross-lingual language model and a co-attention network. Our proposed model leverages effective word embeddings trained on one hundred different languages to generate contextualized representations. Co-attention aims to capture the interaction between the latest utterance and the conversation history, and thereby determines whether the latest utterance causes a dialogue breakdown. Experimental results show that our proposed model outperforms all previous approaches on all evaluation metrics in both the Japanese and English tracks in Dialogue Breakdown Detection Challenge 4 (DBDC4 at IWSDS2019).
Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction

Dependency trees have been shown to be effective in capturing long-range relations between target entities. Nevertheless, how to selectively emphasize target-relevant information and remove irrelevant content from the tree is still an open problem. Existing approaches employing pre-defined rules to eliminate noise may not always yield optimal results due to the complexity and variability of natural language. In this paper, we present a novel architecture named Dynamically Pruned Graph Convolutional Network (DP-GCN), which learns to prune the dependency tree with rethinking in an end-to-end scheme. In each layer of DP-GCN, we employ a selection module to concentrate on nodes expressing the target relation by a set of binary gates, and then augment the pruned tree with a pruned semantic graph to ensure the connectivity. After that, we introduce a rethinking mechanism to guide and refine the pruning operation by feeding back the high-level learned features repeatedly. Extensive experimental results demonstrate that our model achieves impressive results compared to strong competitors.
Resolving Pronouns in Twitter Streams: Context can Help!

Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event. Naturally, many tweets make pronominal reference to these people/characters. We propose an algorithm for resolving personal pronouns that make reference to people involved in an event, in tweet streams collected during the event.
Multilingual Epidemiological Text Classification: A Comparative Study

In this paper, we approach the multilingual text classification task in the context of the epidemiological field. Multilingual text classification models tend to perform differently across different languages (low- or high-resourced), more particularly when the dataset is highly imbalanced, which is the case for epidemiological datasets. We conduct a comparative study of different machine and deep learning text classification models using a dataset comprising news articles related to epidemic outbreaks from six languages, four low-resourced and two high-resourced, in order to analyze the influence of the nature of the language, the structure of the document, and the size of the data. Our findings indicate that the performance of the models based on fine-tuned language models exceeds by more than 50% the chosen baseline models that include a specialized epidemiological news surveillance system and several machine learning models. Also, low-resource languages are highly influenced not only by the typology of the languages on which the models have been pre-trained or/and fine-tuned but also by their size. Furthermore, we discover that the beginning and the end of documents provide the most salient features for this task and, as expected, the performance of the models was proportionate to the training data size.
QANom: Question-Answer driven SRL for Nominalizations

We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom. This scheme extends the QA-SRL formalism (He et al., 2015), modeling the relations between nominalizations and their arguments via natural language question-answer pairs. We construct the first QANom dataset using controlled crowdsourcing, analyze its quality and compare it to expertly annotated nominal-SRL annotations, as well as to other QA-driven annotations. In addition, we train a baseline QANom parser for identifying nominalizations and labeling their arguments with question-answer pairs. Finally, we demonstrate the extrinsic utility of our annotations for downstream tasks using both indirect supervision and zero-shot settings.
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations

Emotion and sentiment classification in dialogues is a challenging task that has gained popularity in recent times. Humans tend to have multiple emotions with varying intensities while expressing their thoughts and feelings. Emotions in an utterance of dialogue can either be independent or dependent on the previous utterances, thus making the task complex and interesting. Multi-label emotion detection in conversations is a significant task that provides the ability to the system to understand the various emotions of the users interacting. Sentiment analysis in dialogue/conversation, on the other hand, helps in understanding the perspective of the user with respect to the ongoing conversation. Along with text, additional information in the form of audio and video assist in identifying the correct emotions with the appropriate intensity and sentiments in an utterance of a dialogue. Lately, quite a few datasets have been made available for dialogue emotion and sentiment classification, but these datasets are imbalanced in representing different emotions and consist of an only single emotion. Hence, we present at first a large-scale balanced Multimodal Multi-label Emotion, Intensity, and Sentiment Dialogue dataset (MEISD), collected from different TV series that has textual, audio and visual features, and then establish a baseline setup for further research.
Joint Event Extraction with Hierarchical Policy Network

Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy. In view of this, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE. The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection. The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue. Extensive experiments on ACE2005 and TAC2015 demonstrate the superiority of HPNet, leading to state-of-the-art performance and is more powerful for sentences with multiple events.
The Prognostic Value of Androgen Receptor Splice Variant 7 in Castration-Resistant Prostate Cancer Treated With Novel Hormonal Therapy or Chemotherapy: A Systematic Review and Meta-analysis

Purpose This study aimed to evaluate the prognostic role of AR-V7 in terms of prostate-specific antigen (PSA) response, progression-free survival (PFS), and overall survival (OS) in CRPC patients treated with novel hormonal therapy (NHT) (Abiraterone and Enzalutamide) or taxane-based chemotherapy (Docetaxel and Cabazitaxel). Methods A comprehensive literature search was conducted on PubMed, Embase, and the Web of Science from inception to February 2020. Studies focusing on the prognostic values of AR-V7 in CRPC patients treated with NHT or chemotherapy were included in our meta-analysis. The OS and PFS were analyzed based on Hazard ratios (HRs) and 95% confidence intervals (CIs). Furthermore, Odds ratios (ORs) and 95% CIs were summarized for the AR-V7 conversion after treatment and the PSA response. Results The AR-V7 positive proportion increased significantly after NHT treatment (OR 2.56, 95% CI 1.51–4.32, P<0.001), however, it declined after chemotherapy (OR 0.51, 95% CI 0.28–0.93, P=0.003). AR-V7-positive patients showed a significantly decreased PSA response rate after NHT (OR 0.13, 95% CI 0.09–0.19, P<0.001) but not statistically significant for chemotherapy (OR 0.63, 95% CI 0.40-1.01, P=0.06). Notably, PFS (HR 3.56, 95% CI 2.53–5.01, P<0.001) and OS (HR 4.47, 95% CI 3.03–6.59, P<0.001) were worse in AR-V7-positive ttreated with NHT. Similarly, AR-V7 positivity correlated with poor prognosis after chemotherapy as evidenced by shorter OS (HR 1.98, 95% CI 1.48-2.66, P<0.001) and a significantly shorter PFS (HR 1.35, 95% CI 0.97-1.87, P=0.07). Conclusion NHT treatment increased AR-V7 positive proportion whereas chemotherapy decreased it. Moreover, AR-V7 positivity correlated with lower PSA response, poorer PFS, and OS in CRPC treated with NHT, and shorter OS in patients receiving chemotherapy.
Exploring the Power of Romanian BERT for Dialect Identification

Dialect identification represents a key aspect for improving a series of tasks, for example, opinion mining, considering that the location of the speaker can greatly influence the attitude towards a subject. In this work, we describe the systems developed by our team for VarDial 2020: Romanian Dialect Identification, a task specifically created for challenging participants to solve the previously mentioned issue. More specifically, we introduce a series of neural systems based on Transformers, that combine a BERT model exclusively pre-trained on the Romanian language with techniques such as adversarial training or character-level embeddings. By using these approaches, we were able to obtain a 0.6475 macro F1 score on the test dataset, thus allowing us to be ranked 5th out of 8 participant teams.
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection

Academia and industry have developed machine learning and natural language processing models to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily depends on labeled datasets for training. This results in the methods’ poor detection performance of the hate speech class as the training datasets are highly imbalanced. In this paper, we propose HateGAN, a deep generative reinforcement learning model, which addresses the challenge of imbalance class by augmenting the dataset with hateful tweets. We conduct extensive experiments to augment two commonly-used hate speech detection datasets with the HateGAN generated tweets. Our experiment results show that HateGAN improves the detection performance of the hate speech class regardless of the classifiers and datasets used in the detection task. Specifically, we observe an average 5% improvement for the hate class F1 scores across all state-of-the-art hate speech classifiers. We also conduct case studies to empirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection.