Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Human-Neurorobotics Approach to Primary Intersubjectivity via Active Inference

Interdisciplinary efforts from developmental psychology, phenomenology, and philosophy of mind, have studied the rudiments of social cognition and conceptualized distinct forms of intersubjective communication and interaction at human early life. Interaction theorists consider primary intersubjectivity a non-mentalist, pre-theoretical, non-conceptual sort of processes that ground a certain level of communication and understanding, and provide support to higher-level cognitive skills. We argue the study of human/neurorobot interaction consists in a unique opportunity to deepen understanding of underlying mechanisms in social cognition through synthetic modeling, while allowing to examine a second person experiential (2PP) access to intersubjectivity in embodied dyadic interaction. Concretely, we propose the study of primary intersubjectivity as a 2PP experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. From our interpretation of the concept of active inference in free-energy principle theory, we propose an open-source methodology named neural robotics library (NRL) for experimental human/neurorobot interaction, wherein a demonstration program named virtual Cartesian robot (VCBot) provides an opportunity to experience the aforementioned embodied interaction to general audiences. Lastly, through a study case, we discuss some ways human-robot primary intersubjectivity can contribute to cognitive science research, such as to the fields of developmental psychology, educational technology, and cognitive rehabilitation.
Functional Analysis of the NucS/EndoMS of the Hyperthermophilic Archaeon Sulfolobus islandicus REY15A

EndoMS is a recently identified mismatch specific endonuclease in Thermococcales of Archaea and Mycobacteria of Bacteria. The homologs of EndoMS are conserved in Archaea and Actinobacteria, where classic MutS-MutL-mediated DNA mismatch repair pathway is absent or non-functional. Here, we report a study on the in vitro mismatch cleavage activity and in vivo function of an EndoMS homolog (SisEndoMS) from Sulfolobus islandicus REY15A, the model archaeon belonging to Crenarchaeota. SisEndoMS is highly active on duplex DNA containing G/T, G/G, and T/T mismatches. Interestingly, the cleavage activity of SisEndoMS is stimulated by the heterotrimeric PCNAs, and when Mn2+ was used as the co-factor instead of Mg2+, SisEndoMS was also active on DNA substrates containing C/T or A/G mismatches, suggesting that the endonuclease activity can be regulated by ion co-factors and accessory proteins. We compared the spontaneous mutation rate of the wild type strain REY15A and ∆endoMS by counter selection against 5-fluoroorotic acid (5-FOA). The endoMS knockout mutant had much higher spontaneous mutation rate (5.06 × 10−3) than that of the wild type (4.6 × 10−6). A mutation accumulation analysis also showed that the deletion mutant had a higher mutation occurrence than the wild type, with transition mutation being the dominant, suggesting that SisEndoMS is responsible for mutation avoidance in this hyperthermophilic archaeon. Overexpression of the wild type SisEndoMS in S. islandicus resulted in retarded growth and abnormal cell morphology, similar to strains overexpressing Hje and Hjc, the Holliday junction endonucleases. Transcriptomic analysis revealed that SisEndoMS overexpression led to upregulation of distinct gene including the CRISPR-Cas IIIB system, methyltransferases, and glycosyltransferases, which are mainly localized to specific regions in the chromosome. Collectively, our results support that EndoMS proteins represent a noncanonical DNA repair pathway in Archaea. The mechanism of the mismatch repair pathway in Sulfolobus which have a single chromosome is discussed.
COVID-19: A Psychosocial Perspective

The World Health Organization declares coronavirus disease 2019 (COVID-19) as a pandemic, and The World Economic Forum argues that the COVID-19-induced global lockdown is the biggest psychological experiment. This study is an attempt to empirically evaluate the possible adverse psychosocial effects caused by COVID-19-related lockdown, if any. To do so, a cross-sectional study is conducted based on a comprehensive online survey using snowball sampling to analyze the level of social and psychological impacts (i.e., stress, belief in stakeholders, fear of losing job, and life satisfaction) during the early stage of the outbreak in Pakistan. The questionnaire is filled out by the residents in Pakistan including working professionals and students (sample size is 428). We find that the development of stress due to COVID-19-induced lockdown is particularly because of mood swings. Additionally, a higher prevalence of stress in the children of highly educated mothers is evident (95% confidence). To assess the belief in stakeholders, we focus gender, demographics, and education. It is observed that parental education and age significantly affect the belief in several stakeholders (i.e., government, media, religious clerics, and family). The lockdown-induced fear of losing job is lower in female and male children whose fathers are graduates. Lastly, we observe that food storage and “no fear of losing job” significantly increases the odds of life satisfaction. These findings have important implications in the context of social insurance, parental education, and policy related to COVID-19 at various levels. This study further facilitates to understand the factors that might affect the mental health and life satisfaction of people during such pandemics.
B-Cell-Specific Myd88 L252P Expression Causes a Premalignant Gammopathy Resembling IgM MGUS

A highly recurrent somatic L265P mutation in the TIR domain of the signaling adapter MYD88 constitutively activates NF-κB. It occurs in nearly all human patients with Waldenström’s macroglobulinemia (WM), a B cell malignancy caused by IgM-expressing cells. Here, we introduced an inducible leucine to proline point mutation into the mouse Myd88 locus, at the orthologous position L252P. When the mutation was introduced early during B cell development, B cells developed normally. However, IgM-expressing plasma cells accumulated with age in spleen and bone, leading to more than 20-fold elevated serum IgM titers. When introduced into germinal center B cells in the context of an immunization, the Myd88L252P mutation caused prolonged persistence of antigen-specific serum IgM and elevated numbers of antigen-specific IgM plasma cells. Myd88L252P-expressing B cells switched normally, but plasma cells expressing other immunoglobulin isotypes did not increase in numbers, implying that IgM expression may be required for the observed cellular expansion. In order to test whether the Myd88L252P mutation can cause clonal expansions, we introduced it into a small fraction of CD19-positive B cells. In this scenario, five out of five mice developed monoclonal IgM serum paraproteins accompanied by an expansion of clonally related plasma cells that expressed mostly hypermutated VDJ regions. Taken together, our data suggest that the Myd88L252P mutation is sufficient to promote aberrant survival and expansion of IgM-expressing plasma cells which in turn can cause IgM monoclonal gammopathy of undetermined significance (MGUS), the premalignant condition that precedes WM.
Mapping Pharmacy Student Learning During Cocurricular Activities to National Standards and Institutional Outcomes

Objective. To assess pharmacy student learning from co-curricular activities and map this to Accreditation Council of Pharmacy Education (ACPE) standards and the institution’s curricular outcomes. Methods. Student representatives of professional organizations at one college of pharmacy were asked to complete a 16-item questionnaire on behalf of their members about each cocurricular activity their organization completed. Descriptive statistics were used to characterize the results. Content analysis was conducted on open-ended questions, and resulting codes were mapped to ACPE Standards 2016 and curricular outcomes. Results. The majority (74%) of the 152 unique cocurricular activities reported were designated as community outreach events and an average of 15 (SD=43) student members participated in each activity. The most frequently selected domain by student representatives was “promoted professionalism” for 86% of activities. Upon distilling student representatives’ open-ended responses regarding their members’ learning, each response was assigned to one or more of 34 codes. The most frequently assigned codes to learning descriptions (36%) were for patient education and counseling. Conclusion. Representatives of student organizations characterized their members’ participation in cocurricular activities as opportunities for learning. The results from the content analysis aligned with the quantitative data collected. Cocurricular activities provide opportunities for Doctor of Pharmacy students to enhance their skills, knowledge, and attitudes in both pharmacy practice and personal areas that map to ACPE Standards 2016 and the college’s own curricular outcomes.
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks

While neural approaches have achieved significant improvement in machine comprehension tasks, models often work as a black-box, resulting in lower interpretability, which requires special attention in domains such as healthcare or education. Quantifying uncertainty helps pave the way towards more interpretable neural networks. In classification and regression tasks, Bayesian neural networks have been effective in estimating model uncertainty. However, inference time increases linearly due to the required sampling process in Bayesian neural networks. Thus speed becomes a bottleneck in tasks with high system complexity such as question-answering or dialogue generation. In this work, we propose a hybrid neural architecture to quantify model uncertainty using Bayesian weight approximation but boosts up the inference speed by 80% relative at test time, and apply it for a clinical dialogue comprehension task. The proposed approach is also used to enable active learning so that an updated model can be trained more optimally with new incoming data by selecting samples that are not well-represented in the current training scheme.
Corpus evidence for word order freezing in Russian and German

We use Universal Dependencies treebanks to test whether a well-known typological trade-off between word order freedom and richness of morphological marking of core arguments holds within individual languages. Using Russian and German treebank data, we show that the following phenomenon (sometimes dubbed word order freezing) does occur: those sentences where core arguments cannot be distinguished by morphological means (due to case syncretism or other kinds of ambiguity) have more rigid order of subject, verb and object than those where unambiguous morphological marking is present. In ambiguous clauses, word order is more often equal to the one which is default or dominant (most frequent) in the language. While Russian and German differ with respect to how exactly they mark core arguments, the effect of morphological ambiguity is significant in both languages. It is, however, small, suggesting that languages do adapt to the evolutionary pressure on communicative efficiency and avoidance of redundancy, but that the pressure is weak in this particular respect.
Incorporating Temporal Information in Entailment Graph Mining

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win ̸→ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.
LAST at SemEval-2020 Task 10: Finding Tokens to Emphasise in Short Written Texts with Precomputed Embedding Models and LightGBM

To select tokens to be emphasised in short texts, a system mainly based on precomputed embedding models, such as BERT and ELMo, and LightGBM is proposed. Its performance is low. Additional analyzes suggest that its effectiveness is poor at predicting the highest emphasis scores while they are the most important for the challenge and that it is very sensitive to the specific instances provided during learning.
Sentence Matching with Syntax- and Semantics-Aware BERT

Sentence matching aims to identify the special relationship between two sentences, and plays a key role in many natural language processing tasks. However, previous studies mainly focused on exploiting either syntactic or semantic information for sentence matching, and no studies consider integrating both of them. In this study, we propose integrating syntax and semantics into BERT with sentence matching. In particular, we use an implicit syntax and semantics integration method that is less sensitive to the output structure information. Thus the implicit integration can alleviate the error propagation problem. The experimental results show that our approach has achieved state-of-the-art or competitive performance on several sentence matching datasets, demonstrating the benefits of implicitly integrating syntactic and semantic features in sentence matching.
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations

Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes with the emotion interaction. We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time. To address this problem, we propose an iterative emotion interaction network, which uses iteratively predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. This approach solves the above problem, and can effectively retain the performance advantages of explicit modeling. We conduct experiments on two datasets, and our approach achieves state-of-the-art performance.
JCT at SemEval-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams

In this paper, we describe our submissions to SemEval-2020 contest. We tackled subtask 12 - “Multilingual Offensive Language Identification in Social Media”. We developed different models for four languages: Arabic, Danish, Greek, and Turkish. We applied three supervised machine learning methods using various combinations of character and word n-gram features. In addition, we applied various combinations of basic preprocessing methods. Our best submission was a model we built for offensive language identification in Danish using Random Forest. This model was ranked at the 6 position out of 39 submissions. Our result is lower by only 0.0025 than the result of the team that won the 4 place using entirely non-neural methods. Our experiments indicate that char ngram features are more helpful than word ngram features. This phenomenon probably occurs because tweets are more characterized by characters than by words, tweets are short, and contain various special sequences of characters, e.g., hashtags, shortcuts, slang words, and typos.
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment

Cross-lingual entity alignment, which aims to match equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many graph neural network (GNN) based methods are proposed for entity alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned links between two KGs. In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual entity alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable performance gains compared to state-of-the-art methods.
Annobot: Platform for Annotating and Creating Datasets through Conversation with a Chatbot

In this paper, we introduce Annobot: a platform for annotating and creating datasets through conversation with a chatbot. This natural form of interaction has allowed us to create a more accessible and flexible interface, especially for mobile devices. Our solution has a wide range of applications such as data labelling for binary, multi-class/label classification tasks, preparing data for regression problems, or creating sets for issues such as machine translation, question answering or text summarization. Additional features include pre-annotation, active sampling, online learning and real-time inter-annotator agreement. The system is integrated with the popular messaging platform: Facebook Messanger. Usability experiment showed the advantages of the proposed platform compared to other labelling tools. The source code of Annobot is available under the GNU LGPL license at https://github.com/rafalposwiata/annobot.
NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo

This paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles. This task comprises of two different sub-tasks, namely A: Span Identification (SI), B: Technique Classification (TC). The goal for the SI sub-task is to identify specific fragments, in a given plain text, containing at least one propaganda technique. The TC sub-task aims to identify the applied propaganda technique in a given text fragment. A different model was trained for each sub-task. Our best performing system for the SI task consists of pre-trained ELMo word embeddings followed by residual bidirectional LSTM network. For the TC sub-task pre-trained word embeddings from GloVe fed to a bidirectional LSTM neural network. The models achieved rank 28 among 36 teams with F1 score of 0.335 and rank 25 among 31 teams with 0.463 F1 score for SI and TC sub-tasks respectively. Our results indicate that the proposed deep learning models, although relatively simple in architecture and fast to train, achieve satisfactory results in the tasks on hand.
Robust Machine Reading Comprehension by Learning Soft labels

Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels. We argue that hard labels limit the model capability on generalization due to the label sparseness problem. In this paper, we propose a robust training method for MRC models to address this problem. Our method consists of three strategies, 1) label smoothing, 2) word overlapping, 3) distribution prediction. All of them help to train models on soft labels. We validate our approach on the representative architecture - ALBERT. Experimental results show that our method can greatly boost the baseline with 1% improvement in average, and achieve state-of-the-art performance on NewsQA and QUOREF.
Topic and Emotion Development among Dutch COVID-19 Twitter Communities in the early Pandemic

The paper focuses on a large collection of Dutch tweets to gain insight into the perception and reactions of users during the early months of the COVID-19 pandemic. We focused on five major communities of users: government and health organizations, news media, politicians, the general public and conspiracy theory supporters, investigating differences among them in topic dominance and the expressions of emotions. Through topic modeling we monitor the evolution of the conversation about COVID-19 among these communities. Our results indicate that the focus on COVID-19 shifted from the virus itself to its impact on the economy between February and April. Surprisingly, the overall emotional public response appears to be substantially positive and expressing trust, although differences can be observed in specific groups of users.
A guide to the dataset explosion in QA, NLI, and commonsense reasoning

Question answering, natural language inference and commonsense reasoning are increasingly popular as general NLP system benchmarks, driving both modeling and dataset work. Only for question answering we already have over 100 datasets, with over 40 published after 2018. However, most new datasets get “solved” soon after publication, and this is largely due not to the verbal reasoning capabilities of our models, but to annotation artifacts and shallow cues in the data that they can exploit. This tutorial aims to (1) provide an up-to-date guide to the recent datasets, (2) survey the old and new methodological issues with dataset construction, and (3) outline the existing proposals for overcoming them. The target audience is the NLP practitioners who are lost in dozens of the recent datasets, and would like to know what these datasets are actually measuring. Our overview of the problems with the current datasets and the latest tips and tricks for overcoming them will also be useful to the researchers working on future benchmarks.
Domain-Specific Sentiment Lexicons Induced from Labeled Documents

Sentiment analysis is an area of substantial relevance both in industry and in academia, including for instance in social studies. Although supervised learning algorithms have advanced considerably in recent years, in many settings it remains more practical to apply an unsupervised technique. The latter are oftentimes based on sentiment lexicons. However, existing sentiment lexicons reflect an abstract notion of polarity and do not do justice to the substantial differences of word polarities between different domains. In this work, we draw on a collection of domain-specific data to induce a set of 24 domain-specific sentiment lexicons. We rely on initial linear models to induce initial word intensity scores, and then train new deep models based on word vector representations to overcome the scarcity of the original seed data. Our analysis shows substantial differences between domains, which make domain-specific sentiment lexicons a promising form of lexical resource in downstream tasks, and the predicted lexicons indeed perform effectively on tasks such as review classification and cross-lingual word sentiment prediction.