Miracle-dz/UniFAR-FTUs huggingface dataset:
CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries

The huge influx of published papers in the field of machine learning makes the task of summarization of scholarly documents vital, not just to eliminate the redundancy but also to provide a complete and satisfying crux of the content. We participated in LongSumm 2021: The 2^{nd} Shared Task on Generating Long Summaries for scientific documents, where the task is to generate long summaries for scientific papers provided by the organizers. This paper discusses our extractive summarization approach to solve the task. We used TextRank algorithm with the BM25 score as a similarity function. Even after being a graph-based ranking algorithm that does not require any learning, TextRank produced pretty decent results with minimal compute power and time. We attained 3^{rd} rank according to ROUGE-1 scores (0.5131 for F-measure and 0.5271 for recall) and performed decently as shown by the ROUGE-2 scores.
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network

Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of the constructed knowledge graphs. Fortunately, other triples in the sentence provide supplementary information for discovering entity pairs that may have implicit relations. Also, the relation types between the implicitly connected entity pairs can be identified with relational reasoning patterns in the real world. In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples. To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory. To infer the relation types of implicit relational triples, we propose to introduce real-world relational reasoning patterns in our model and capture these patterns with a relation network. We conduct experiments on several benchmark datasets, and the results prove the validity of our method.
Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining

This paper describes UTokyo’s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas. We present a low-resource machine translation system that improves translation accuracy using cross-lingual language model pretraining. Our system uses an mBART implementation of fairseq to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Asháninka, Guaraní, Wixarika, Náhuatl, Hñähñu, Quechua, Shipibo-Konibo, and Rarámuri. On average, our system achieved BLEU scores that were 1.64 higher and chrF scores that were 0.0749 higher than the baseline.
Transliteration for Low-Resource Code-Switching Texts: Building an Automatic Cyrillic-to-Latin Converter for Tatar

We introduce a Cyrillic-to-Latin transliterator for the Tatar language based on subword-level language identification. The transliteration is a challenging task due to the following two reasons. First, because modern Tatar texts often contain intra-word code-switching to Russian, a different transliteration set of rules needs to be applied to each morpheme depending on the language, which necessitates morpheme-level language identification. Second, the fact that Tatar is a low-resource language, with most of the texts in Cyrillic, makes it difficult to prepare a sufficient dataset. Given this situation, we proposed a transliteration method based on subword-level language identification. We trained a language classifier with monolingual Tatar and Russian texts, and applied different transliteration rules in accord with the identified language. The results demonstrate that our proposed method outscores other Tatar transliteration tools, and imply that it correctly transcribes Russian loanwords to some extent.
Continual Learning for Neural Machine Translation

Neural machine translation (NMT) models are data-driven and require large-scale training corpus. In practical applications, NMT models are usually trained on a general domain corpus and then fine-tuned by continuing training on the in-domain corpus. However, this bears the risk of catastrophic forgetting that the performance on the general domain is decreased drastically. In this work, we propose a new continual learning framework for NMT models. We consider a scenario where the training is comprised of multiple stages and propose a dynamic knowledge distillation technique to alleviate the problem of catastrophic forgetting systematically. We also find that the bias exists in the output linear projection when fine-tuning on the in-domain corpus, and propose a bias-correction module to eliminate the bias. We conduct experiments on three representative settings of NMT application. Experimental results show that the proposed method achieves superior performance compared to baseline models in all settings.
Continuous Model Improvement for Language Understanding with Machine Translation

Scaling conversational personal assistants to a multitude of languages puts high demands on collecting and labelling data, a setting in which cross-lingual learning techniques can help to reconcile the need for well-performing Natural Language Understanding (NLU) with a desideratum to support many languages without incurring unacceptable cost. In this work, we show that automatically annotating unlabeled utterances using Machine Translation in an offline fashion and adding them to the training data can improve performance for existing NLU features for low-resource languages, where a straightforward translate-test approach as considered in existing literature would fail the latency requirements of a live environment. We demonstrate the effectiveness of our method with intrinsic and extrinsic evaluation using a real-world commercial dialog system in German. Beyond an intrinsic evaluation, where 56% of the resulting automatically labeled utterances had a perfect match with ground-truth labels, we see significant performance improvements in an extrinsic evaluation settings when manual labeled data is available in small quantities.
Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions

We present the first work on automatically capturing alliance rupture in transcribed therapy sessions, trained on the text and self-reported rupture scores from both therapists and clients. Our NLP baseline outperforms a strong majority baseline by a large margin and captures client reported ruptures unidentified by therapists in 40% of such cases.
Highland Puebla Nahuatl Speech Translation Corpus for Endangered Language Documentation

Documentation of endangered languages (ELs) has become increasingly urgent as thousands of languages are on the verge of disappearing by the end of the 21st century. One challenging aspect of documentation is to develop machine learning tools to automate the processing of EL audio via automatic speech recognition (ASR), machine translation (MT), or speech translation (ST). This paper presents an open-access speech translation corpus of Highland Puebla Nahuatl (glottocode high1278), an EL spoken in central Mexico. It then addresses machine learning contributions to endangered language documentation and argues for the importance of speech translation as a key element in the documentation process. In our experiments, we observed that state-of-the-art end-to-end ST models could outperform a cascaded ST (ASR > MT) pipeline when translating endangered language documentation materials.
Entity Prediction in Knowledge Graphs with Joint Embeddings

Knowledge Graphs (KGs) have become increasingly popular in the recent years. However, as knowledge constantly grows and changes, it is inevitable to extend existing KGs with entities that emerged or became relevant to the scope of the KG after its creation. Research on updating KGs typically relies on extracting named entities and relations from text. However, these approaches cannot infer entities or relations that were not explicitly stated. Alternatively, embedding models exploit implicit structural regularities to predict missing relations, but cannot predict missing entities. In this article, we introduce a novel method to enrich a KG with new entities given their textual description. Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly. We show that our approach can identify new concepts in a document corpus and transfer them into the KG, and we find that the performance of our method improves substantially when extended with techniques from association rule mining, text mining, and active learning.
Classification of COVID19 tweets using Machine Learning Approaches

The reported work is a description of our participation in the “Classification of COVID19 tweets containing symptoms” shared task, organized by the “Social Media Mining for Health Applications (SMM4H)” workshop. The literature describes two machine learning approaches that were used to build a three class classification system, that categorizes tweets related to COVID19, into three classes, viz., self-reports, non-personal reports, and literature/news mentions. The steps for pre-processing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation. Both the developed learning models, when evaluated by the organizers, garnered F1 scores of 0.93 and 0.92 respectively.
Determining the Credibility of Science Communication

Most work on scholarly document processing assumes that the information processed is trust-worthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible – e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. I will present some first steps towards addressing these problems and outline remaining challenges.
Posterior summaries of grocery retail topic models: Evaluation, interpretability and credibility

Understanding the shopping motivations behind market baskets has significant commercial value for the grocery retail industry. The analysis of shopping transactions demands techniques that can cope with the volume and dimensionality of grocery transactional data while delivering interpretable outcomes. Latent Dirichlet allocation (LDA) allows processing grocery transactions and the discovering of customer behaviours. Interpretations of topic models typically exploit individual samples overlooking the uncertainty of single topics. Moreover, training LDA multiple times show topics with large uncertainty, that is, topics (dis)appear in some but not all posterior samples, concurring with various authors in the field. In response, we introduce a clustering methodology that post‐processes posterior LDA draws to summarise topic distributions represented as recurrent topics. Our approach identifies clusters of topics that belong to different samples and provides associated measures of uncertainty for each group. Our proposed methodology allows the identification of an unconstrained number of customer behaviours presented as recurrent topics. We also establish a more holistic framework for model evaluation, which assesses topic models based not only on their predictive likelihood but also on quality aspects such as coherence and distinctiveness of single topics and credibility of a set of topics. Using the outcomes of a tailored survey, we set thresholds that aid in interpreting quality aspects in grocery retail data. We demonstrate that selecting recurrent topics not only improves predictive likelihood but also outperforms interpretability and credibility. We illustrate our methods with an example from a large British supermarket chain.
Comments on “Expanding grass-based agriculture on marginal land in the U.S. Great Plains: The role of management intensive grazing”

A recent paper by Wang et al. (2021) argues that management intensive grazing (MIG) practice adoption might be ‘ . a key factor for restoring marginal croplands to permanent grassland cover . ’ in the United States Northern Great Plains. The matter is important for land use policy because U.S. Federal and State governments actively seek to promote grass cover through a variety of policy instruments. In this note we show that the significant positive coefficient on MIG adoption they estimate in a multivariate ordered probit model does not indicate a valid causal relationship. Their estimates are vulnerable to bias and inconsistency due to potential endogeneity and so do not support their policy inference.
A-DDPG: Attention Mechanism-based Deep Reinforcement Learning for NFV

The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different quality of service (QoS) requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and traffic routing problem. However, those prior approaches mainly assume that the state of the network is static and known, disregarding real-time network variations. To bridge that gap, in this paper, we formulate the VNF placement and traffic routing problem as a Markov Decision Process model to capture the dynamic network state transitions. In order to jointly minimize the delay and cost of NFV providers and maximize the revenue, we devise a customized Deep Reinforcement Learning (DRL) algorithm, called A-DDPG, for VNF placement and traffic routing in a real-time network. A-DDPG uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). The simulation results show that A-DDPG outperforms the state-of-the-art in terms of network utility, delay, and cost.
A robust plant localization and identification system for precision farming – The effects of uncontrolled illumination on an end-to-end deep-learning plant detection algorithm

In this research, we investigated the robustness of an end-to-end deep-learning plant detection algorithm with respect to influences from uncontrolled illumination. For this research, we acquired two datasets, one containing images of plants taken under controlled illumination and one dataset with images acquired under uncontrolled illumination. We trained and evaluated the YOLOv3 object detector on both datasets. The object detector scored a mean Average Precision of 0.96 on controlled illumination conditions and 0.90 on the uncontrolled illumination conditions. This difference in performance is significant.
Exploring Rank Aggregation for Cross-Lingual Ontology Alignments: A Comparative Study

Cross-language ontology alignments are of paramount importance in several applications. A common approach to deﬁne proper alignments relies on identifying the relationships among concepts from different ontologies by performing multiple entity-based searches. In this strategy, the most suitable matching is deﬁned by the top-ranked concept found. Often, multiple similarity rankers, deﬁned in terms of different similarity criteria, are considered to deﬁne candidate entities. In this case, their complementary view can be exploited in the deﬁnition of the best possible matching. In this paper, we explore the use of rank aggregation functions, under both unsupervised and supervised settings, in the task of deﬁning suitable matches among entities belonging to ontologies encoded in distinct languages. We conducted a comparative study involving a comprehensive set of experiments with standard datasets from the OAEI competition, using ontologies in the Conference domain, and mappings among 36 language pairs. Experimental results show that supervised rank aggregation approaches, particularly LambdaMART and Random Forest, leads to better results in cross-language ontology matching when compared with unsupervised techniques.
Quantitative biofilm for bacterial pathogens of ventilator-associated pneumonia

Background & Objective: Ventilator–associated pneumonia (VAP) is one of the most common nosocomial infections in clinical care settings. Several bacteria with biofilm–producing ability offer serious challenge in their eradication. Prompt and accurate diagnosis is needed to provide the best care for the patients. This study aimed to analyze whether biofilm examination using quantitative method can be used as a diagnostic tool for bacterial pathogens associated with VAP. Methodology: This observational analytical study was conducted in Intensive Care Units of three teaching hospitals in Surakarta, Central Java, Indonesia, from November 2019 to April 2020. The subjects were between 19 and 65 y old, with a newly introduced endotracheal tube (ETT) connected to mechanical ventilators, and without pneumonia. Biofilm quantitative measurement used a microtiter plate method from bacterial culture found on ETT at the 48th hour after being mechanically ventilated. The Clinical Pulmonary Infection Score (CPIS) assessment was done at the 48th hour and CPIS of less than 6 was defined as VAP. The analysis used Spearman’s rank and Kendall tau–b correlation. The samples were taken using a consecutive sampling technique. Results: A significant correlation between biofilm and VAP was found (ρ = 0.039, p < 0.05). Biofilm was also sufficiently correlated with an increase in CPIS (τb = 0.341, p < 0.05) Conclusions: Quantitative biofilm can be used as a diagnostic tool for establishing the diagnosis of VAP so that appropriate therapy can be administered immediately. Key words: Bacterial pathogen; Biofilm; Ventilator–associated pneumonia Abbreviations: CPIS: Clinical Pulmonary Infection Score; VAP: Ventilator–associated pneumonia; ETT: Endotracheal tube; PCR: Polymerase chain reaction; OD: Optical density Citation: Dewi FH, Suradi, Purwanto B, Wasita B. Quantitative biofilm for bacterial pathogens of ventilator-associated pneumonia. Anaesth. pain intensive care 2021;25(2):132-137. DOI: 10.35975/apic.v25i2. 1468 Received: 24 September 2020, Reviewed: 27, 30 October 2020, Accepted: 3 March 2021
Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data

—Social media has rapidly expanded over a period of time and generated a huge repository of content. Sentiment analysis of this data has a vast scope in decision support and attracted many researchers to explore various possibilities for technique enhancement and accuracy improvement. Twitter is one of the social media platforms that are widely explored in the area of sentiment analysis. This paper presents a systematic survey related to Social Networking Sites Sentiment Analysis and mainly focus on Twitter sentiment analysis. The paper explores and identifies the techniques and tools used in a well-structured approach to find out the research gaps and identify future scope in this area of research. The techniques evolved over time to improve the efficiency of classification. Total 55 research papers are included in this survey. The result reflects that Twitter is the most explored social networking site for opinion mining. Naïve Bayes and SVM machine learning algorithms are implemented in maximum researches. As the latest advancements, Stack based ensemble, fuzzy based and neural network based classifiers are also implemented to enhance the efficiency of classification. WEKA, R Studio, Python are mostly used tools by research scholars for implementation. The overall evolution of the research goes through various changes in terms of technologies, tools, social media platforms and data corpus targeted.
Effect of Biochar Treated Sewage Sludge on the Yield and Uptake of Copper and Zinc in Chinese Cabbage (Brassica rapa)

One of the potential uses of treated sewage sludge is its application as a soil conditioner in agricultural fields. A factorial experiment was conducted to compare the main effects of biochar and sewage sludge and the interaction effect between biochar and sewage sludge on the plant uptake of copper and zinc and vegetable yield. The experiment was done in a green house and Chinese cabbage was used as the test crop. The experiment consisted of two factors namely, biochar and sewage sludge, each of which was applied to the soil at four levels. The copper and zinc concentration were determined in the laboratory based on Atomic Absorption Spectroscopy. A two-way analysis of variance test was performed, and the Tukey’s HSD test was used to separate statistically significant treatment means at 5% level of significance. Results showed a significant interaction effect between biochar and sewage sludge (p = 0.012) on the level of copper concentration in plant tissue. It was observed that sewage sludge had a significant effect on zinc concentration (p < 0.001) in the plant tissue while biochar did not show any significant effect. Further, results showed significant increase in dry matter yield of Chinese cabbage with increasing levels of biochar applied (p < 0.001). This study demonstrates that biochar-treated sewage sludge has potential for use as a soil conditioner in vegetable production. However, it is recommended that care should be taken to avoid overuse by utilizing evaluated and treated sludge for soil amendment.
Understanding and Mitigating Bias in Online Health Search

Search engines are perceived as a reliable source for general information needs. However, finding the answer to medical questions using search engines can be challenging for an ordinary user. Content can be biased and results may present different opinions. In addition, interpreting medically related content can be difficult for users with no medical background. All of these can lead users to incorrect conclusions regarding health related questions. In this work we address this problem from two perspectives. First, to gain insight on users' ability to correctly answer medical questions using search engines, we conduct a comprehensive user study. We show that for questions regarding medical treatment effectiveness, participants struggle to find the correct answer and are prone to overestimating treatment effectiveness. We analyze participants' demographic traits according to age and education level and show that this problem persists in all demographic groups. We then propose a semi-automatic machine learning approach to find the correct answer to queries on medical treatment effectiveness as it is viewed by the medical community. The model relies on the opinions presented in medical papers related to the queries, as well as features representing their impact. We show that, compared to human behaviour, our method is less prone to bias. We compare various configurations of our inference model and a baseline method that determines treatment effectiveness based solely on the opinion of medical papers. The results bolster our confidence that our approach can pave the way to developing automatic bias-free tools that can help mediate complex health related content to users.