Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial Least Squares calibrations and batch statistical process control to 1 monitor phenolic extraction in red wine fermentations under different 2 maceration conditions .

10 The extraction of phenolic compounds during maceration is of utmost importance in 11 red winemaking. However, the monitoring of phenolic extraction is often hampered 12 by analytical and statistical constraints. The aim of this study was to monitor phenolic 13 extraction kinetics with the use of PLS phenolic calibrations and batch statistical 14 process control. Eight batches of Cabernet Sauvignon and Shiraz grapes during 15 alcoholic fermentation under different maceration conditions (pressing at 1/3 rd , 2/3 rd 16 and end of fermentation) and punch down regimes (low vs. high frequency) were 17 evaluated in the study. Cabernet Sauvignon appeared to be a more suitable cultivar 18 for longer maceration conditions with increased tannin extraction observed. Similar 19 trends were observed for punch down for both cultivars. The use of PLS calibrations 20 and batch level modelling provided an enhanced interpretation and understanding of 21 phenolic extraction during red wine fermentations.
Complexities in university students’ understanding of parametric equations and curves

Despite the importance and many applications that parametric equations and curves have in mathematics and other sciences, there is a lack of research regarding this topic in mathematics education. The goal of this article is to explore university students’ understanding of parametric equations and curves based on two lenses: Action, Process, Object, Schema (APOS) and Onto-Semiotic Approach (OSA). Recent research showed that these two theories complement each other and the combination of them has good potential to analyse students’ performance of mathematical concepts. Three tasks of parametric equations and curves were proposed to characterize the schema development of twenty-seven university students in terms of triad (i.e. intra, inter and trans). Based on the results, most of the students participating in this study had difficulties regarding the practical tasks and developing the mental structures needed to solve parametric equation tasks, especially the mental structures that have to be made to draw parametric curves and identify their directions, and to compute the first and the second derivatives of parametric equations. Finally, we finish the article by making practical suggestions for instructors and researchers.
Direct Numerical Simulation of an Airfoil with Sand Grain Roughness on the Leading Edge

As part of a computational study of acoustic radiation due to the passage of turbulent boundary layer eddies over the trailing edge of an airfoil, the Lattice-Boltzmann method is used to perform direct numerical simulations of compressible, low Mach number flow past an NACA 0012 airfoil at zero degrees angle of attack. The chord Reynolds number of approximately 0.657 million models one of the test conditions from a previous experiment by Brooks, Pope, and Marcolini at NASA Langley Research Center. A unique feature of these simulations involves direct modeling of the sand grain roughness on the leading edge, which was used in the abovementioned experiment to trip the boundary layer to fully turbulent flow. This report documents the findings of preliminary, proof-of-concept simulations based on a narrow spanwise domain and a limited time interval. The inclusion of fully-resolved leading edge roughness in this simulation leads to significantly earlier transition than that in the absence of any roughness. The simulation data is used in conjunction with both the Ffowcs Williams-Hawkings acoustic analogy and a semi-analytical model by Roger and Moreau to predict the farfield noise. The encouraging agreement between the computed noise spectrum and that measured in the experiment indicates the potential payoff from a full-fledged numerical investigation based on the current approach. Analysis of the computed data is used to identify the required improvements to the preliminary simulations described herein.
Affirmative Consent on campus: student understanding, perceptions of effectiveness, and behavioral change

ABSTRACT Affirmative Consent (AC) policies have become a popular way for universities across the US to address the issue of sexual assault on campus. This study utilizes a sample of over 1400 university students to address student self-identified understanding of AC, perceptions of AC policy effectiveness at decreasing sexual assaults on campus, and self-reported behavior change. Results show that about 80% of the sample reported understanding AC, a slight majority of the sample (53%) believed AC policies were effective, and only 20.5% of respondents reported changing their own behavior as a result of AC policies. Exposure to AC through professors or administrators increased AC understanding and perceptions of effectiveness. Knowing others who changed their own behavior was significantly related to understanding of AC, perceived effectiveness of AC, and personal self-reported behavior change. Additionally, issue salience as measured through self-reported concern about sexual assault on campus and self-reported fear of sexual assault on campus was related to perceived effectiveness and self-reported behavior change. Multivariate analyses explored these issues further and raise questions of the ability of these policies alone to combat sexual assault on campus.
Job Characteristics and Job Engagement: A Two-Wave Study among Library Employees

Abstract The purpose of this study was to investigate whether coworker support moderates the relationship between role stressors and job engagement among library employees. Library employees (n = 96) were surveyed over two time periods to assess these relationships. As predicted, job engagement at time 1 was significantly related to job engagement at time 2. As hypothesized, coworker support was found to buffer the effect of role ambiguity on job engagement. Job engagement was not as impacted for employees who had supportive coworkers in the face of ambiguity compared to those who did not feel supported by their colleagues.
Peptide-based immunotherapy of experimental autoimmune encephalomyelitis without anaphylaxis

Administration of peptide antigens in tolerogenic form holds promise as a specific treatment for autoimmune and allergic disorders. However, experiments in rodent autoimmune models have highlighted the risk of anaphylaxis in response to systemic peptide application once the aberrant immune response is underway. Thus, mice with clinical signs of experimental autoimmune encephalomyelitis (EAE) or diabetes have been reported to suffer fatal anaphylaxis upon administration of native autoantigenic peptides. Clearly, this might represent a significant barrier to the use of synthetic peptides in the treatment of ongoing human autoimmune conditions. Here we describe the development of an altered peptide ligand (APL) engineered to prevent anaphylaxis (no antibody binding) whilst retaining the ability to silence pathogenic myelin‐reactive T lymphocytes. Administration of the APL to mice with an ongoing anti‐myelin immune response did not cause anaphylaxis, but led to complete protection from the subsequent induction of EAE and, when given during ongoing EAE, led to a rapid remission of clinical signs. The approach of removing antibody recognition whilst maintaining the desired functional effect (in this case T cell tolerance) may be of value in other situations in which there is a risk of triggering anaphylaxis with peptide‐based drugs.
Introduction to systems of systems engineering

This article provides an introduction and overview of basic concepts of ‘systems of systems’ (SoS). Beginning with the definition of SoS and SoS engineering, the paper describes the characteristics of SoS. It compares systems with SoS and discusses the implications for systems engineering. Finally, the paper concludes with a discussion of the challenges SoS pose for systems engineering with a description of recognized SoS ‘pain points.
Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks

This study introduces a novel approach to the joint extraction of entities and relations by stacking convolutional neural networks (CNNs) on pretrained language models. We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem. Regarding each table as an image and each cell in a table as an image pixel, we apply two-dimensional CNNs to the tables to capture local dependencies and predict the cell labels. The experimental results showed that the performance of the proposed method is comparable to those of current state-of-art systems on the CoNLL04, ACE05, and ADE datasets.Even when freezing pretrained language model parameters, the proposed method showed a stable performance, whereas the compared methods suffered from significant decreases in performance. This observation indicates that the parameters of the pretrained encoder may incorporate dependencies among the entity and relation labels during fine-tuning.
Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a robust model that generalizes across a wide array of input examples. In this paper, we propose a novel training technique for the CWI task based on domain adaptation to improve the target character and context representations. This technique addresses the problem of working with multiple domains, inasmuch as it creates a way of smoothing the differences between the explored datasets. Moreover, we also propose a similar auxiliary task, namely text simplification, that can be used to complement lexical complexity prediction. Our model obtains a boost of up to 2.42% in terms of Pearson Correlation Coefficients in contrast to vanilla training techniques, when considering the CompLex from the Lexical Complexity Prediction 2021 dataset. At the same time, we obtain an increase of 3% in Pearson scores, while considering a cross-lingual setup relying on the Complex Word Identification 2018 dataset. In addition, our model yields state-of-the-art results in terms of Mean Absolute Error.
DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling

The paper presents the results of the Shared Task hosted by the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering co-located at ACL 2022. The primary goal of this Shared Task is to build goal-oriented information-seeking conversation systems that are grounded in the domain documents, where each dialogue could correspond to multiple subtasks that are based on different documents. The task is to generate agent responses in natural language given the dialogue and document contexts. There are two task settings and leaderboards based on (1) the same sets of domains (SEEN) and (2) one unseen domain (UNSEEN). There are over 20 teams participating in Dev Phase and 8 teams participating in both Dev and Test Phases. Multiple submissions significantly outperform the baseline. The best-performing system achieves 52.06 F1 and the total of 191.30 on the SEEN task; and 34.65 F1 and the total of 130.79 on the UNSEEN task.
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese

We investigate the usage of entity linking (EL)in downstream tasks and present the first modularized EL toolkit for easy task adaptation. Different from the existing EL methods that dealwith all the features simultaneously, we modularize the whole model into separate parts witheach feature. This decoupled design enablesflexibly adding new features without retraining the whole model as well as flow visualization with better interpretability of the ELresult. We release the corresponding toolkit,HOSMEL, for Chinese, with three flexible usage modes, a live demo, and a demonstrationvideo. Experiments on two benchmarks forthe question answering task demonstrate thatHOSMEL achieves much less time and spaceconsumption as well as significantly better accuracy performance compared with existingSOTA EL methods. We hope the release ofHOSMEL will call for more attention to studyEL for downstream tasks in non-English languages.
Leveraging Similar Users for Personalized Language Modeling with Limited Data

Personalized language models are designed and trained to capture language patterns specific to individual users. This makes them more accurate at predicting what a user will write. However, when a new user joins a platform and not enough text is available, it is harder to build effective personalized language models. We propose a solution for this problem, using a model trained on users that are similar to a new user. In this paper, we explore strategies for finding the similarity between new users and existing ones and methods for using the data from existing users who are a good match. We further explore the trade-off between available data for new users and how well their language can be modeled.
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual Templates

Cross-lingual natural language inference (XNLI) is a fundamental task in cross-lingual natural language understanding. Recently this task is commonly addressed by pre-trained cross-lingual language models. Existing methods usually enhance pre-trained language models with additional data, such as annotated parallel corpora. These additional data, however, are rare in practice, especially for low-resource languages. Inspired by recent promising results achieved by prompt-learning, this paper proposes a novel prompt-learning based framework for enhancing XNLI. It reformulates the XNLI problem to a masked language modeling problem by constructing cloze-style questions through cross-lingual templates. To enforce correspondence between different languages, the framework augments a new question for every question using a sampled template in another language and then introduces a consistency loss to make the answer probability distribution obtained from the new question as similar as possible with the corresponding distribution obtained from the original question. Experimental results on two benchmark datasets demonstrate that XNLI models enhanced by our proposed framework significantly outperform original ones under both the full-shot and few-shot cross-lingual transfer settings.
Context Matters: A Pragmatic Study of PLMs’ Negation Understanding

In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view. So far, research in NLP on negation has almost exclusively adhered to the semantic view. In this article, we adopt the pragmatic paradigm to conduct a study of negation understanding focusing on transformer-based PLMs. Our results differ from previous, semantics-based studies and therefore help to contribute a more comprehensive – and, given the results, much more optimistic – picture of the PLMs’ negation understanding.
OpenBrand: Open Brand Value Extraction from Product Descriptions

Extracting attribute-value information from unstructured product descriptions continue to be of a vital importance in e-commerce applications. One of the most important product attributes is the brand which highly influences costumers’ purchasing behaviour. Thus, it is crucial to accurately extract brand information dealing with the main challenge of discovering new brand names. Under the open world assumption, several approaches have adopted deep learning models to extract attribute-values using sequence tagging paradigm. However, they did not employ finer grained data representations such as character level embeddings which improve generalizability. In this paper, we introduce OpenBrand, a novel approach for discovering brand names. OpenBrand is a BiLSTM-CRF-Attention model with embeddings at different granularities. Such embeddings are learned using CNN and LSTM architectures to provide more accurate representations. We further propose a new dataset for brand value extraction, with a very challenging task on zero-shot extraction. We have tested our approach, through extensive experiments, and shown that it outperforms state-of-the-art models in brand name discovery.
Is Attention Explanation? An Introduction to the Debate

The performance of deep learning models in NLP and other fields of machine learning has led to a rise in their popularity, and so the need for explanations of these models becomes paramount. Attention has been seen as a solution to increase performance, while providing some explanations. However, a debate has started to cast doubt on the explanatory power of attention in neural networks. Although the debate has created a vast literature thanks to contributions from various areas, the lack of communication is becoming more and more tangible. In this paper, we provide a clear overview of the insights on the debate by critically confronting works from these different areas. This holistic vision can be of great interest for future works in all the communities concerned by this debate. We sum up the main challenges spotted in these areas, and we conclude by discussing the most promising future avenues on attention as an explanation.
Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion

We advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs. Starting from well-founded assumptions about word graphs, we present simple but reliable path-reranking and topic segmentation schemes. Robustness of our method is demonstrated on datasets across multiple domains, including meetings, interviews, movie scripts, and day-to-day conversations. We also identify possible avenues to augment our heuristic-based system with deep learning. We open-source our code, to provide a strong, reproducible baseline for future research into unsupervised dialogue summarization.
PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence

Work on social media rumour verification utilises signals from posts, their propagation and users involved. Other lines of work target identifying and fact-checking claims based on information from Wikipedia, or trustworthy news articles without considering social media context. However works combining the information from social media with external evidence from the wider web are lacking. To facilitate research in this direction, we release a novel dataset, PHEMEPlus, an extension of the PHEME benchmark, which contains social media conversations as well as relevant external evidence for each rumour. We demonstrate the effectiveness of incorporating such evidence in improving rumour verification models. Additionally, as part of the evidence collection, we evaluate various ways of query formulation to identify the most effective method.
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling

In sequence modeling, certain tokens are usually less ambiguous than others, and representations of these tokens require fewer refinements for disambiguation. However, given the nature of attention-based models like Transformer and UT (universal transformer), all tokens are equally processed towards depth. Inspired by the equilibrium phenomenon, we present a lazy transition, a mechanism to adjust the significance of iterative refinements for each token representation. Our lazy transition is deployed on top of UT to build LT (lazy transformer), where all tokens are processed unequally towards depth. Eventually, LT is encouraged to oscillate around a relaxed equilibrium. Our experiments show that LT outperforms baseline models on several tasks of machine translation, pre-training, Learning to Execute, and LAMBADA.
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition

Cross-lingual named entity recognition task is one of the critical problems for evaluating the potential transfer learning techniques on low resource languages. Knowledge distillation using pre-trained multilingual language models between source and target languages have shown their superiority in transfer. However, existing cross-lingual distillation models merely consider the potential transferability between two identical single tasks across both domains. Other possible auxiliary tasks to improve the learning performance have not been fully investigated. In this study, based on the knowledge distillation framework and multi-task learning, we introduce the similarity metric model as an auxiliary task to improve the cross-lingual NER performance on the target domain. Specifically, an entity recognizer and a similarity evaluator are first trained in parallel as two teachers from the source domain. Then, two tasks in the student model are supervised by these teachers simultaneously. Empirical studies on the three datasets across 7 different languages confirm the effectiveness of the proposed model.
Critical success factors of manufacturing solution adoption: a qualitative case study in metal-stamping industry

Manufacturing solution like Enterprise Resources Planning (ERP) system is designed for the manufacturing organization to link the fragmentation of the data and information because it integrates and streamlines internal processes by providing a suite of software modules that cover all functional areas of a business. However not all ERP system project implementation is successful especially the high-mix industry because the production process is rather complex and requires to involve a lot of resources and time. These have resulted a number of researches focusing on finding the critical success factors (CSFs) of the manufacturing solution implementation. This qualitative research built a case study in a sheet metal-stamping company that operated under Make-To-Order (MTO) and high-mix environment. Face-to-face interviews were conducted and data were coded and analyzed using Nvivo 10 software. Researchers identified 10 Critical Success Factors for high-mix sheet-metal industry. This research also identified 5 success measures on manufacturing solution adoption. Researchers argue that user paradigm on success relied on the system usability and user satisfaction. In conclusion, manufacturing users still have high believed in manufacturing solution implementation because it could bring real benefits to the company eventhough there are implementation challenges.
Unmet Creativity Support Needs in Computationally Supported Creative Writing

Large language models (LLMs) enabled by the datasets and computing power of the last decade have recently gained popularity for their capacity to generate plausible natural language text from human-provided prompts. This ability makes them appealing to fiction writers as prospective co-creative agents, addressing the common challenge of writer’s block, or getting unstuck. However, creative writers face additional challenges, including maintaining narrative consistency, developing plot structure, architecting reader experience, and refining their expressive intent, which are not well-addressed by current LLM-backed tools. In this paper, we define these needs by grounding them in cognitive and theoretical literature, then survey previous computational narrative research that holds promise for supporting each of them in a co-creative setting.