Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute Effects of kinesiotaping on Pain and Muscle Strength in Individuals with Anterior Cruciate Ligament Tear

Abstract Objective Limited data are available regarding the effects of application of kinesiology tape (KT) on pain and muscular strength in individuals with complete anterior cruciate ligament (ACL) tear. Thus, we aimed to evaluate the short-term effects of KT on knee pain and maximal strength. Materials and Methods 25 subjects were divided into two groups: a) the control group participants (n=11) were without ACL-tear; and b) the experimental group participants (n=14) were with ACL-tear. Participants in the experimental group were tested under two conditions: with and without KT. Pain was recorded using the visual analog scale, isometric and concentric maximal voluntary contractions (MVCs) were measured using dynamometry, and limb symmetry index was calculated. Results Application of KT causes reduction in pain by 50 % (p<0.05), increases knee flexors and knee extensors isometric MVC and knee extensor concentric MVC at 60°/s angular velocity (p<0.05), and improves limb symmetry (p<0.05), but deficit of MVC as compared to healthy controls is maintained (p<0.05). Conclusions Short-term KT can be an effective tool for decreasing ACL-tear associated pain and muscle weakness.
Deep Neural Network with LinUCB: A Contextual Bandit Approach for Personalized Recommendation

Recommender systems are widely used in many Web applications to recommend items which are relevant to a user’s preferences. However, focusing on exploiting user preferences while ignoring exploration will lead to biased feedback and hurt the user’s experience in the long term. The Mutli-Armed Bandit (MAB) is introduced to balance the tradeoff between exploitation and exploration. By utilizing context information in the reward function, contextual bandit algorithms lead to better performance compared to context-free bandit algorithms. However, existing contextual bandit algorithms either assume a linear relation between the expected reward and context features, whose representation power gets limited, or use a deep neural network in the reward function which is impractical in implementation. In this paper, we propose a new contextual bandit algorithm, DeepLinUCB, which leverages the representation power of deep neural network to transform the raw context features in the reward function. Specifically, this deep neural network is dedicated to the recommender system, which is efficient and practical in real-world applications. Furthermore, we conduct extensive experiments in our online recommender system using requests from real-world scenarios and show that DeepLinUCB is efficient and outperforms other bandit algorithms.
Factors Affecting Grade Eight Students' Learning Motivation in Chinese Reading when Learning with Flipped Classroom Approach

With the spread of information technology in Chinese education, the flipped classroom, an innovative learning method with the potential to improve teaching effectiveness, is gradually gaining attention from scholars. However, limited research has been conducted on Chinese language teaching in the flipped classroom, which is a major subject for a large number of Chinese students. It has been shown that the motivation of students to study in flipped classroom activities can be affected by many factors and they may impact the effectiveness of the course. Therefore, this study randomly selected 48 Grade 8 students from Mainland China to participate in three-week Chinese reading flipped classroom activities. Both quantitative (questionnaires) and qualitative (interviews) methods were used to explore which factors significantly impacted students' motivation in the Chinese reading flipped classroom activities and how these factors influenced students from the student's perspective. The results showed that students' self-motivation (e.g., learning will) and self-efficacy (e.g., learning behavior) were the main factors influencing students' motivation to participate in the Chinese reading flipped classrooms, with averages of 4.239 and 4.163 respectively. And students' controlled motivation (e.g. study cognitive) was not a major factor with an average of 2.674. Through thematic analysis, it was concluded that students' self-achievement and exploratory desire stimulated their self-motivation and self-efficacy to learn in the Chinese reading flipped classrooms. In summary, this study provides educators with insights into how to increase student learning motivation.
Visually-Impaired People Studying via eBook: Investigating Current Use and Potential for Improvement

Everyday activities and tasks should be easy to perform for everyone, especially in an educational context, in order to foster inclusivity and assure equal opportunities for all. In this paper, we investigate strategies and issues experienced by visually impaired people when studying via eBook. An online survey was designed to investigate preferences regarding the different formats and understand what types of actions are possible and desirable when using eBooks in an educational context. We collected the views and experiences of 75 visually-impaired people, which revealed the need to develop tools that can provide both full accessibility and high usability when reading for study. Visually impaired people would like to rely on the same widely-used strategies that sighted people use when studying a text. In addition, 92% of the visually-impaired people participating in the online survey declared they were interested in a (new) reading app. The results could orient the design of new digital reading tools and functionalities that can improve interaction.
Investigating Zoom Continuance Use by Ghanaian University Students in Blended Learning Arrangements in the Post-Covid Era

The study sought to determine factors that significantly influence university students’ continuance use of Zoom in blended learning arrangements in the post-Covid era. Zoom Cloud Meetings is a proprietary video teleconferencing software program developed by Zoom Video Communications. Zoom became one of the e-learning platforms that gained attention in the education sector in Ghana after the COVID-19 pandemic outbreak and has been widely used by educational institutions for online lectures. The research model comprises a set of relationships between constructs from the Information System (IS) success model (system quality, usage intention, and user satisfaction), Expectation Confirmation Model (ECM) of IS (perceived usefulness and usage continuance), and two constructs identified from empirical review (instructional quality and computer self-efficacy). Three hundred and thirty-five (335) students from selected universities in Ghana who partake in Zoom-based lectures completed an online survey via Google forms. Survey data were analysed using partial least squares structural equation modelling (PLS-SEM). Lecturers and management of universities will find the study useful for the improvement of teaching and learning via Zoom-based lectures.
Dual-grained Text-Image Olfactory Matching Model with Mutual Promotion Stages

Olfactory experience has great advantages in awakening human memories and emotions, which may even surpass vision in some cases. Studies have proved that olfactory scene descriptions in images and text content can also arouse human olfactory imagination, but there are still few studies on solving related problems from the perspective of computer vision and NLP. This paper proposes a multimodal model that can detect similar olfactory experience in paired text-image samples. The model builds two stages, coarse-grained and fine-grained. The model adopts the feature fusion method based on pre-trained CLIP for coarse-grained matching training to obtain a preliminary feature extractor to promote fine-grained matching training, and then uses the similarity calculation method based on stacked cross attention for fine-grained matching training to obtain the final feature extractor which in turn promotes coarse-grained matching training. Finally, we manually build an approximate olfactory nouns list during fine-grained matching training, which not only yields significantly better performance when fed back to the fine-grained matching process, but this noun list can be used for future research. Experiments on the MUSTI task dataset of MediaEval2022 prove that the coarse-grained and fine-grained matching stages in proposed model both perform well, and both F1 measures exceed the existing baseline models.
Impact of COVID-19 Pandemic on Cultural Products Interests

The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples’ interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users’ search logs to reflect humans’ underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples’ cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks.
The association between oxidative balance score and periodontitis in adults: a population-based study

Introduction The pathogenesis between oxidative stress and periodontitis was correlated. The Oxidative Balance Score (OBS) is a systematic tool to assess the effects of diet and lifestyle in relation to oxidative stress. However, the association between OBS and periodontitis has not been reported previously. Methods Sixteen dietary factors and four lifestyle factors were selected to score the OBS. Multivariate logistic regression and sensitivity analysis were used to investigate the relationship between OBS and periodontitis based on data from the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Subgroup analysis and interaction tests were used to investigate whether this association was stable across populations. Results This study included 3,706 participants. There was a negative linear association between OBS and periodontitis in all participants [0.89 (0.80, 0.97)], and after converting OBS to a quartile variable, participants with OBS in the highest quartile had a 29% lower risk of periodontitis than those with OBS in the lowest quartile [0.71 (0.42, 0.98)]. This negative association differed with respect to age and diabetes. Conclusion There is a negative association between OBS and periodontitis in US adults. Our results suggest that OBS may be used as a biomarker for measuring periodontitis.
The Community Notes Observatory: Can Crowdsourced Fact-Checking be Trusted in Practice?

Fact-checking is an important tool in fighting online misinformation. However, it requires expert human resources, and thus does not scale well on social media because of the flow of new content. Crowdsourcing has been proposed to tackle this challenge, as it can scale with a smaller cost, but it has always been studied in controlled environments. In this demo, we present the Community Notes Observatory, an online system to evaluate the first large-scale effort of crowdsourced fact-checking deployed in practice. We let demo attendees search and analyze tweets that are fact-checked by Community Notes users and compare the crowd’s activity against professional fact-checkers. The attendees will explore evidence of i) differences in how the crowd and experts select content to be checked, ii) how the crowd and the experts retrieve different resources to fact-check, and iii) the edge the crowd shows in fact-checking scalability and efficiency as compared to expert checkers.
Experimental Investigation and Model Development for Thermal Conductivity of Glycerol–MgO Nanofluids

This paper presents experimental and theoretical determination of the effective thermal conductivity of three magnesium oxide (MgO) nanoparticles of different sizes dispersed in glycerol. The glycerol-based nanofluids were prepared at volume fractions ranging from 0.5% to 4% and no surfactant. The nanoparticles were dispersed and deagglomerated for 2 hours using an ultrasonic probe. The effective thermal conductivity of nanofluids was measured from 20°C to 45°C using a thermal conductivity analyzer. The experimental results show an increase in the thermal conductivity of MgO–glycerol nanofluids with increasing volume fraction of nanoparticles. The thermal conductivity ratio is unaffected as the temperature increases. In the given volume fraction and temperature range, the thermal conductivity ratio of MgO–glycerol nanofluids decreases with increasing particle size. The obtained experimental data were also compared with some existing theoretical and empirical models that may work for glycerol-based nanofluids. The comparison of experimental data with these available models shows that the data do not agree with the models. Therefore, a new empirical correlation was developed for the MgO–glycerol nanofluids.
Multiple solutions of p-fractional Schrödinger-Choquard-Kirchhoff equations with Hardy-Littlewood-Sobolev critical exponents

Abstract In this article, we are concerned with multiple solutions of Schrödinger-Choquard-Kirchhoff equations involving the fractional p p -Laplacian and Hardy-Littlewood-Sobolev critical exponents in R N {{\mathbb{R}}}^{N} . We classify the multiplicity of the solutions in accordance with the Kirchhoff term M ( ⋅ ) M\left(\cdot ) and different ranges of q q shown in the nonlinearity f ( x , ⋅ ) f\left(x,\cdot ) by means of the variational methods and Krasnoselskii’s genus theory. As an immediate consequence, some recent related results have been improved and extended.
Triple-Hybrid Energy-based Model Makes Better Calibrated Natural Language Understanding Models

Though pre-trained language models achieve notable success in many applications, it’s usually controversial for over-confident predictions. Specifically, the in-distribution (ID) miscalibration and out-of-distribution (OOD) detection are main concerns. Recently, some works based on energy-based models~(EBM) have shown great improvements on both ID calibration and OOD detection for images. However, it’s rarely explored in natural language understanding tasks due to the non-differentiability of text data which makes it more difficult for EBM training. In this paper, we first propose a triple-hybrid EBM which combines the benefits of classifier, conditional generative model and marginal generative model altogether. Furthermore, we leverage contrastive learning to approximately train the proposed model, which circumvents the non-differentiability issue of text data. Extensive experiments have been done on GLUE and six other multiclass datasets in various domains. Our model outperforms previous methods in terms of ID calibration and OOD detection by a large margin while maintaining competitive accuracy.
Using Punctuation as an Adversarial Attack on Deep Learning-Based NLP Systems: An Empirical Study

This work empirically investigates punctuation insertions as adversarial attacks on NLP systems. Data from experiments on three tasks, five datasets, and six models with four attacks show that punctuation insertions, when limited to a few symbols (apostrophes and hyphens), are a superior attack vector compared to character insertions due to 1) a lower after-attack accuracy ($A_{aft-atk}$) than alphabetical character insertions; 2) higher semantic similarity between the resulting and original texts; and 3) a resulting text that is easier and faster to read as assessed with the Test of Word Reading Efficiency (TOWRE)). The tests also indicate that 4) grammar checking does not mitigate punctuation insertions and 5) punctuation insertions outperform word-level attacks in settings with a limited number of word synonyms and queries to the victim’s model. Our findings indicate that inserting a few punctuation types that result in easy-to-read samples is a general attack mechanism. In light of this threat, we assess the impact of punctuation insertions, potential mitigations, the mitigation’s tradeoffs, punctuation insertion’s worst-case scenarios and summarize our findings in a qualitative casual map, so that developers can design safer, more secure systems.
A simple but effective model for attachment in discourse parsing with multi-task learning for relation labeling

In this paper, we present a discourse parsing model for conversation trained on the STAC. We fine-tune a BERT-based model to encode pairs of discourse units and use a simple linear layer to predict discourse attachments. We then exploit a multi-task setting to predict relation labels. The multitask approach effectively aids in the difficult task of relation type prediction; our f1 score of 57 surpasses the state of the art with no loss in performance for attachment, confirming the intuitive interdependence of these two tasks. Our method also improves over previous discourse parsing models in allowing longer input sizes and in permitting attachments in which one node has multiple parents, an important feature of multiparty conversation.
What Did You Learn To Hate? A Topic-Oriented Analysis of Generalization in Hate Speech Detection

Hate speech has unfortunately become a significant phenomenon on social media platforms, and it can cover various topics (misogyny, sexism, racism, xenophobia, etc.) and targets (e.g., black people, women). Various hate speech detection datasets have been proposed, some annotated for specific topics, and others for hateful speech in general. In either case, they often employ different annotation guidelines, which can lead to inconsistencies, even in datasets focusing on the same topics. This can cause issues in models trying to generalize across more data and more topics in order to improve detection accuracy. In this paper, we propose, for the first time, a topic-oriented approach to study generalization across popular hate speech datasets. We first perform a comparative analysis of the performances of Transformer-based models in capturing topic-generic and topic-specific knowledge when trained on different datasets. We then propose a novel, simple yet effective approach to study more precisely which topics are best captured in implicit manifestations of hate, showing that selecting combinations of datasets with better out-of-domain topical coverage improves the reliability of automatic hate speech detection.
“Chère maison” or “maison chère”? Transformer-based prediction of adjective placement in French

In French, the placement of the adjective within a noun phrase is subject to variation: it can appear either before or after the noun. We conduct experiments to assess whether transformer-based language models are able to learn the adjective position in noun phrases in French –a position which depends on several linguistic factors. Prior findings have shown that transformer models are insensitive to permutated word order, but in this work, we show that finetuned models are successful at learning and selecting the correct position of the adjective. However, this success can be attributed to the process of finetuning rather than the linguistic knowledge acquired during pretraining, as evidenced by the low accuracy of experiments of classification that make use of pretrained embeddings. Comparing the finetuned models to the choices of native speakers (with a questionnaire), we notice that the models favor context and global syntactic roles, and are weaker with complex structures and fixed expressions.
Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation

While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content. Recent work shows that planning can be a useful intermediate step to render conditional generation less opaque and more grounded. We present a web browser-based demonstration for query-focused summarization that uses a sequence of question-answer pairs, as a blueprint plan for guiding text generation (i.e., what to say and in what order). We illustrate how users may interact with the generated text and associated plan visualizations, e.g., by editing and modifying the plan in order to improve or control the generated output.A short video demonstrating our system is available at https://goo.gle/text-blueprint-demo
Long-tailed Extreme Multi-label Text Classification by the Retrieval of Generated Pseudo Label Descriptions

Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe data scarcity problem associated with the long tail of rare labels in highly skewed distributions. This paper addresses the challenge of tail label prediction by leveraging the power of dense neural retrieval model in mapping input documents (as queries) to relevant label descriptions. To further enhance the quality of label descriptions, we propose to generate pseudo label descriptions from a trained bag-of-words (BoW) classifier, which demonstrates better classification performance under severe scarce data conditions.The proposed approach achieves the state-of-the-art (SOTA) performance of overall label prediction on XMTC benchmark datasets and especially outperforms the SOTA models in the tail label prediction. We also provide a theoretical analysis for relating the BoW and neural models w.r. t. performance lower bound.
Get to Know Your Parallel Data: Performing English Variety and Genre Classification over MaCoCu Corpora

Collecting texts from the web enables a rapid creation of monolingual and parallel corpora of unprecedented size. However, unlike manually-collected corpora, authors and end users do not know which texts make up the web collections. In this work, we analyse the content of seven European parallel web corpora, collected from national top-level domains, by analysing the English variety and genre distribution in them. We develop and provide a lexicon-based British-American variety classifier, which we use to identify the English variety. In addition, we apply a Transformer-based genre classifier to corpora to analyse genre distribution and the interplay between genres and English varieties. The results reveal significant differences among the seven corpora in terms of different genre distribution and different preference for English varieties.
Findings of the VarDial Evaluation Campaign 2023

This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with EACL 2023. Three separate shared tasks were included this year: Slot and intent detection for low-resource language varieties (SID4LR), Discriminating Between Similar Languages – True Labels (DSL-TL), and Discriminating Between Similar Languages – Speech (DSL-S). All three tasks were organized for the first time this year.