Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization

Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event detection as two independent tasks, which makes it impossible to integrate their advantages and obtain a globally optimal summary. In this paper, we present a joint learning-based heterogeneous graph attention network for TLS (HeterTls), in which date selection and event detection are combined into a unified framework to improve the extraction accuracy and remove redundant sentences simultaneously. Our heterogeneous graph involves multiple types of nodes, the representations of which are iteratively learned across the heterogeneous graph attention layer. We evaluated our model on four datasets, and found that it significantly outperformed the current state-of-the-art baselines with regard to ROUGE scores and date selection metrics.
Explicit Graph Reasoning Fusing Knowledge and Contextual Information for Multi-hop Question Answering

Current graph-neural-network-based (GNN-based) approaches to multi-hop questions integrate clues from scattered paragraphs in an entity graph, achieving implicit reasoning by synchronous update of graph node representations using information from neighbours; this is poorly suited for explaining how clues are passed through the graph in hops. In this paper, we describe a structured Knowledge and contextual Information Fusion GNN (KIFGraph) whose explicit multi-hop graph reasoning mimics human step by step reasoning. Specifically, we first integrate clues at multiple levels of granularity (question, paragraph, sentence, entity) as nodes in the graph, connected by edges derived using structured semantic knowledge, then use a contextual encoder to obtain the initial node representations, followed by step-by-step two-stage graph reasoning that asynchronously updates node representations. Each node can be related to its neighbour nodes through fused structured knowledge and contextual information, reliably integrating their answer clues. Moreover, a masked attention mechanism (MAM) filters out noisy or redundant nodes and edges, to avoid ineffective clue propagation in graph reasoning. Experimental results show performance competitive with published models on the HotpotQA dataset.
OK Boomer: Probing the socio-demographic Divide in Echo Chambers

Social media platforms such as Twitter or Reddit have become an integral part in political opinion formation and discussions, accompanied by potential echo chamber forming. In this paper, we examine the relationships between the interaction patterns, the opinion polarity, and the socio-demographic characteristics in discussion communities on Reddit. On a dataset of over 2 million posts coming from over 20k users, we combine network community detection algorithms, reliable stance polarity annotations, and NLP-based socio-demographic estimations, to identify echo chambers and understand their properties at scale. We show that the separability of the interaction communities is more strongly correlated to the relative socio-demographic divide, rather than the stance polarity gap size. We further demonstrate that the socio-demographic classifiers have a strong topical bias and should be used with caution, merely for the relative community difference comparisons within a topic, rather than for any absolute labeling.
Frustratingly Easy System Combination for Grammatical Error Correction

In this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification. We demonstrate that with the right problem formulation, a simple logistic regression algorithm can be highly effective for combining GEC models. Our method successfully increases the F0.5 score from the highest base GEC system by 4.2 points on the CoNLL-2014 test set and 7.2 points on the BEA-2019 test set. Furthermore, our method outperforms the state of the art by 4.0 points on the BEA-2019 test set, 1.2 points on the CoNLL-2014 test set with original annotation, and 3.4 points on the CoNLL-2014 test set with alternative annotation. We also show that our system combination generates better corrections with higher F0.5 scores than the conventional ensemble.
Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts

We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of ‘Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year’s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual’s mood (dras- tic changes-‘Switches’- and gradual changes -‘Escalations’- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual – a continuation of the CLPsych 2019 Shared Task– where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).
An exploratory data analysis: the performance differences of a medical code prediction system on different demographic groups

Recent studies show that neural natural processing models for medical code prediction suffer from a label imbalance issue. This study aims to investigate further imbalance in a medical code prediction dataset in terms of demographic variables and analyse performance differences in demographic groups. We use sample-based metrics to correctly evaluate the performance in terms of the data subject. Also, a simple label distance metric is proposed to quantify the difference in the label distribution between a group and the entire data. Our analysis results reveal that the model performs differently towards different demographic groups: significant differences between age groups and between insurance types are observed. Interestingly, we found a weak positive correlation between the number of training data of the group and the performance of the group. However, a strong negative correlation between the label distance of the group and the performance of the group is observed. This result suggests that the model tends to perform poorly in the group whose label distribution is different from the global label distribution of the training data set. Further analysis of the model performance is required to identify the cause of these differences and to improve the model building.
Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation

Cognitive distortions are counterproductive patterns of thinking that are one of the targets of cognitive behavioral therapy (CBT). These can be challenging for clinicians to detect, especially those without extensive CBT training or supervision. Text classification methods can approximate expert clinician judgment in the detection of frequently occurring cognitive distortions in text-based therapy messages. However, performance with infrequent distortions is relatively poor. In this study, we address this sparsity problem with two approaches: Data Augmentation and Domain-Specific Model. The first approach includes Easy Data Augmentation, back translation, and mixup techniques. The second approach utilizes a domain-specific pretrained language model, MentalBERT. To examine the viability of different data augmentation methods, we utilized a real-world dataset of texts between therapists and clients diagnosed with serious mental illness that was annotated for distorted thinking. We found that with optimized parameter settings, mixup was helpful for rare classes. Performance improvements with an augmented model, MentalBERT, exceed those obtained with data augmentation.
Retrieval-augmented Generation across Heterogeneous Knowledge

Retrieval-augmented generation (RAG) methods have been receiving increasing attention from the NLP community and achieved state-of-the-art performance on many NLP downstream tasks. Compared with conventional pre-trained generation models, RAG methods have remarkable advantages such as easy knowledge acquisition, strong scalability, and low training cost. Although existing RAG models have been applied to various knowledge-intensive NLP tasks, such as open-domain QA and dialogue systems, most of the work has focused on retrieving unstructured text documents from Wikipedia. In this paper, I first elaborate on the current obstacles to retrieving knowledge from a single-source homogeneous corpus. Then, I demonstrate evidence from both existing literature and my experiments, and provide multiple solutions on retrieval-augmented generation methods across heterogeneous knowledge.
Neural Networks in a Product of Hyperbolic Spaces

Machine learning in hyperbolic spaces has attracted much attention in natural language processing and many other fields. In particular, Hyperbolic Neural Networks (HNNs) have improved a wide variety of tasks, from machine translation to knowledge graph embedding. Although some studies have reported the effectiveness of embedding into the product of multiple hyperbolic spaces, HNNs have mainly been constructed in a single hyperbolic space, and their extension to product spaces has not been sufficiently studied. Therefore, we propose a novel method to extend a given HNN in a single space to a product of hyperbolic spaces. We apply our method to Hyperbolic Graph Convolutional Networks (HGCNs), extending several HNNs. Our model improved the graph node classification accuracy especially on datasets with tree-like structures. The results suggest that neural networks in a product of hyperbolic spaces can be more effective than in a single space in representing structural data.
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?

We present the Uppsala University system for SemEval-2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE). We explore the performance of multilingual reverse dictionaries as well as the possibility of utilizing annotated data in other languages to improve the quality of a reverse dictionary in the target language. We mainly focus on character-based embeddings.In our main experiment, we train multilingual models by combining the training data from multiple languages. In an additional experiment, using resources beyond the shared task, we use the training data in Russian and French to improve the English reverse dictionary using unsupervised embeddings alignment and machine translation. The results show that multilingual models occasionally but not consistently can outperform the monolingual baselines. In addition, we demonstrate an improvement of an English reverse dictionary using translated entries from the Russian training data set.
OSU at SigMorphon 2022: Analogical Inflection With Rule Features

OSU’s inflection system is a transformer whose input is augmented with an analogical exemplar showing how to inflect a different word into the target cell. In addition, alignment-based heuristic features indicate how well the exemplar is likely to match the output. OSU’s scores substantially improve over the baseline transformer for instances where an exemplar is available, though not quite matching the challenge winner. In Part 2, the system shows a tendency to over-apply the majority pattern in English, but not Arabic.
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies

Event trigger detection, entity mention recognition, event argument extraction, and relation extraction are the four important tasks in information extraction that have been performed jointly (Joint Information Extraction - JointIE) to avoid error propagation and leverage dependencies between the task instances (i.e., event triggers, entity mentions, relations, and event arguments). However, previous JointIE models often assume heuristic manually-designed dependency between the task instances and mean-field factorization for the joint distribution of instance labels, thus unable to capture optimal dependencies among instances and labels to improve representation learning and IE performance. To overcome these limitations, we propose to induce a dependency graph among task instances from data to boost representation learning. To better capture dependencies between instance labels, we propose to directly estimate their joint distribution via Conditional Random Fields. Noise Contrastive Estimation is introduced to address the maximization of the intractable joint likelihood for model training. Finally, to improve the decoding with greedy or beam search in prior work, we present Simulated Annealing to better find the globally optimal assignment for instance labels at decoding time. Experimental results show that our proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages.
UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition

This paper presents the approaches and systems of the UA-KO team for the Korean portion of SemEval-2022 Task 11 on Multilingual Complex Named Entity Recognition.We fine-tuned Korean and multilingual BERT and RoBERTA models, conducted experiments on data augmentation, ensembles, and task-adaptive pretraining. Our final system ranked 8th out of 17 teams with an F1 score of 0.6749 F1.
Factors Affecting the Accuracy and Precision of Triangulated Radio Telemetry Locations of Eastern Indigo Snakes (Drymarchon couperi)

Officers and Editors for 2014-2015 President ROBERT D. ALDRIDGE Department of Biology Saint Louis University St. Louis, MO 63103, USA President-Elect AARON BAUER Department of Biology Villanova University Villanova, PA 19085, USA Secretary MARION PREEST Joint Science Department The Claremont Colleges Claremont, CA 91711, USA Treasurer ANN PATERSON Department of Natural Science Williams Baptist College Walnut Ridge, AR 72476, USA Publications Secretary BRECK BARTHOLOMEW P.O. Box 58517 Salt Lake City, UT 84158, USA Immediate Past-President JOSEPH R. MENDELSON III Zoo Atlanta Atlanta, GA 30315, USA Directors (Class and Category) FRANK BURBRINK (2016 R) College of Staten Island, USA ALISON CREE (2016 Non-US) University of Otago, NEW ZEALAND TIFFANY DOAN (2014 R) State College of Florida, Manatee-Sarasota, USA LISA HAZARD (2016 R) Montclair State University, USA TRAVIS LADUC (2014 Mem. at-Large) University of Texas, USA JENNIFER PRAMUK (2014 Cons) Woodland Park Zoo, USA CAROL SPENCER (2014 R) University of California, Berkeley, USA GREGORY WATKINS-COLWELL (2016 R) Yale Peabody Mus. of Nat. Hist., USA Trustee GEORGE PISANI University of Kansas, USA Journal of Herpetology PAUL BARTELT, Co-Editor Waldorf College Forest City, IA 50436, USA TIFFANY DOAN, Co-Editor State College of Florida, Manatee-Sarasota Bradenton, FL 34207, USA Herpetological Review ROBERT W. HANSEN, Editor 16333 Deer Path Lane Clovis, CA 93619, USA Contributions to Herpetology KRAIG ADLER, Editor Cornell University Ithaca, NY 14853-2702, USA Facsimile Reprints in Herpetology AARON BAUER, Editor Villanova University Villanova, PA 19085, USA Catalogue of American Amphibians and Reptiles CHRISTOPHER BELL, Co-Editor University of Texas, Austin Austin, TX 78712, USA TRAVIS LADUC, Co-Editor University of Texas, Austin Austin, TX 78758, USA Herpetological Circulars JOHN J. MORIARTY, Editor Three Rivers Park District Plymouth, MN 55441, USA Herpetological Conservation JOSEPH C. MITCHELL, Editor Mitchell Ecol. Res. Service P.O. Box 5638 Gainesville, FL 32627-5638, USA Publisher of Journal of Herpetology, Herpetological Review, Herpetological Circulars,
Augmented Human Action Classification with Joint Estimation via double CNN

In this project, we proposed an augmented human action classification model with human joint keypoint information. To approach this final goal of our project, we completed two intermediate goals: the first goal was to implement a single-person joint localization model, in which we explored three models: the CDRP model which we implemented in our own, the ResNET 34 model similar to DeepPose [8] and a stacked hourglass model as benchmark. The validation accuracy using mean PCKh@0.5 metric is around 26 for baseline and 34 for ResNET34, while the benchmark model reaches around 82. The second goal was to incorporate the keypoint information to the classifier model. For the joints embedding layer, we implemented both linear and convnet methods. The baseline model for the classifier was a CNN model with only the image as input, which we included for comparison purposes. The validation accuracy for base-line model was 48.34%, while the models with linear and convnet joints embedding layer had validation accuracies of 52.19% and 63.53% respectively. We concluded that including joints information was beneficial to the model performance, and that the convnet embedding layer outper-formed the linear as it included information of joint connections. Lastly, the limitations of our model and the dataset were identified, and possible future work was discussed.
Giant Osteochondroma of the mandibular condyle and temporomandibular joint - A case report

Osteochondroma is one of the common bone tumours but is rarely seen in the head and neck region. Osteochondroma of the mandibular condyle, extending to the temporomandibular joint (TMJ) is an infrequent occurrence. Patients commonly present with restricted mouth opening and malocclusion. Due to the significant overlap in features between chondromas and condylar hyperplasia, it is very likely to be misdiagnosed, resulting in treatment errors. In this report, an interesting case of a large osteochondroma of the mandibular condyle extending into the zygomatic and petrous part of the temporal bone involving the left TMJ in a 35-year-old female patient is described who presented with facial asymmetry and restricted mouth opening. This paper outlines the clinico-radiographic and histopathological features for diagnosis and appropriate treatment of osteochondroma.
Detection and comparison of prevalence of Porphyromonas gingivalis through culture and Real Time-polymerase chain reaction in subgingival plaque samples of chronic periodontitis and healthy individuals

Introduction: The micro-flora of oral cavity is a myriad of micro-organism. Any infection of oral cavity leads to diseased condition which is a transitional transformation of the micro-organism in a specific paradigm depending upon the diseased condition. Periodontitis is one of the predominant chronic diseases which is a multifactorial infection. Porphyromonas gingivalis is a key etiological agent in causing periodontitis. To study the predominance of these bacteria in the diseased condition is important to detect, quantify and to find its efficacy by comparing different methods for identification. Aim and Objectives: The aim of the study is to determine the prevalence of P. gingivalis by anerobic culture and by real-time polymerase chain reaction (PCR) from subgingival plaque samples of chronic periodontitis and healthy individual and to compare efficacy of two methods. Materials and Methods: A total of 400 subjects were considered, and subgingival plaque was collected using paper points. Individual were equally divided into two groups: chronic periodontitis (200) and healthy individuals (200). Each plaque sample collected was divided into two aliquots of which the first aliquot was subjected for anerobic culture to isolate P. gingivalis. Phenotypical identification was done morphologically and biochemically further quantification of P. gingivalis was done by colony-forming unit. The second aliquot was subjected for DNA extraction and real-time PCR was conducted to detect and quantify P. gingivalis using specific primer. Results: Out of 400 samples, 73% showed detection of P. gingivalis by culture method and through reverse transcription-PCR (RT-PCR), the detection was 75%. Individual detection of P. gingivalis by culture in chronic periodontitis was 89.5% and 54.4% in healthy individuals, while detection by RT-PCR was found to be 91.5% in chronic periodontitis and 58% in healthy individuals. However, comparison between two techniques in detection of P. gingivalis was statistically insignificant. Conclusion: When we compared RT-PCR with culture RT-PCR showed higher positivity. RT-PCR is more sensitive and requires less time to detect. However, in the present study, culture also showed good positivity, suggesting proper dilution and with extended incubation, the specificity of culture can be improved to a great extent.
Microhardness Evaluation of Microhybrid Versus Nanofilled Resin Composite After Exposure to Acidic Drinks

Objective: To investigate the effect of two acidic agents on microhardness of nanofilled and microhybrid resin composite materials. Materials and Methods: 70 resin composite discs (10 mm diameter and 2 mm thick) were prepared from 2 resin composites (35 from each type), nanofilled (Z350XT) and microhybrid (Z250), 3M/ESPE. Ten discs (5 from each group) were used as control (tested at 1 h before immersion). Each group was divided into 3 subgroups (n = 10) according to the storage media: distilled water (control), box-type cola and orange juice. Each subgroup was further subdivided into 2 divisions (n = 5) according to microhardness testing at 24 h and 7days after immersions. Digital Vickers Microhardness Tester (FM-7, Future Tech, Tokyo, Japan) was used to measure surface microhardness with a Vickers diamond indenter. The surface of the specimens received a load of 200 g for 10 seconds. Three indentations not less than 1 mm from each other were placed on the surface of all specimens. Vickers hardness number (VHN) was calculated for each indentation Data were statistically analyzed using one- way ANOVA followed by Newman-keuls tests (P ≤ 0.05). Results: Orange juice showed statistically significantly the lowest VHN mean value (92.7) followed by the Cola group (95.15) then the water group (104.02) compared to the control group (117.4). Microhybrid composite groups showed statistically significant higher VHN mean value (108.1) than the nanofilled composite (100.2). The 7days groups showed statistically significant lower VHN mean value (97.3) than 24h groups (106.6). Conclusions: All storage media reduced hardness of resin composites with orange juice showing the highest reduction in hardness values. Microhybrid is more resistant to degradation than nanofilled composite. Over time, microhardness of resin composites decreased progressively.