Miracle-dz/UniFAR-FTUs huggingface dataset:
UMUTeam at SemEval-2022 Task 5: Combining image and textual embeddings for multi-modal automatic misogyny identification

In this manuscript we describe the participation of the UMUTeam on the MAMI shared task proposed at SemEval 2022. This task is concerning the identification of misogynous content from a multi-modal perspective. Our participation is grounded on the combination of different feature sets within the same neural network. Specifically, we combine linguistic features with contextual transformers based on text (BERT) and images (BEiT). Besides, we also evaluate other ensemble learning strategies and the usage of non-contextual pretrained embeddings. Although our results are limited, we outperform all the baselines proposed, achieving position 36 in the binary classification task with a macro F1-score of 0.687, and position 28 in the multi-label task of misogynous categorisation, with an macro F1-score of 0.663.
Scene Graph Parsing via Abstract Meaning Representation in Pre-trained Language Models

In this work, we propose the application of abstract meaning representation (AMR) based semantic parsing models to parse textual descriptions of a visual scene into scene graphs, which is the first work to the best of our knowledge. Previous works examined scene graph parsing from textual descriptions using dependency parsing and left the AMR parsing approach as future work since sophisticated methods are required to apply AMR. Hence, we use pre-trained AMR parsing models to parse the region descriptions of visual scenes (i.e. images) into AMR graphs and pre-trained language models (PLM), BART and T5, to parse AMR graphs into scene graphs. The experimental results show that our approach explicitly captures high-level semantics from textual descriptions of visual scenes, such as objects, attributes of objects, and relationships between objects. Our textual scene graph parsing approach outperforms the previous state-of-the-art results by 9.3% in the SPICE metric score.
taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning

We present a multi-modal deep learning system for the Multimedia Automatic Misogyny Identification (MAMI) challenge, a SemEval task of identifying and classifying misogynistic messages in online memes. We adapt multi-task learning for the multimodal subtasks of the MAMI challenge to transfer knowledge among the correlated subtasks. We also leverage on ensemble learning for synergistic integration of models individually trained for the subtasks. We finally discuss about errors of the system to provide useful insights for future work.
NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection

Idioms are lexically-complex phrases whose meaning cannot be derived by compositionally interpreting their components. Although the automatic identification and understanding of idioms is essential for a wide range of Natural Language Understanding tasks, they are still largely under-investigated.This motivated the organization of the SemEval-2022 Task 2, which is divided into two multilingual subtasks: one about idiomaticity detection, and the other about sentence embeddings. In this work, we focus on the first subtask and propose a Transformer-based dual-encoder architecture to compute the semantic similarity between a potentially-idiomatic expression and its context and, based on this, predict idiomaticity. Then, we show how and to what extent Named Entity Recognition can be exploited to reduce the degree of confusion of idiom identification systems and, therefore, improve performance.Our model achieves 92.1 F1 in the one-shot setting and shows strong robustness towards unseen idioms achieving 77.4 F1 in the zero-shot setting. We release our code at https://github.com/Babelscape/ner4id.
DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles

The application of supervised methods to automatic summarization requires the availability of adequate corpora consisting of a set of document-summary pairs. As in most Natural Language Processing tasks, the great majority of available datasets for summarization are in English, making it difficult to develop automatic summarization models for other languages. Although Spanish is gradually forming part of some recent summarization corpora, it is not the same for minority languages such as Catalan.In this work, we describe the construction of a corpus of Catalan and Spanish newspapers, the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA) corpus. It is a high-quality large-scale corpus that can be used to train summarization models for Catalan and Spanish.We have carried out an analysis of the corpus, both in terms of the style of the summaries and the difficulty of the summarization task. In particular, we have used a set of well-known metrics in the summarization field in order to characterize the corpus. Additionally, for benchmarking purposes, we have evaluated the performances of some extractive and abstractive summarization systems on the DACSA corpus.
Logical Story Representations via FrameNet + Semantic Parsing

We propose a means of augmenting FrameNet parsers with a formal logic parser to obtain rich semantic representations of events. These schematic representations of the frame events, which we call Episodic Logic (EL) schemas, abstract constants to variables, preserving their types and relationships to other individuals in the same text. Due to the temporal semantics of the chosen logical formalism, all identified schemas in a text are also assigned temporally bound “episodes” and related to one another in time. The semantic role information from the FrameNet frames is also incorporated into the schema’s type constraints. We describe an implementation of this method using a neural FrameNet parser, and discuss the approach’s possible applications to question answering and open-domain event schema learning.
UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection

Sarcasm has gained notoriety for being difficult to detect by machine learning systems due to its figurative nature. In this paper, Bidirectional Encoder Representations from Transformers (BERT) model has been used with ensemble loss made of cross-entropy loss and negative log-likelihood loss to classify whether a given sentence is in English and Arabic tweets are sarcastic or not. From the results obtained in the experiments, our proposed BERT with ensemble loss achieved superior performance when applied to English and Arabic test datasets. For the validation dataset, our model performed better on the Arabic dataset but failed to outperform the baseline method (made of BERT with only a single loss function) when applied on the English validation set.
Distributional properties of political dogwhistle representations in Swedish BERT

“Dogwhistles” are expressions intended by the speaker have two messages: a socially-unacceptable “in-group” message understood by a subset of listeners, and a benign message intended for the out-group. We take the result of a word-replacement survey of the Swedish population intended to reveal how dogwhistles are understood, and we show that the difficulty of annotating dogwhistles is reflected in the separability in the space of a sentence-transformer Swedish BERT trained on general data.
USST’s System for AutoSimTrans 2022

This paper describes our submitted text-to-text Simultaneous translation (ST) system, which won the second place in the Chinese→English streaming translation task of AutoSimTrans 2022. Our baseline system is a BPE-based Transformer model trained with the PaddlePaddle framework. In our experiments, we employ data synthesis and ensemble approaches to enhance the base model. In order to bridge the gap between general domain and spoken domain, we select in-domain data from general corpus and mixed then with spoken corpus for mixed fine tuning. Finally, we adopt fixed wait-k policy to transfer our full-sentence translation model to simultaneous translation model. Experiments on the development data show that our system outperforms than the baseline system.
A Comprehensive Dataset for German Offensive Language and Conversation Analysis

In this work, we present a new publicly available offensive language dataset of 10.278 German social media comments collected in the first half of 2021 that were annotated by in total six annotators. With twelve different annotation categories, it is far more comprehensive than other datasets, and goes beyond just hate speech detection. The labels aim in particular also at toxicity, criminal relevance and discrimination types of comments.Furthermore, about half of the comments are from coherent parts of conversations, which opens the possibility to consider the comments’ contexts and do conversation analyses in order to research the contagion of offensive language in conversations.
KARAKTERISTIK BIROKRASI LOKAL (Aristokrasi Jabatan Kepala Desa di Desa Sumberejo, Kecamatan Kerjo, Kabupaten Karanganyar)

Topik hari ini yang menjadi menarik adalah munculnya fenomena regenerasi dan pejabat yang akan berada di birokrasi melalui sebuah skenario di mana kepala daerah calon termasuk kepala desa berasal dari darah atau ikatan keluarga dengan pejabat incumbent. Ini adalah cermin dari aristokrasi dalam praktik demokrasi kita. Posisi adalah amanah yang dipercayakan oleh masyarakat kepada mereka yang berkompeten dan benar-benar mampu melaksanakan tanggung jawab secara profesional, untuk menuju posisi terendah dalam pemerintahan desa bahkan sebagai kepala desa, kehadiran karakteristik birokrasi lokal di desa mengarah pada pembentukan dinasti atau tidak atau aristokrasi birokrasi menjadi penting adalah adanya partisipasi masyarakat untuk mengawasi proses pembuktian proses pemilihan desa dianggap memenuhi prosedur atau hanya skenario posisi turunan. Kata Kunci : Birokrasi, Aristokrasi, Office, Kepala Desa. Topic today that becomes interesting is the emergence of the phenomenon of regeneration and officials who would have been in the bureaucracy through a scenario where the prospective head region including the head of the village is derived from the blood or family ties with the incumbent officials. It is a mirror of the aristocracy in our democratic practices. Position is the mandate entrusted by the people to those who are competent and truly capable of carrying out responsibilities in a professional manner, to the lowest position in the village administration even as the village head, the presence of the characteristic of the local bureaucracy in the village leads to the formation of a dynasty or not or bureaucratic aristocracy becomes important is the presence of community participation to oversee the process of proving the election process for the village is considered to meet the procedure or simply a derivative positions scenario. Keywords : Bureaucracy, Aristocracy, Office, Head Village
PSR_2100115 734..750

doi:10.1017/S0003055421001155 © The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercialShareAlike licence (https://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Classification of cellular states for high-speed image-based microfluidic cell sorting

The Steinmetz lab at Stanford has recently developed Image Cell Sorting (ICS) which combines FACS with high-speed laser microscopy and image analysis, enabling high-speed sorting of cells based on microscopy images that are constructed from laser pulses in real time (sorting up to 15,000 images and cells/sec). Coupling this technology with deep learning has the potential to unlock more complex cellular states for image-based sorting. Here, we establish the feasibility of deep learning on ICS cellular imaging by training cell type classifiers on a dataset of ∼ 2 million images from published and unpublished ICS experiments. We process this first-of-its-kind dataset from raw 7-channel laser pulse images, and train multiple ResNet and MobileNetV2 models to classify 36 cell classes. Models were chosen to test a range of model sizes and explore tradeoffs between prediction accuracy and inference time at testing, which is an important variable for fast image-based sorting. We also tested whether pre-training models on natural images would improve performance as seen in many other domains, since ICS cell images differ substantially from natural images. Next, we tested the capacity of different architectures to classify cell types based on fewer laser channels, because 4 of the 7 available lasers require special sample preprocessing to add molecular fluorophores that are not compatible with many applications. Lastly, we applied saliency mapping on test set images to explore the feasibility of biological interpretation of deep learning models in this context.
Bag of Tricks for Faster & Stable Image Classification

In recent years, breakthroughs have brought us closer to our goal of Artificial General Intelligence. GPT-3 [2], one such recent success, took 3640 PF-days to train, and cost over 5M dollars. In an era where we use progressively larger pre-trained models and fine-tune on downstream tasks, an important question that remains to be answered is efficient (re)training of such models; such techniques can not only save compute time and help research progress at a faster rate, but also help reduce our carbon footprint. Accordingly, many papers in top conferences focus on techniques for faster and stable (i.e, better generalization ability) training of architectures. In this paper, we specifically focus on various techniques from recent papers (mainly NeurIPS 2020 & 2021) such as Bandit-based Importance Sampling, Convolutional Normalization, approximate tensor operations and Gradient-based weight initialization, and apply these techniques on an image classification problem. Our results show that using these methods in combination can lead to a speedup of the order of 1.5-2.5 × along with higher validation accuracy with models such as VGG-16 and ResNet-18, and on even small datasets such as CIFAR-100. We provide intuition as to why and when these methods work, and what regimes of hyper-parameters work best for different model architectures. We combine our 4 different proposed methods in a easy to use plug-and-play module (link can be found here), which allows any Py-Torch nn.Module model to be trained with any combination of these methods on a range of datasets.
Different modalities of nasolabial flaps in nasal-defect reconstruction: clinical experience in 40 cases and review of literature

Background Nasal-defect reconstruction is a challenging subject, nasolabial flaps are common methods for nasal reconstruction. Different modalities of nasolabial flaps were identified as V–Y advancement, superiorly or inferiorly based, freestyle perforator based, interpolated flap, and propeller types are commonly used. Objective The objective of the study is to evaluate the different modalities of nasolabial flaps in nasal-defect reconstruction and review of literature about nasal reconstructions with nasolabial flaps defining the advantages and disadvantages for each subunit. Patients and methods The study included 40 patients presented with nasal defects admitted to Benha University Hospital in the period from December 2018 to December 2020. Patients were reconstructed with different nasolabial flap types. Results In total, 40 patients whose nasal defects were reconstructed with nasolabial flaps were included in this study. The most frequent lesion was basal-cell carcinoma (73.3%). The most frequent flap type was superiorly based flap (60.0%), followed by inferiorly based flap (23.3%), and freestyle perforator nasolabial island flap was the least frequent one (16.7). All patients (100.0%) reported good functional outcomes such as normal breathing and no airway obstruction. About half of the patients reported excellent outcomes (53.3%). Partial wound dehiscence in three (7.5%) patients and alar distortion in two (5%) patients. Conclusion V–Y-advancement flap and freestyle perforator-based flap may be preferred for sidewall-and dorsum-defect reconstruction. Two‑stage interpolation type gives the best results for tip-region defects. Propeller and transposition (superior or inferior based) type flaps may be the choice for treatment in alar-region defects.
Effects of fentanyl and dexmedetomidine as adjuvants to bupivacaine in paravertebral block for postoperative analgesia in patients undergoing modified radical mastectomy: A prospective randomised double-blind study

Background and Aims: Paravertebral block (PVB) is an established method, indicated for postoperative analgesia after modified radical mastectomy (MRM). Although many additives to bupivacaine in PVB have been tried to prolong the analgesia in postoperative period, no additive has been found without any adverse effects. We have compared the duration of analgesia in PVB using adjuvants like dexmedetomidine and fentanyl with bupivacaine after MRM. Methods: A total of 60 female patients enroled for MRM were divided into two groups of 30 patients each. Group BF received PVB with 20 ml bupivacaine 0.25% with fentanyl 1 μg/kg and group BD received 20 ml bupivacaine 0.25% with dexmedetomidine 1 μg/kg for PVB. After confirming successful PVB, surgery was done under general anaesthesia. Time for first rescue analgesic request was the primary outcome of the study. The secondary outcome was comparison of visual analogue scale scores for pain and total analgesic consumption. Side effects like sedation, nausea, vomiting, bradycardia and hypotension in the postoperative period till 24 h were also assessed. Results: The time for first rescue analgesic request was 6.32 ± 1.75 h in the BD group contrary to 3.94 ± 2.12 h in group BF (P < 0.05). Total paracetamol consumed as rescue analgesia in the first 24 h of postoperative period was remarkably reduced in group BD (1.7 ± 0.94 gm) in contrary to group BF (2.6 ± 0.98 gm) (P < 0.05). There was no significant difference in the incidence of complications between the groups. Conclusion: Dexmedetomidine provides prolonged postoperative analgesia compared with fentanyl when used as an adjuvant to bupivacaine in PVB after MRM.
F ACTORS I NFLUENCING THE D ISPLAY OF M ULTIPLE D EFENSIVE B EHAVIORS IN E ASTERN I NDIGO S NAKES ( D RYMARCHON COUPERI )

—The ability to display multiple defensive behaviors may increase the chances of an individual avoiding predation. Morphological and physiological condition often influences the display of particular behaviors. Understanding the factors influencing the display of particular behaviors from a suite of potential behaviors can help understand the conditions under which displaying certain suites of defensive behaviors will confer the greatest advantage. Drymarchon couperi (Eastern Indigo Snake) is a large, non-venomous snake that exhibits multiple visual, auditory, olfactory, and physical defensive behaviors. We studied the responses of wild D. couperi to human capture and examined how the number and presence of individual behaviors were related to extrinsic and intrinsic variables using encounters from 84 snakes. Snakes were more likely to flee from the observer at warmer body temperatures and, once captured, exhibited wide variation in defensive behaviors with less costly (i.e., less aggressive) behaviors predominating. Individuals were more likely to display any type of defensive behavior earlier in the field season (i.e., November through January). However, our variables had relatively little influence on the presence of particular defensive behaviors although, for some behaviors, the probability of displaying a behavior increased as the number of other behaviors exhibited increased. Our study shows that D. couperi defensive behavior is quite variable and that the factors contributing variation are unclear. Environmental factors (e.g., distance to retreat site) or individual predispositions may contribute to some of this variation.
Connecting children's quilt literature with the Iowa Core Curriculum social studies standards

The purpose of this qualitative content analysis was to identify themes found in children's quilt picture books that could connect to the Iowa Core Curriculum Social Studies Standards. Elementary teachers could utilize the quilt picture books to teach the elementary social studies curriculum. This researcher analyzed 15 quilt picture books with a copyright of 1994 and newer that were in the Wilson Children's Core Collection. The themes that emerged from the content analysis of children's quilt picture books were generosity, slavery, multicultural, generational, and westward expansion. This researcher found that the books could be used to teach essential skills in the social studies content areas of history, geography, behavioral science, and civic literacy. Children's literature has the ability to impact student learning in a positive way, yet prior research on quilt picture books did connect the books to the social studies standards.
Quasi‐linear SVM classifier with segmented local offsets for imbalanced data classification

Within‐class imbalance problems often occur in imbalanced data classification, which worsen the imbalance distribution problem and increase the learning concept complexity. However, most existing methods for the imbalanced data classification focus on rectifying the between‐class imbalance problem, which is insufficient and inappropriate in many different scenarios. This paper proposes a simple yet effective support vector machine (SVM) classifier with local offset adjustment for imbalance classification problems. First, a geometry‐based partitioning method is modified for imbalanced datasets to divide the input space into multiple linearly separable partitions along the potential separation boundary. Then an F‐score‐based method is applied to obtain local offsets optimized on each local cluster. Finally, by constructing a quasi‐linear kernel based on the partitioning information, a quasi‐linear SVM classifier with local offsets is constructed for the imbalanced datasets. Simulation results on different real‐world datasets show that the proposed method is effective for imbalanced data classifications. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.