Reviewerly/exHarmony huggingface dataset:
The resistance toward COVID-19 contact tracing apps: A study of psychological reactance among young adults in Italy

Western governments’ attempts to encourage young adults to adopt COVID-19 contact tracing apps (CTAs) have been unsuccessful. Drawing on psychological reactance theory, we propose that government-imposed containment measures (e.g., lockdowns, curfews) may cause young adults to resist CTAs. We investigate how and when threats to freedom posed by government-imposed containment measures to young adults reduce their CTA adoption intentions. We conducted a survey of young adults during the second general lockdown (March‒April 2021) in Italy. The results show that when young Italian adults focus on the restrictive nature of government-imposed containment measures, their sense of freedom is threatened. Threats to freedom produce psychological states of either helplessness or reactance, depending on if young Italian adults think they can recover their freedom. Helpless young adults are motivated to adopt CTAs because they seek guidance from containment measures. Reactant young adults resist CTAs because they exhibit aversive psychological states toward containment measures. These results offer relevant insights for policymakers. They shed light on young Italian adults’ resistance toward CTAs. They also inform governments on how to interact more efficiently with young adults if a novel pandemic should occur.
A Secured Data Storage in Cloud Computing by Using Block Design Based Key Agreement

Cloud computing has emerged as a dominant paradigm for storing and accessing data. In group data sharing scenarios, where multiple users collaborate and exchange sensitive information in the cloud, ensuring secure and efficient key agreement becomes crucial. This paper presents a comprehensive analysis of key agreement mechanisms for group data sharing in cloud computing environments. We explore various cryptographic techniques and protocols specifically designed for establishing secure communication channels among group members. The paper discusses the challenges associated with key agreement in the cloud, proposes potential solutions, and provides insights into the implementation and evaluation of such mechanisms. The proposed framework aims to protect data confidentiality, integrity, and availability, ensuring a robust and reliable cloud storage environment. We explore various cryptographic techniques, including encryption, key management, and authentication mechanisms, and discuss their application in securing cloud-based data storage. The framework addresses common security threats and provides guidelines for implementing a secure cloud storage solution.
Роль библиотек в сохранении культурного наследия многонациональных государств

The role of libraries in preserving the cultural heritage of indigenous peoples is significant and multi-faceted. Libraries build up collections of information resources in indigenous languages, carry out extensive research work, generate bibliographic databases, conduct cultural and educational activities, organize exhibi-tions. These activities support the preservation and promotion of the local tangible and intangible cultural heritage, the written and spoken indigenous languages. This is aimed at developing young people’s interest and responsible attitude to the languages and cultures of the indigenous peoples of Russia.
Health monitoring system for Nordic walking

This article discusses an analytical approach to writing user and system requirements for the Nordic Walking Health Monitoring System and describes the tools which allow designing this web-based system.
Transformer based Self-supervised learning for content-based image retrieval

Diversity in training data encompassing variety of patient conditions is a recipe for the success of medical models based on DL. Ensuring diverse patient conditions is often impeded by the necessity to manually identify and include such cases, which is time-consuming and expensive. Here we propose a method of retrieving images similar to a handful of example images based on features learnt using self-supervised learning. We demonstrate the features learnt using SSL on transformer based networks are excellent feature learners which not only eliminates the need for annotation but enable accurate KNN based image retrieval matching the desired patient conditions.
QC of image registration using a DL network trained using only synthetic images

To perform robust quality control of medical imaging registration, we propose a method that can QC co-registration for multiple organs, without the restriction of the modalities. A rule-based image synthesis pipeline is used to generate random contrasts and shapes as training images. ResNet34 is trained to predict image alignment. Two MRI datasets with the spine or brain as subjects are used as external validation sets. The proposed model trained with synthetic images is validated on either one of the real MRI datasets and outperforms the same model trained on the other MRI dataset, which shows better generalizability.
CONVERSATIONAL IMPLICATURE AND FLOUTING MAXIMS IN SHANG-CHI AND THE LEGENDS OF THE TEN RINGS MOVIE

This research intends to discover the conversational implicature, flouting of maxim, and the most dominant maxim flouted. The theory of Grice (1989) was applied to analyse the conversational implicature and cooperative principles. The descriptive qualitative method is used to explain the conversational implicatures and flouting maxim. After analysing the data, we found 26 data of conversational implicature that divided into generalized implicature 14 data and particularized implicature 12 data, while the flouting of maxim is found 26 data, there are 4 data maxim of quantity, 6 data maxim of quality, 7 data maxim of relation, and 9 data maxim of manner that indicates as a flout during the conversation, maxim of manner became the most dominant maxim that flouted with 9 data. From the research, it can be concluded that generalized conversational implicature was appear frequently in the movie than particularized conversational implicature with 12 data, 26 data of flouting maxim was found in the movie with maxim of manner as the most dominant maxim flouted. Conversational generalized implicature happen more in the movie because the speakers believe that the hearer would understand the information without explaining the knowledge of the conversation. Flouting maxim of manner happen because the speakers explaining information unclear and tend to be ambiguous.
Detecting Adversarial Samples through Sharpness of Loss Landscape

Deep neural networks (DNNs) have been proven to be sensitive towards perturbations on input samples, and previous works highlight that adversarial samples are even more vulnerable than normal ones.In this work, this phenomenon is illustrated from the perspective of sharpness via visualizing the input loss landscape of models.We first show that adversarial samples locate in steep and narrow local minima of the loss landscape (high sharpness) while normal samples, which differs distinctly from adversarial ones, reside in the loss surface that is more flatter (low sharpness).Based on this, we propose a simple and effective sharpness-based detector to distinct adversarial samples by maximizing the loss increment within the region where the inference sample is located.Considering that the notion of sharpness of a loss landscape is relative, we further propose an adaptive optimization strategy in an attempt to fairly compare the relative sharpness among different samples.Experimental results show that our approach can outperform previous detection methods by large margins (average +6.6 F1 score) for four advanced attack strategies considered in this paper across three text classification tasks.Our codes are publicly available at https://github.com/ruizheng20/sharpness_detection.
Self-Supervised Sentence Polishing by Adding Engaging Modifiers

Teachers often guide students to improve their essays by adding engaging modifiers to polish the sentences.In this work, we present the first study on automatic sentence polishing by adding modifiers.Since there is no available dataset for the new task, we first automatically construct a large number of parallel data by removing modifiers in the engaging sentences collected from public resources.Then we finetune LongLM (Guan et al., 2022) to reconstruct the original sentences from the corrupted ones.Considering that much overlap between inputs and outputs may bias the model to completely copy the inputs, we split each source sentence into sub-sentences and only require the model to generate the modified sub-sentences.Furthermore, we design a retrieval augmentation algorithm to prompt the model to add suitable modifiers.Automatic and manual evaluation on the auto-constructed test set and real human texts show that our model can generate more engaging sentences with suitable modifiers than strong baselines while keeping fluency.We deploy the model at http://coai.cs.
Guiding Neural Machine Translation with Semantic Kernels

Machine Translation task has made great progress with the help of auto-regressive decoding paradigm and Transformer architecture.In this paradigm, though the encoder can obtain global source representations, the decoder can only use translation history to determine the current word.Previous promising works attempted to address this issue by applying a draft or a fixed-length semantic embedding as targetside global information.However, these methods either degrade model efficiency or show limitations in expressing semantics.Motivated by Functional Equivalence Theory, we extract several semantic kernels from a source sentence, each of which can express one semantic segment of the original sentence.Together, these semantic kernels can capture global semantic information, and we project them into target embedding space to guide target sentence generation.We further force our model to use semantic kernels at each decoding step through an adaptive mask algorithm.Empirical studies on various machine translation benchmarks show that our approach gains approximately an improvement of 1 BLEU score on most benchmarks over the Transformer baseline and about 1.7 times faster than previous works on average at inference time.
Towards Weakly-Supervised Hate Speech Classification Across Datasets

As pointed out by several scholars, current research on hate speech (HS) recognition is characterized by unsystematic data creation strategies and diverging annotation schemata. Subsequently, supervised-learning models tend to generalize poorly to datasets they were not trained on, and the performance of the models trained on datasets labeled using different HS taxonomies cannot be compared. To ease this problem, we propose applying extremely weak supervision that only relies on the class name rather than on class samples from the annotated data. We demonstrate the effectiveness of a state-of-the-art weakly-supervised text classification model in various in-dataset and cross-dataset settings. Furthermore, we conduct an in-depth quantitative and qualitative analysis of the source of poor generalizability of HS classification models.
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations

Discourse analysis is an important task because it models intrinsic semantic structures between sentences in a document.Discourse markers are natural representations of discourse in our daily language.One challenge is that the markers as well as pre-defined and human-labeled discourse relations can be ambiguous when describing the semantics between sentences.We believe that a better approach is to use a contextual-dependent distribution over the markers to express discourse information.In this work, we propose to learn a Distributed Marker Representation (DMR) by utilizing the (potentially) unlimited discourse marker data with a latent discourse sense, thereby bridging markers with sentence pairs.Such representations can be learned automatically from data without supervision, and in turn provide insights into the data itself.Experiments show the SOTA performance of our DMR on the implicit discourse relation recognition task and strong interpretability.Our method also offers a valuable tool to understand complex ambiguity and entanglement among discourse markers and manually defined discourse relations.
Class based Influence Functions for Error Detection

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets.However, they are unstable when applied to deep networks.In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem.We show that IFs are unreliable when the two data points belong to two different classes.Our solution leverages class information to improve the stability of IFs.Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.
Generalizing Backpropagation for Gradient-Based Interpretability

Many popular feature-attribution methods for interpreting deep neural networks rely on computing the gradients of a model's output with respect to its inputs.While these methods can indicate which input features may be important for the model's prediction, they reveal little about the inner workings of the model itself.In this paper, we observe that the gradient computation of a model is a special case of a more general formulation using semirings.This observation allows us to generalize the backpropagation algorithm to efficiently compute other interpretable statistics about the gradient graph of a neural network, such as the highest-weighted path and entropy.We implement this generalized algorithm, evaluate it on synthetic datasets to better understand the statistics it computes, and apply it to study BERT's behavior on the subject-verb number agreement task (SVA).With this method, we (a) validate that the amount of gradient flow through a component of a model reflects its importance to a prediction and (b) for SVA, identify which pathways of the self-attention mechanism are most important.
JudithJeyafreeda at SemEval-2023 Task 10: Machine Learning for Explainable Detection of Online Sexism

The rise of the internet and social media platforms has brought about significant changes in how people interact with each another.For a lot of people, the internet have also become the only source of news and information about the world.Thus due to the increase in accessibility of information, online sexism has also increased.Efforts should be made to make the internet a safe space for everyone, irrespective of gender, both from a larger social norms perspective and legal or technical regulations to help alleviate online gender-based violence.As a part of this, this paper explores simple methods that can be easily deployed to automatically detect online sexism in textual statements.
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning

It has always been an important yet challenging problem to control language models to avoid generating texts with undesirable attributes, such as toxic language and unnatural repetition.We introduce CLICK for controllable text generation, which needs no modification to the model architecture and facilitates out-of-thebox use of trained models.It employs a contrastive loss on sequence likelihood, which fundamentally decreases the generation probability of negative samples (i.e., generations with undesirable attributes).It also adopts a novel likelihood ranking-based strategy to construct contrastive samples from model generations.On the tasks of language detoxification, sentiment steering, and repetition reduction, we show that CLICK outperforms strong baselines of controllable text generation and demonstrate the superiority of CLICK's sample construction strategy. 1
Label Agnostic Pre-training for Zero-shot Text Classification

Conventional approaches to text classification typically assume the existence of a fixed set of predefined labels to which a given text can be classified.However, in real-world applications, there exists an infinite label space for describing a given text.In addition, depending on the aspect (sentiment, topic, etc.) and domain of the text (finance, legal, etc.), the interpretation of the label can vary greatly.This makes the task of text classification, particularly in the zero-shot scenario, extremely challenging.In this paper, we investigate the task of zeroshot text classification with the aim of improving the ability of pre-trained language models (PLMs) to generalize to both seen and unseen data across varying aspects and domains.To solve this we introduce two new simple yet effective pre-training strategies, Implicit and Explicit pre-training.These methods inject aspect-level understanding into the model at train time with the goal of conditioning the model to build task-level understanding.To evaluate this, we construct and release UTCD, a new benchmark dataset for evaluating text classification in zero-shot settings.Experimental results on UTCD show that our approach achieves improved zero-shot generalization on a suite of challenging datasets across an array of zero-shot formalizations.
The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code

Causal reasoning, the ability to identify causeand-effect relationship, is crucial in human thinking.Although large language models (LLMs) succeed in many NLP tasks, it is still challenging for them to conduct complex causal reasoning like abductive reasoning and counterfactual reasoning.Given the fact that programming code may express causal relations more often and explicitly with conditional statements like if, we want to explore whether Code-LLMs acquire better causal reasoning abilities.Our experiments show that compared to textonly LLMs, Code-LLMs with code prompts are significantly better in causal reasoning.We further intervene on the prompts from different aspects, and discover that the programming structure is crucial in code prompt design, while Code-LLMs are robust towards format perturbations.Code and data are available at
Tenzin-Gyatso at SemEval-2023 Task 4: Identifying Human Values behind Arguments Using DeBERTa

Identifying human values behind arguments is a complex task which requires understanding of premise, stance and conclusion together.We propose a method that uses a pre-trained language model, DeBERTa, to tokenize and concatenate the text before feeding it into a fully connected neural network.We also show that leveraging the hierarchy in values improves the performance by .14F1 score compared to only using level 2 values.Our code is made publicly available here. 1
PRAM: An End-to-end Prototype-based Representation Alignment Model for Zero-resource Cross-lingual Named Entity Recognition

Zero-resource cross-lingual named entity recognition (ZRCL-NER) aims to leverage rich labeled source language data to address the NER problem in the zero-resource target language.Existing methods are built either based on data transfer or representation transfer.However, the former usually leads to additional computation costs, and the latter lacks explicit optimization specific to the NER task.To overcome the above limitations, we propose a novel prototype-based representation alignment model (PRAM) for the challenging ZRCL-NER task.PRAM models the crosslingual (CL) NER task and transfers knowledge from source languages to target languages in a unified neural network, and performs end-toend training, avoiding additional computation costs.Moreover, PRAM borrows the CL inference ability of multilingual language models and enhances it with a novel training objectiveattribution-prediction consistency (APC)-for explicitly enforcing the entity-level alignment between entity representations and predictions, as well as that across languages using prototypes as bridges.The experimental results show that PRAM significantly outperforms existing state-of-the-art methods, especially in some challenging scenarios.
Context-aware Medication Event Extraction from Unstructured Text

Accurately capturing medication history is crucial in delivering high-quality medical care.The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives.We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task.Our study evaluates the performance of various pretrained language models in this task.Further, we find that data augmentation coupled with domain-specific training provides notable improvements.With experiments, we also underscore the importance of careful data preprocessing in medical event detection.
RED&lt;sup&gt;FM&lt;/sup&gt;: a Filtered and Multilingual Relation Extraction Dataset

Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge.However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English.In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems.First, we present SRED FM , an automatically annotated dataset covering 18 languages, 400 relation types, 13 entity types, totaling more than 40 million triplet instances.Second, we propose RED FM , a smaller, human-revised dataset for seven languages that allows for the evaluation of multilingual RE systems.To demonstrate the utility of these novel datasets, we experiment with the first end-to-end multilingual RE model, mREBEL, that extracts triplets, including entity types, in multiple languages.We release our resources and model checkpoints at https://www.github.com/babelscape/rebel.
ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models

Knowledge Distillation (KD) (Hinton et al., 2015) is one of the most effective approaches for deploying large-scale pre-trained language models in low-latency environments by transferring the knowledge contained in the largescale models to smaller student models.Previous KD approaches use the soft labels and intermediate activations generated by the teacher to transfer knowledge to the student model parameters alone.In this paper, we show that having access to non-parametric memory in the form of a knowledge base with the teacher's soft labels and predictions can further enhance student capacity and improve generalization.To enable the student to retrieve from the knowledge base effectively, we propose a new Retrieval-augmented KD framework with a loss function that aligns the relational knowledge in teacher and student embedding spaces.We show through extensive experiments that our retrieval mechanism can achieve state-of-the-art performance for taskspecific knowledge distillation on the GLUE benchmark (Wang et al., 2018a).
KnowLab at RadSum23: comparing pre-trained language models in radiology report summarization

This paper presents our contribution to the RadSum23 shared task organized as part of the BioNLP 2023.We compared state-of-theart generative language models in generating high-quality summaries from radiology reports.A two-stage fine-tuning approach was introduced for utilizing knowledge learnt from different datasets.We evaluated the performance of our method using a variety of metrics, including BLEU, ROUGE, Bertscore, CheXbert, and RadGraph.Our results revealed the potentials of different models in summarizing radiology reports and demonstrated the effectiveness of the two-stage fine-tuning approach.We also discussed the limitations and future directions of our work, highlighting the need for better understanding the architecture design's effect and optimal way of fine-tuning accordingly in automatic clinical summarizations.