Reviewerly/exHarmony huggingface dataset:
Data and Model Dependencies of Membership Inference Attack

Machine learning (ML) models have been shown to be vulnerable to Membership Inference Attacks (MIA), which infer the membership of a given data point in the target dataset by observing the prediction output of the ML model. While the key factors for the success of MIA have not yet been fully understood, existing defense mechanisms such as using L2 regularization \cite{10shokri2017membership} and dropout layers \cite{salem2018ml} take only the model's overfitting property into consideration. In this paper, we provide an empirical analysis of the impact of both the data and ML model properties on the vulnerability of ML techniques to MIA. Our results reveal the relationship between MIA accuracy and properties of the dataset and training model in use. In particular, we show that the size of shadow dataset, the class and feature balance and the entropy of the target dataset, the configurations and fairness of the training model are the most influential factors. Based on those experimental findings, we conclude that along with model overfitting, multiple properties jointly contribute to MIA success instead of any single property. Building on our experimental findings, we propose using those data and model properties as regularizers to protect ML models against MIA. Our results show that the proposed defense mechanisms can reduce the MIA accuracy by up to 25\% without sacrificing the ML model prediction utility.
Historical Document Processing: Historical Document Processing: A Survey of Techniques, Tools, and Trends

Historical Document Processing is the process of digitizing written material from the past for future use by historians and other scholars. It incorporates algorithms and software tools from various subfields of computer science, including computer vision, document analysis and recognition, natural language processing, and machine learning, to convert images of ancient manuscripts, letters, diaries, and early printed texts automatically into a digital format usable in data mining and information retrieval systems. Within the past twenty years, as libraries, museums, and other cultural heritage institutions have scanned an increasing volume of their historical document archives, the need to transcribe the full text from these collections has become acute. Since Historical Document Processing encompasses multiple sub-domains of computer science, knowledge relevant to its purpose is scattered across numerous journals and conference proceedings. This paper surveys the major phases of, standard algorithms, tools, and datasets in the field of Historical Document Processing, discusses the results of a literature review, and finally suggests directions for further research.
Flexible non-parametric regression models for compositional data

Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression models can provide a convenient alternative method for prediction. To this end, we consider an extension to the classical $k$--$NN$ regression, termed $\alpha$--$k$--$NN$ regression, that yields a highly flexible non-parametric regression model for compositional data through the use of the $\alpha$-transformation. Unlike many of the recommended regression models for compositional data, zeros values (which commonly occur in practice) are not problematic and they can be incorporated into the proposed models without modification. Extensive simulation studies and real-life data analyses highlight the advantage of using these non-parametric regressions for complex relationships between the compositional response data and Euclidean predictor variables. Both suggest that $\alpha$--$k$--$NN$ regression can lead to more accurate predictions compared to current regression models which assume a, sometimes restrictive, parametric relationship with the predictor variables. In addition, the $\alpha$--$k$--$NN$ regression, in contrast to current regression techniques, enjoys a high computational efficiency rendering it highly attractive for use with large scale, massive, or big data.
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

We prove that two-layer (Leaky)ReLU networks initialized by e.g. the widely used method proposed by He et al. (2015) and trained using gradient descent on a least-squares loss are not universally consistent. Specifically, we describe a large class of one-dimensional data-generating distributions for which, with high probability, gradient descent only finds a bad local minimum of the optimization landscape, since it is unable to move the biases far away from their initialization at zero. It turns out that in these cases, the found network essentially performs linear regression even if the target function is non-linear. We further provide numerical evidence that this happens in practical situations, for some multi-dimensional distributions and that stochastic gradient descent exhibits similar behavior. We also provide empirical results on how the choice of initialization and optimizer can influence this behavior.
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception

In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present Deep SESR, a residual-in-residual network-based generative model that can learn to restore perceptual image qualities at 2x, 3x, or 4x higher spatial resolution. We supervise its training by formulating a multi-modal objective function that addresses the chrominance-specific underwater color degradation, lack of image sharpness, and loss in high-level feature representation. It is also supervised to learn salient foreground regions in the image, which in turn guides the network to learn global contrast enhancement. We design an end-to-end training pipeline to jointly learn the saliency prediction and SESR on a shared hierarchical feature space for fast inference. Moreover, we present UFO-120, the first dataset to facilitate large-scale SESR learning; it contains over 1500 training samples and a benchmark test set of 120 samples. By thorough experimental evaluation on the UFO-120 and other standard datasets, we demonstrate that Deep SESR outperforms the existing solutions for underwater image enhancement and super-resolution. We also validate its generalization performance on several test cases that include underwater images with diverse spectral and spatial degradation levels, and also terrestrial images with unseen natural objects. Lastly, we analyze its computational feasibility for single-board deployments and demonstrate its operational benefits for visually-guided underwater robots. The model and dataset information will be available at: https://github.com/xahidbuffon/Deep-SESR.
Design Factors to Improve the Consistency and Sustainable User Experience of Responsive Interface Design

Computers have been extended to a variety of devices, such as smart phones, tablets, and smart watches, thereby increasing the importance of responsive interfaces across multi-terminal devices. To ensure a consistent and sustainable user experience for websites and software products, it is important to study the layout, design elements, and users’ visual perception of different terminal interfaces. In this paper, the multi-terminal interfaces of 40 existing responsive websites were studied in a hierarchical grouping experiment, and six typical interface layouts were classified and extracted. Then, the main design factors affecting interface consistency of the responsive website were extracted and classified through eye tracking and a questionnaire survey. Finally, taking a sales management software tool (SA) as an example for design application, we successfully created responsive interfaces across multi-terminal devices with a consistent and sustainable experience.
A Study on Financial Feasibility of Cloud Kitchen Firms in Hyderabad Region in Telangana, India

Cloud kitchens are commercial kitchens that prepare food only for delivery purpose and do not provide dine-in facility for customers. In the cloud kitchen model, a brand owns or rents a space where its chefs work and uses its own or third-party order and delivery systems. It may also provide a takeaway service where customers can wait to collect their food. The cost incurred to establish a cloud kitchen is much lesser than conventional restaurant and can be situated within a small area and it has less operational costs due to which cloud kitchens are more profitable than normal restaurants. In this perspective, the study is conducted to analyze the financial viability of cloud kitchen firms. Hyderabad city was preferred as the study area. For the analysis, data was collected through personal interviews from the selected cloud kitchen firms with the help of structured questionnaire. Data regarding the establishment and operational costs of cloud kitchen firms are taken from cloud kitchen. The analytical tools NPV and B: C ratios are used to know the financial feasibility of the cloud kitchen firms.
LONG-SHORT TERM MEMORY (LSTM) FOR PREDICTING VELOCITY AND DIRECTION SEA SURFACE CURRENT ON BALI STRAIT

The strategic role of the Bali Strait as a connection between the islands of Java and Bali is growing in line with the increase in the economy and tourism of the two islands. Therefore, it is necessary to have a further understanding of the condition of the waters in the Bali strait, one of which is ocean currents. This study aims to predict future ocean currents based on 30-minute data in the Bali Strait in the range of 16 May 2021 to 9 June 2021 obtained from the Perak II Surabaya Maritime Meteorological Station. In this study, the Long Short Term Memory method was used. The parameters used are hidden layer, batch size, and learn rate drop. Based on the parameters used, the results showed that the smallest MAPE value was 18.64% for U ocean current velocity data and 5.29% for V ocean current velocity data.
IDANI: Inference-time Domain Adaptation via Neuron-level Interventions

Large pre-trained models are usually fine-tuned on downstream task data, and tested on unseen data.When the train and test data come from different domains, the model is likely to struggle, as it is not adapted to the test domain.We propose a new approach for domain adaptation (DA), using neuron-level interventions: We modify the representation of each test example in specific neurons, resulting in a counterfactual example from the source domain, which the model is more familiar with.The modified example is then fed back into the model.While most other DA methods are applied during training time, ours is applied during inference only, making it more efficient and applicable.Our experiments show that our method improves performance on unseen domains.
Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task

Automatic grouping of textual answers has the potential of allowing batch grading, but is challenging because the answers, especially longer essays, have many claims.To explore the feasibility of grouping together answers based on their semantic meaning, this paper investigates the grouping of short textual answers, proxies of single claims.This is approached as a paraphrase identification task, where neural and non-neural sentence embeddings and a paraphrase identification model are tested.These methods are evaluated on a dataset consisting of over 4000 short textual answers from various disciplines.The results map out the suitable question types for the paraphrase identification model and those for the neural and non-neural methods.
DOCmT5: Document-Level Pretraining of Multilingual Language Models

In this paper, we introduce DOCmT5, a multilingual sequence-to-sequence language model pretrained with large scale parallel documents.While previous approaches have focused on leveraging sentence-level parallel data, we try to build a general-purpose pretrained model that can understand and generate long documents.We propose a simple and effective pretraining objective -Document reordering Machine Translation (DrMT), in which the input documents that are shuffled and masked need to be translated.DrMT brings consistent improvements over strong baselines on a variety of document-level generation tasks, including over 12 BLEU points for seen-languagepair document-level MT, over 7 BLEU points for unseen-language-pair document-level MT and over 3 ROUGE-1 points for seen-languagepair cross-lingual summarization.We achieve state-of-the-art (SOTA) on WMT20 De-En and IWSLT15 Zh-En document translation tasks.We also conduct extensive analysis on various factors for document pretraining, including (1) the effects of pretraining data quality and (2) the effects of combining mono-lingual and crosslingual pretraining.We plan to make our model checkpoints publicly available.
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation

Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference.The chosen set is typically determined by separately trained alignment model parameters, independent of the sourcesentence context at inference time.While vocabulary selection appears competitive with respect to automatic quality metrics in prior work, we show that it can fail to select the right set of output words, particularly for semantically non-compositional linguistic phenomena such as idiomatic expressions, leading to reduced translation quality as perceived by humans.Trading off latency for quality by increasing the size of the allowed set is often not an option in real-world scenarios.We propose a model of vocabulary selection, integrated into the neural translation model, that predicts the set of allowed output words from contextualized encoder representations.This restores translation quality of an unconstrained system, as measured by human evaluations on WMT newstest2020 and idiomatic expressions, at an inference latency competitive with alignment-based selection using aggressive thresholds, thereby removing the dependency on separately trained alignment models.
Quantifying Language Variation Acoustically with Few Resources

Deep acoustic models represent linguistic information based on massive amounts of data.Unfortunately, for regional languages and dialects such resources are mostly not available.However, deep acoustic models might have learned linguistic information that transfers to lowresource languages.In this study, we evaluate whether this is the case through the task of distinguishing low-resource (Dutch) regional varieties.By extracting embeddings from the hidden layers of various wav2vec 2.0 models (including new models which are pre-trained and/or fine-tuned on Dutch) and using dynamic time warping, we compute pairwise pronunciation differences averaged over 10 words for over 100 individual dialects from four (regional) languages.We then cluster the resulting difference matrix in four groups and compare these to a gold standard, and a partitioning on the basis of comparing phonetic transcriptions.Our results show that acoustic models outperform the (traditional) transcription-based approach without requiring phonetic transcriptions, with the best performance achieved by the multilingual XLSR-53 model fine-tuned on Dutch.On the basis of only six seconds of speech, the resulting clustering closely matches the gold standard.
One Size Does Not Fit All: The Case for Personalised Word Complexity Models

Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand.It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling.However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience.In this paper, we show that personal models are best when predicting word complexity for individual readers.We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research. 1
Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution

As using they/them as personal pronouns becomes increasingly common in English, it is important that coreference resolution systems work as well for individuals who use personal "they" as they do for those who use gendered personal pronouns.We introduce a new benchmark for coreference resolution systems which evaluates singular personal "they" recognition.Using these WinoNB schemas, we evaluate a number of publicly available coreference resolution systems and confirm their bias toward resolving "they" pronouns as plural.
What changed? Investigating Debiasing Methods using Causal Mediation Analysis

Previous work has examined how debiasing language models affect downstream tasks, specifically, how debiasing techniques influence task performance and whether debiased models also make impartial predictions in downstream tasks or not.However, what we don't understand well yet is why debiasing methods have varying impacts on downstream tasks and how debiasing techniques affect internal components of language models, i.e., neurons, layers, and attentions.In this paper, we decompose the internal mechanisms of debiasing language models with respect to gender by applying causal mediation analysis to understand the influence of debiasing methods on toxicity detection as a downstream task.Our findings suggest a need to test the effectiveness of debiasing methods with different bias metrics, and to focus on changes in the behavior of certain components of the models, e.g.,first two layers of language models, and attention heads.
Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks

Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today's NLU models still struggle to capture their semantics.We rely on Generalized Quantifier Theory for languageindependent representations of the semantics of quantifier words, to quantify their contribution to the errors of NLU models.We find that quantifiers are pervasive in NLU benchmarks, and their occurrence at test time is associated with performance drops.Multilingual models also exhibit unsatisfying quantifier reasoning abilities, but not necessarily worse for non-English languages.To facilitate directlytargeted probing, we present an adversarial generalized quantifier NLI task (GQNLI) and show that pre-trained language models have a clear lack of robustness in generalized quantifier reasoning.
How Universal is Metonymy? Results from a Large-Scale Multilingual Analysis

Temuulen Khishigsuren, Gábor Bella, Thomas Brochhagen, Daariimaa Marav, Fausto Giunchiglia, Khuyagbaatar Batsuren. Proceedings of the 4th Workshop on Research in Computational Linguistic Typology and Multilingual NLP. 2022.
GraDA: Graph Generative Data Augmentation for Commonsense Reasoning

Recent advances in commonsense reasoning have been fueled by the availability of largescale human annotated datasets.Manual annotation of such datasets, many of which are based on existing knowledge bases, is expensive and not scalable.Moreover, it is challenging to build augmentation data for commonsense reasoning because the synthetic questions need to adhere to real-world scenarios.Hence, we present GRADA, a graph-generative data augmentation framework to synthesize factual data samples from knowledge graphs for commonsense reasoning datasets.First, we train a graph-to-text model for conditional generation of questions from graph entities and relations.Then, we train a generator with GAN loss to generate distractors for synthetic questions.Our approach improves performance for SocialIQA, CODAH, HellaSwag and Common-senseQA, and works well for generative tasks like ProtoQA.We show improvement in robustness to semantic adversaries after training with GRADA and provide human evaluation of the quality of synthetic datasets in terms of factuality and answerability.Our work provides evidence and encourages future research into graph-based generative data augmentation. 1
ISCAS at SemEval-2022 Task 10: An Extraction-Validation Pipeline for Structured Sentiment Analysis

ISCAS participated in both sub-tasks in SemEval-2022 Task 10: Structured Sentiment competition.We design an extractionvalidation pipeline architecture to tackle both monolingual and cross-lingual sub-tasks.Experimental results show the multilingual effectiveness and cross-lingual robustness of our system.Our system is openly released on: https://github.com/luxinyu1/SemEval2022-Task10/.
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models

We investigate the extent to which pre-trained language models acquire analytical and deductive logical reasoning capabilities as a side effect of learning word prediction.We present AnaLog, a natural language inference task designed to probe models for these capabilities, controlling for different invalid heuristics the models may adopt instead of learning the desired generalisations.We test four language models on AnaLog, finding that they have all learned, to a different extent, to encode information that is predictive of entailment beyond shallow heuristics such as lexical overlap and grammaticality.We closely analyse the best performing language model and show that while it performs more consistently than other language models across logical connectives and reasoning domains, it still is sensitive to lexical and syntactic variations in the realisation of logical statements. 2 Related Work 2.1 Learning Logic from Text Recent work has explored which aspects of logical reasoning are statistically learnable from text.Examining how well LMs encode the semantics of
Representation Learning for Conversational Data using Discourse Mutual Information Maximization

Although many pretrained models exist for text or images, there have been relatively fewer attempts to train representations specifically for dialog understanding.Prior works usually relied on finetuned representations based on generic text representation models like BERT or GPT-2.But such language modeling pretraining objectives do not take the structural information of conversational text into consideration.Although generative dialog models can learn structural features too, we argue that the structure-unaware word-by-word generation is not suitable for effective conversation modeling.We empirically demonstrate that such representations do not perform consistently across various dialog understanding tasks.Hence, we propose a structure-aware Mutual Information based loss-function DMI (Discourse Mutual Information) for training dialog-representation models, that additionally captures the inherent uncertainty in response prediction.Extensive evaluation on nine diverse dialog modeling tasks shows that our proposed DMI-based models outperform strong baselines by significant margins.