Reviewerly/exHarmony huggingface dataset:
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment

Word translation without parallel corpora has become feasible, rivaling the performance of supervised methods.Recent findings have shown the improvement in accuracy and robustness of unsupervised word translation (UWT) by utilizing visual observations, which are universal representations across languages.Our work investigates the potential of using not only visual observations but also pretrained language-image models for enabling a more efficient and robust UWT.We develop a novel UWT method dubbed Word Alignment using Language-Image Pretraining (WALIP), leveraging visual observations via the shared imagetext embedding space of CLIPs (Radford et al., 2021).WALIP has a two-step procedure.First, we retrieve word pairs with high confidences of similarity, computed using our proposed imagebased fingerprints, which define the initial pivot for the alignment.Second, we apply our robust Procrustes algorithm to estimate the linear mapping between two embedding spaces, which iteratively corrects and refines the estimated alignment.Our extensive experiments show that WALIP improves upon the state-of-theart performance of bilingual word alignment for a few language pairs across different word embeddings and displays great robustness to the dissimilarity of language pairs or training corpora for two word embeddings.
SimQA: Detecting Simultaneous MT Errors through Word-by-Word Question Answering

Detractors of neural machine translation admit that while its translations are fluent, it sometimes gets key facts wrong.This is particularly important in simultaneous interpretation where translations have to be provided as fast as possible: before a sentence is complete.Yet, evaluations of simultaneous machine translation (SIMULMT) fail to capture if systems correctly translate the most salient elements of a question: people, places, and dates.To address this problem, we introduce a downstream word-by-word question answering evaluation task (SIMQA): given a source language question, translate the question word by word into the target language, and answer as soon as possible.SIMQA jointly measures whether the SIMULMT models translate the question quickly and accurately, and can reveal shortcomings in existing neural systemshallucinating or omitting facts.
Leveraging Data Recasting to Enhance Tabular Reasoning

Creating challenging tabular inference data is essential for learning complex reasoning.Prior work has mostly relied on two data generation strategies.The first is human annotation, which yields linguistically diverse data but is difficult to scale.The second category for creation is synthetic generation, which is scalable and cost effective but lacks inventiveness.In this research, we present a framework for semiautomatically recasting existing tabular data to make use of the benefits of both approaches.We utilize our framework to build tabular NLI instances from five datasets that were initially intended for tasks like table2text creation, tabular Q/A, and semantic parsing.We demonstrate that recasted data could be used as evaluation benchmarks as well as augmentation data to enhance performance on tabular NLI tasks.Furthermore, we investigate the effectiveness of models trained on recasted data in the zero-shot scenario, and analyse trends in performance across different recasted datasets types.
ArabIE: Joint Entity, Relation and Event Extraction for Arabic

Previous work on Arabic information extraction has mainly focused on named entity recognition and very little work has been done on Arabic relation extraction and event recognition.Moreover, modeling Arabic data for such tasks is not straightforward because of the morphological richness and idiosyncrasies of the Arabic language.We propose in this article the first neural joint information extraction system for the Arabic language.
BARLE: Background-Aware Representation Learning for Background Shift Out-of-Distribution Detection

Machine learning models often suffer from a performance drop when they are applied to out-of-distribution (OOD) samples, i.e., those drawn far away from the training data distribution.Existing OOD detection work mostly focuses on identifying semantic-shift OOD samples, e.g., instances from unseen new classes.However, background-shift OOD detection, which identifies samples with domain or style-change, represents a more practical yet challenging task.In this paper, we propose Background-Aware Representation Learning (BARLE) for background-shift OOD detection in NLP.Specifically, we generate semanticspreserving background-shifted pseudo OOD samples from pretrained masked language models.We then contrast the in-distribution (ID) samples with their pseudo OOD counterparts.Unlike prior semantic-shift OOD detection work that often leverages an external text corpus, BARLE only uses ID data, which is more flexible and cost-efficient.In experiments across several text classification tasks, we demonstrate that BARLE is capable of improving background-shift OOD detection performance while maintaining ID classification accuracy.We further investigate the properties of the generated pseudo OOD samples, uncovering the working mechanism of BARLE.
ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture

Youssef Mohamed, Mohamed Abdelfattah, Shyma Alhuwaider, Feifan Li, Xiangliang Zhang, Kenneth Church, Mohamed Elhoseiny. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge

Understanding rich narratives, such as dialogues and stories, often requires natural language processing systems to access relevant knowledge from commonsense knowledge graphs.However, these systems typically retrieve facts from KGs using simple heuristics that disregard the complex challenges of identifying situationally-relevant commonsense knowledge (e.g., contextualization, implicitness, ambiguity).In this work, we propose the new task of commonsense fact linking, where models are given contexts and trained to identify situationallyrelevant commonsense knowledge from KGs.Our novel benchmark, ComFact, contains ∼293k in-context relevance annotations for commonsense triplets across four stylistically diverse dialogue and storytelling datasets.Experimental results confirm that heuristic fact linking approaches are imprecise knowledge extractors.Learned fact linking models demonstrate across-the-board performance improvements (∼34.6%F1) over these heuristics.Furthermore, improved knowledge retrieval yielded average downstream improvements of 9.8% for a dialogue response generation task.However, fact linking models still significantly underperform humans, suggesting our benchmark is a promising testbed for research in commonsense augmentation of NLP systems. 1 " : Jamie was sleeping at a friend's house. " : It was her first time away at a friend's house. : Jamie was scared and missed her home and family.
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling

Large pre-trained language models (LMs) have been widely adopted in biomedical and clinical domains, introducing many powerful LMs such as bio-lm and BioELECTRA.However, the applicability of these methods to real clinical use cases is hindered, due to the limitation of pre-trained LMs in processing long textual data with thousands of words, which is a common length for a clinical note.In this work, we explore long-range adaptation from such LMs with Longformer, allowing the LMs to capture longer clinical notes context.We conduct experiments on three n2c2 challenges datasets and a longitudinal clinical dataset from Hong Kong Hospital Authority electronic health record (EHR) system to show the effectiveness and generalizability of this concept, achieving 10% F1-score improvement.Based on our experiments, we conclude that capturing a longer clinical note interval is beneficial to the model performance, but there are different cut-off intervals to achieve the optimal performance for different target variables.Our code is available at https://github.com/HLTCHKUST/long-biomedical-model.
CC-Top: Constrained Clustering for Dynamic Topic Discovery

Research on multi-class text classification of short texts mainly focuses on supervised (transfer) learning approaches, requiring a finite set of pre-defined classes which is constant over time.This work explores deep constrained clustering (CC) as an alternative to supervised learning approaches in a setting with a dynamically changing number of classes, a task we introduce as dynamic topic discovery (DTD).We do so by using pairwise similarity constraints instead of instance-level class labels which allow for a flexible number of classes while exhibiting a competitive performance compared to supervised approaches.First, we substantiate this through a series of experiments and show that CC algorithms exhibit a predictive performance similar to state-of-the-art supervised learning algorithms while requiring less annotation effort.Second, we demonstrate the overclustering capabilities of deep CC for detecting topics in short text data sets in the absence of the ground truth class cardinality during model training.Third, we showcase how these capabilities can be leveraged for the DTD setting as a step towards dynamic learning over time.Finally, we release our codebase to nurture further research in this area.
Data-Efficient Concept Extraction from Pre-trained Language Models for Commonsense Explanation Generation

Predicting the key explanation concept is essential for generating commonsense explanations.This paper introduces a method to predict the concept from pre-trained language models for commonsense explanation generation.Our experiment found that adopting a language model as the concept extractor and fine-tuning it with 20% training data can improve the quality and accuracy of the generated explanations over multiple evaluation metrics.Compared with conventional methods that search concepts over knowledge graphs, our method does not require the preparation and training models to search through knowledge graphs.To better understand the results from pre-trained language models, we also designed a metric to evaluate the retrieved concepts.Through analysis and experiments, we show the correlation between this metric and the performance of the generators, and we also show the importance of attaching concepts for generating high-quality sentences.
Semi-supervised New Slot Discovery with Incremental Clustering

Discovering new slots is critical to the success of dialogue systems.Most existing methods rely on automatic slot induction in an unsupervised fashion or perform domain adaptation across zero or few-shot scenarios.They have difficulties in providing high-quality supervised signals to learn clustering-friendly features, and are limited in effectively transferring the prior knowledge from known slots to new slots.In this work, we propose a Semi-supervised Incremental Clustering method (SIC), to discover new slots with the aid of existing linguistic annotation models and limited known slot data.Specifically, we harvest slot value candidates with NLP model cues and innovatively formulate the slot discovery task under an incremental clustering framework.The model gradually calibrates slot representations under the supervision of generated pseudo-labels, and automatically learns to terminate when no more salient slot remains.Our thorough evaluation on five public datasets demonstrates that the proposed method significantly outperforms state-of-theart models.
A Hybrid Knowledge and Transformer-Based Model for Event Detection with Automatic Self-Attention Threshold, Layer and Head Selection

Event and argument role detection are frequently conceived as separate tasks.In this work we conceive both processes as one task in a hybrid event detection approach.Its main component is based on automatic keyword extraction (AKE) using the self-attention mechanism of a BERT transformer model.As a bottleneck for AKE is defining the threshold of the attention values, we propose a novel method for automatic self-attention threshold selection.It is fueled by core event information, or simply the verb and its arguments as the backbone of an event.These are outputted by a knowledge-based syntactic parser.In a second step the event core is enriched with other semantically salient words provided by the transformer model.Furthermore, we propose an automatic self-attention layer and head selection mechanism, by analyzing which self-attention cells in the BERT transformer contribute most to the hybrid event detection and which linguistic tasks they represent.This approach was integrated in a pipeline event extraction approach and outperforms three state of the art multi-task event extraction methods.
Further analysis of the statistical independence of the NIST SP 800-22 randomness tests

In multiple applications, from Statistics to Particle Physics and notably in Cryptography and Computer Security, it is necessary to obtain long sequences of random numbers. In order to verify the properties of these sequences, different statistical tests are commonly applied, which are usually included in the so-called test batteries or test suites. The batteries need to be both effective and efficient. Their effectiveness relates to how well they can spot non-randomness behaviour, the efficiency is related to the computational time they require. It is therefore essential for tests included in batteries to measure their independence features: Test independence is important for good effectiveness, as high correlations between tests could lead to a decreased efficiency (testing for the same features multiple times) and effectiveness (missing an opportunity to test for an orthogonal randomness property when we essentially measure the same twice). Moreover, the related study of test coverage is often based on the assumption that tests are independent. This paper describes a series of experiments aimed at scrutinizing dependencies among the statistical tests in the NIST SP 800-22 suite. In order to do so, sequences of varying lengths from sources of varying entropy have been generated and tested. Afterwards, an inferential study was carried out to find whether significant correlations exist and to present our findings in a statistically sound way.
Traffic Sign Recognition Using Deep Learning Neural Network and Spatial Transformer

Recently, numerous applications of Intelligent Transportation Systems (ITS) have gained increasingly more focus. One of the most crucial functions of ITS is Traffic Signs Detection and Recognition (TSDR) by notifying drivers of the status of road signs and providing helpful information regarding safety procedures which helps to improve safety. A deep learning system is designed that is a combination of Convolution Neural Network(CNN) and Spatial Transformer Network(STN) to obtain maximum accuracy. AlexNet (Model-A) and BaselineNet (Model-B) are the two CNN- architecture-based models and the two sets of datasets used in the study are Belgian Traffic Sign Classification(BTSC) and German Traffic Sign Recognition Benchmark(GTSRB). To test the models in rainy, foggy, extreme sunlight, and low-light conditions augmenters like gaussian-blur, fog, snow, spatter, and contrast have been applied to the images. Further, a comparison is done between the model performance parameters after training both datasets.
Underwater Photography Noise cancellation Using Artificial Intelligence and Deep Learning

Cameras submerged underwater are widely used to see the ocean floor. Unmanned underwater automation, autonomous underwater vehicles, and in situ ocean sensors are common places to find them (AUVs). While being an essential sensor for keeping track of underwater landscapes, recent underwater camera sensors have several issues. Because of how light moves through water and the biological activity at the seafloor, underwater photographs are typically cluttered with scatters and noise. Over the past five years, a variety of tactics have been developed to Important facts of oceanographic study include image processing and underwater sensing. One prior challenge is light absorption with a scattering effect, which reduces the image quality in underwater conditions with respect to its ground truth.
Multiple Image Tampering Detection using Deep Learning Algorithm

Image manipulation has become a widespread problem with digital photos in recent years. The detection of copy-move picture forgeries, image splicing, and recoloring are the three basic types of image forgeries that are mentioned. The dataset for detection of copy-move images is MICC-220 which involves 220 images with varying lighting conditions and camerasettings. In this paper, Scale-invariant feature transform, DBSCAN algorithm for copy-move image detection, and a deep architecture of a convolutional neural network are some of the approaches and models used to identify recolored images. The dataset used for detection of imagesplicing is CASIA V2 dataset, which contains 4795 photos, to classify altered images and detect different types of image tampering. In addition, image Error Level Analysis, an image compression techniquesareinvolved, along with the convolutional neural network to identify the altered images.
Comparison of Hybrid Novel Pearson Correlation Coefficient (HNPCC) with K-Nearest Neighbor (KNN) Model to Improve Accuracy for Movie Recommendation System

A hybrid recommendation model based on the HNPCC and the K-Nearest Neighbor (KNN) model were evaluated to increase movie recommendation accuracy. The information gathered from the movielens dataset, which contains 23 attributes and with 30 samples, for use in a hybrid movie recommendation system. The sample size for each set is 30 people, and pre-test power is 0.8.Using an independent t-test to decide statistical significance with p<0.05, it was found that HNPCC has a slightly higher accuracy of 94.3% significantly, while KNN has a lower accuracy of 92.9%.As a result of the comparison, the HNPCC outperforms the KNN in terms of enhanced accuracy.
Hand Gesture Recognition of an Individual using Model based Clustering Technique to obtain Better Accuracy Compared with Support Vector Machine

The purpose of this study is to demonstrate the accuracy of a new hand gesture recognition system based on Model Based Clustering and to compare it with a Support Vector Machine (SVM). Materials and Methods:A total of forty unique pictures of hand gestures were extracted from a dataset that was hosted on Github and made available to the public. Two groups are included in this research work. (N = 20) Model-based clustering method and (N = 20) SVM. The sample size is calculated with G power of 80 % and significance p < 0.05. Results: The innovative Model Based Clustering technique has achieved 96.66 % of accuracy and 0.46066 standard deviation while SVM has achieved 90.79 % of accuracy and 0.01861 standard deviation. The Model Based Clustering algorithm has significant accuracy of 0.000 (p < 0.05). Conclusion: The performance of the hand gesture recognition using clustering technique has provided better accuracy and reduced time complexity than Support Vector Machine (SVM).
Blockchain Assisted Archimedes Optimization with Machine Learning Driven Drug Supply Management for Pharmaceutical Sector

Drug supply chain management (DSCM) is a vital procedure for pharmaceutical industries as it makes sure that medicines are delivered securely and effectively to patients. Effectual DSCM comprises the coordination and management of many key actions like warehousing, drug manufacturing, transportation, and distribution. For improving DSCM, pharmaceutical companies leverage technologies such as Blockchain (BC), Internet of Things (IoT), and artificial intelligence (AI) for enhancing traceability, increasing inventory management, and increasing communication and collaboration between stakeholders. Therefore, this study presents a Blockchain Assisted Archimedes Optimization with Machine Learning Driven Drug Supply Management (BAOML-DSM) technique for Pharmaceutical Sector. The presented BAOML-DSM technique focuses on the recommendation of drugs in the pharmaceutical sector. To accomplish this, the BAOML-DSM technique exploits the Hyperledger fabric for DSC management that allows to achieving of tracking procedures in the smart pharmaceutical industry. At the initial stage, preprocessing and Glove based word embedding process takes place. In addition, the gradient boosting decision tree (GBDT) model is employed and is applied for drug recommendation. Moreover, the Archimedes Optimization Algorithm (AOA) is employed for the hyperparameter tuning process which helps in optimal parameter tuning of the GBDT model. The experimental result analysis of the presented BAOML-DSM algorithm takes place using a benchmark dataset. The comprehensive results of the BAOML-DSM technique ensured the improved performance of the BAOML-DSM technique in terms of several metrics.
Prediction of Human Activity Recognition Using Convolution Neural Network Algorithm in Comparison with Grid Search Algorithm

The objective of this piece is to find human activity recognition not through the use of grid search but rather through the application of convolution neural network algorithms. The calculation is carried out by utilising G-power 0.8 with alpha, and the confidence interval is established at 95%. Fifty people will serve as the sample size for the algorithm that uses convolution neural networks to make predictions about human activity recognition (Group 1 equals twenty-five, and Group 2 equals twenty-five). In comparison, the accuracy that can be achieved through grid search is 89.6012, while the accuracy that can be achieved through the Novel Convolution Neural Network is 98.6512. The performance of the Novel Convolution Neural Network is noticeably superior to that of grid search because it incorporates the accuracy of both methods into a single solution.
An Outsourced Access Management System for Secure and Verifiable Fog-Cloud Computing

In this study, we provide HPCS, a clinical decision support system architecture that utilises a mixture of public and private cloud resources. A fog server in "HPCS" employs a minimal data mining technique to keep constant, secure tabs on a patient's health status. Once abnormal symptoms have been discovered, they may be safely sent to a server in the cloud, where they can be predicted with great accuracy. We create a unique secure outsourced inner-product protocol to produce a one-layer neural network on fog servers securely. Any activation function in a multilayer neural network may be safely implemented on a cloud server without compromising the user's privacy using a piecewise polynomial computing technique. One unique protocol, the "privacy-preserving fraction approximation protocol," is aimed at solving the problem of computational overload. We use simulations to demonstrate that the "HPCS" strikes an ideal balance between real-time and highly accurate prediction, therefore achieving the aim of monitoring the health state of patients without compromising their privacy to other parties.