Reviewerly/exHarmony huggingface dataset:
Enhancing Marketing Provision through Increased Online Safety That Imbues Consumer Confidence: Coupling AI and ML with the AIDA Model

To enhance the effectiveness of artificial intelligence (AI) and machine learning (ML) in online retail operations and avoid succumbing to digital myopia, marketers need to be aware of the different approaches to utilizing AI/ML in terms of the information they make available to appropriate groups of consumers. This can be viewed as utilizing AI/ML to improve the customer journey experience. Reflecting on this, the main question to be addressed is: how can retailers utilize big data through the implementation of AI/ML to improve the efficiency of their marketing operations so that customers feel safe buying online? To answer this question, we conducted a systematic literature review and posed several subquestions that resulted in insights into why marketers need to pay specific attention to AI/ML capability. We explain how different AI/ML tools/functionalities can be related to different stages of the AIDA (Awareness, Interest, Desire, and Action) model, which in turn helps retailers to recognize potential opportunities as well as increase consumer confidence. We outline how digital myopia can be reduced by focusing on human inputs. Although challenges still exist, it is clear that retailers need to identify the boundaries in terms of AI/ML’s ability to enhance the company’s business model.
Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror

As the elderly population increases globally, the demand for systems and algorithms that target the elderly is increasing. Focusing on the extendibility of smart mirrors, our purpose is to create a motion detection system based on video input by an attached device (an RGB camera). The motion detection system presented in this paper is based on an algorithm that returns a Boolean value indicating the detection of motion based on skeletal information. We analyzed the problems that occur when the adjacent frame subtraction method (AFSM) is used in the motion detection algorithm based on the skeleton-related output of the pose estimation model. We compared and tested the motion recognition rate for slow-motion with the previously used AFSM and the vector sum method (VSM) proposed in this paper. As an experimental result, the slow-motion detection rate showed an increase of 30–70%.
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction

Although most studies have treated attribute value extraction (AVE) as named entity recognition, these approaches are not practical in real-world e-commerce platforms because they perform poorly, and require canonicalization of extracted values.Furthermore, since values needed for actual services is static in many attributes, extraction of new values is not always necessary.Given the above, we formalize AVE as extreme multi-label classification (XMC).A major problem in solving AVE as XMC is that the distribution between positive and negative labels for products is heavily imbalanced.To mitigate the negative impact derived from such biased distribution, we propose label masking, a simple and effective method to reduce the number of negative labels in training.We exploit attribute taxonomy designed for ecommerce platforms to determine which labels are negative for products.Experimental results using a dataset collected from a Japanese ecommerce platform demonstrate that the label masking improves micro and macro F 1 scores by 3.38 and 23.20 points, respectively.
Sphynx: A Deep Neural Network Design for Private Inference

Private inference (PI) methods involving deep networks enable cryptographically secure machine learning but incur severe latency costs. We outline how neural architecture searches are used to design networks with fewer nonlinearities, enabling efficient PI.
Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Reading is integral to everyday life, and yet learning to read is a struggle for many young learners.During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improve retention.Historically such questions were written by skilled teachers, but recently language models have been used to generate comprehension questions.However, many existing Question Generation (QG) systems focus on generating literal questions from the text, and have no way to control the type of the generated question.In this paper, we study QG for reading comprehension where inferential questions are critical and extractive techniques cannot be used.We propose a two-step model (HTA-WTA) that takes advantage of previous datasets, and can generate questions for a specific targeted comprehension skill.We propose a new reading comprehension dataset that contains questions annotated with story-based reading comprehension skills (SBRCS), allowing for a more complete reader assessment.Across several experiments, our results show that HTA-WTA outperforms multiple strong baselines on this new dataset.We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.
Op-RMSprop (Optimized-Root Mean Square Propagation) Classification for Prediction of Polycystic Ovary Syndrome (PCOS) using Hybrid Machine Learning Technique

Polycystic Ovary Syndrome is a common women's health problem caused by the imbalance in the reproductive hormones which causes problems in the ovaries.An appropriate machine learning (ML) algorithm can be applied to analyze the datasets and validate the performance of the algorithm in terms of accuracy.In this paper, a unique hybrid and optimized methodology are proposed which uses SVM linear kernel with Logistic Regression functionalities in a different way.The output of this model is passed on to the RMSprop optimizer.Optimization will train the model iteratively to get better output.For this research 1600 datasets were collected from the leading hospital in Bangalore Urban region.This optimized hybrid method is tested over PCOS datasets and it exhibited 89.03% accuracy.The results showed that the optimized-hybrid model works efficiently when compared to other existing ML Algorithms like SVM, Logistic regression, Decision tree, KNN, Random forest, and Adaboost.Also, the results of the optimizedhybrid SVLR model showed good results in terms of F-measure, precision, and recall statistical criteria.Overall this paper summarizes the working of the proposed optimized-SVLR hybrid model and prediction of PCOS.
Static Code Analysis Alarms Filtering Reloaded: A New Real-World Dataset and its ML-Based Utilization

Even though Static Code Analysis (SCA) tools are integrated into many modern software building and testing pipelines, their practical impact is still seriously hindered by the excessive number of false positive warnings they usually produce.To cope with this problem, researchers have proposed several post-processing methods that aim to filter out false hits (or equivalently identify "actionable" warnings) after the SCA tool produced its results.However, we found that most of these approaches are targeted (i.e., deal with only a few SCA warning types) and evaluated on synthetic benchmarks or small-scale manually collected data sets (i.e., with typical sample sizes of several hundred).In this paper, we present a dataset containing 224,484 real-world warning samples fixed (true positives) or explicitly ignored (false positives) by the developers, which we collected from 9,958 different opensource Java projects from GitHub using a data mining approach.Additionally, we utilize this rich dataset to train a code embedding-based machine learning model for filtering false positive warnings produced by 160 different SonarQube rule checks, one of the most widely adopted SCA tools today.This is the most extensive real-world public dataset and study we know of in this area.Our method works with an accuracy of 91% (best F1-score of 81.3% and AUC of 95.3%) for the classification of SonarQube warnings.
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor Areas

Vision and language navigation (VLN) is a challenging visually-grounded language understanding task.Given a natural language navigation instruction, a visual agent interacts with a graph-based environment equipped with panorama images and tries to follow the described route.Most prior work has been conducted in indoor scenarios where best results were obtained for navigation on routes that are similar to the training routes, with sharp drops in performance when testing on unseen environments.We focus on VLN in outdoor scenarios and find that in contrast to indoor VLN, most of the gain in outdoor VLN on unseen data is due to features like junction type embedding or heading delta that are specific to the respective environment graph, while image information plays a very minor role in generalizing VLN to unseen outdoor areas.These findings show a bias to specifics of graph representations of urban environments, demanding that VLN tasks grow in scale and diversity of geographical environments. 1
Lebanon Solar Rooftop Potential Assessment Using Buildings Segmentation From Aerial Images

Estimating solar rooftop potential at a national level is a fundamental building block for every country to utilize solar power efficiently.Solar rooftop potential assessment relies on several features such as building geometry, location, and surrounding facilities.Hence, national-level approximations that do not take these factors into deep consideration are often inaccurate.This paper introduces Lebanon's first comprehensive footprint and solar rooftop potential maps using deep learning-based instance segmentation to extract buildings' footprints from satellite images.A photovoltaic panels placement algorithm that considers the morphology of each roof is proposed.We show that the average rooftop's solar potential can fulfill the yearly electric needs of a single-family residence while using only 5% of the roof surface.The usage of 50% of a residential apartment rooftop area would achieve energy security for up to 8 households.We also compute the average and total solar rooftop potential per district to localize regions corresponding to the highest and lowest solar rooftop potential yield.Factors such as size, ground coverage ratio and P V out are carefully investigated for each district.Baalbeck district yielded the highest total solar rooftop potential despite its low built-up area.While, Beirut capital city has the highest average solar rooftop potential due to its extremely populated urban nature.Reported results and analysis reveal solar rooftop potential urban patterns and provides policymakers and key stakeholders with tangible insights.Lebanon's total solar rooftop potential is about 28.1 T W h/year, two times larger than the national energy consumption in 2019.
Evaluation Mechanism for Decentralized Collaborative Pattern Learning in Heterogeneous Vehicular Networks

Collaborative machine learning, especially Federated Learning (FL), is widely used to build high-quality Machine Learning (ML) models in the Internet of Vehicles (IoV).In this paper, we study the performance evaluation problem in an inherently heterogeneous IoV, where the final models across the network are not identical and are computed on different standards.Previous studies assume that local agents are receiving data from the same phenomenon, and a same final model is fitted to them.However, this "one model fits all" approach leads to a biased performance evaluation of individual agents.We propose a general approach to measure the performance of individual agents, where the common knowledge and correlation between different agents are explored.Experimental results indicate that our evaluation scheme is efficient in these settings.
A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model

As ever larger language models grow more ubiquitous, it is crucial to consider their environmental impact.Characterised by extreme size and resource use, recent generations of models have been criticised for their voracious appetite for compute, and thus significant carbon footprint.Although reporting of carbon impact has grown more common in machine learning papers, this reporting is usually limited to compute resources used strictly for training.In this work, we propose a holistic assessment of the footprint of an extremescale language model, Noor.Noor is an ongoing project aiming to develop the largest multi-task Arabic language models-with up to 13B parameters-leveraging zero-shot generalisation to enable a wide range of downstream tasks via natural language instructions.We assess the total carbon bill of the entire project: starting with data collection and storage costs, including research and development budgets, pretraining costs, future serving estimates, and other exogenous costs necessary for this international cooperation.Notably, we find that inference costs and exogenous factors can have a significant impact on total budget.Finally, we discuss pathways to reduce the carbon footprint of extreme-scale models.
Counterfactual Explanations for Natural Language Interfaces

A key challenge facing natural language interfaces is enabling users to understand the capabilities of the underlying system.We propose a novel approach for generating explanations of a natural language interface based on semantic parsing.We focus on counterfactual explanations, which are post-hoc explanations that describe to the user how they could have minimally modified their utterance to achieve their desired goal.In particular, the user provides an utterance along with a demonstration of their desired goal; then, our algorithm synthesizes a paraphrase of their utterance that is guaranteed to achieve their goal.In two user studies, we demonstrate that our approach substantially improves user performance, and that it generates explanations that more closely match the user's intent compared to two ablations. 1
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency

A well-calibrated neural model produces confidence (probability outputs) closely approximated by the expected accuracy.While prior studies have shown that mixup training as a data augmentation technique can improve model calibration on image classification tasks, little is known about using mixup for model calibration on natural language understanding (NLU) tasks.In this paper, we explore mixup for model calibration on several NLU tasks and propose a novel mixup strategy for pre-trained language models that improves model calibration further.Our proposed mixup is guided by both the Area Under the Margin (AUM) statistic (Pleiss et al., 2020) and the saliency map of each sample (Simonyan et al., 2013).Moreover, we combine our mixup strategy with model miscalibration correction techniques (i.e., label smoothing and temperature scaling) and provide detailed analyses of their impact on our proposed mixup.We focus on systematically designing experiments on three NLU tasks: natural language inference, paraphrase detection, and commonsense reasoning.Our method achieves the lowest expected calibration error compared to strong baselines on both indomain and out-of-domain test samples while maintaining competitive accuracy.
Bias Mitigation in Machine Translation Quality Estimation

Machine Translation Quality Estimation (QE) aims to build predictive models to assess the quality of machine-generated translations in the absence of reference translations.While state-of-the-art QE models have been shown to achieve good results, they over-rely on features that do not have a causal impact on the quality of a translation.In particular, there appears to be a partial input bias, i.e., a tendency to assign high-quality scores to translations that are fluent and grammatically correct, even though they do not preserve the meaning of the source.We analyse the partial input bias in further detail and evaluate four approaches to use auxiliary tasks for bias mitigation.Two approaches use additional data to inform and support the main task, while the other two are adversarial, actively discouraging the model from learning the bias.We compare the methods with respect to their ability to reduce the partial input bias while maintaining the overall performance.We find that training a multitask architecture with an auxiliary binary classification task that utilises additional augmented data best achieves the desired effects and generalises well to different languages and quality metrics.
JHU IWSLT 2022 Dialect Speech Translation System Description

This paper details the Johns Hopkins speech translation (ST) system used in the IWLST2022 dialect speech translation task.Our system uses a cascade of automatic speech recognition (ASR) and machine translation (MT).We use a Conformer model for ASR systems and a Transformer model for machine translation.Surprisingly, we found that while using additional ASR training data resulted in only a negligible change in performance as measured by BLEU or word error rate (WER), aggressive text normalization improved BLEU more significantly.We also describe an approach, similar to back-translation, for improving performance using synthetic dialect source text produced from source sentences in mismatched dialects.
Side-Channel Fuzzy Analysis-Based AI Model Extraction Attack With Information-Theoretic Perspective in Intelligent IoT

Accessibility to smart devices provides opportunities for side-channel attacks (SCAs) on artificial intelligent (AI) models in the intelligent Internet of Things (IoT). However, the existing literature exposes some shortcomings: 1) incapability of quantifying and analyzing the leaked information through side channels of the intelligent IoT and 2) inability to devise efficient and accurate SCA algorithms. To address these challenges, we propose a side-channel fuzzy analysis-empowered AI model extraction attack in the intelligent IoT. First, the integrated AI model extraction framework is proposed, including power trace-based structure, execution time-based metaparameters, and hierarchical weight extractions. Then, we develop the information theory-based analysis for the AI model extraction via SCA. We derive a mutual information-enabled quantification method, theoretical lower/upper bounds of information leakage, and the minimum number of attack queries to obtain accurate weights. Furthermore, a fuzzy gray correlation-based multiple-microspace parallel SCA algorithm is proposed to extract model weights in the intelligent IoT. Based on the established information-theoretic analysis model, the proposed fuzzy gray correlation-based SCA algorithm obtains high-precision AI weights. Experimental results, consisting of simulation and real-world experiments, verify that the developed analysis method with the information-theoretic perspective is feasible and demonstrate that the designed fuzzy gray correlation-based SCA algorithm is effective for AI model extraction.
Weighted Clustering

We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights. We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify algorithms accordingly.
The Role of Mobile Devices on Online EFL Skill Courses During Covid-19 Emergency Remote Education

The recent Covid-19 based emergency remote education caught educational practitioners unprepared and caused learners to rely on any technology which offered them the best pedagogical solution. Since mobile devices such as smartphones, tablets, and notebooks are widely used in daily life, EFL learners also preferred to utilize them for online courses due to their undisputable mobility. However, using mobile devices in long online EFL courses is not without its limitations. This study aimed to examine the mobile experience during EFL skill courses in emergency remote education. 47 EFL preparatory class students who were attending online courses for 4 months with their mobile devices were asked to write a reflection for each skill course based on three main categories: Limitations of mobile devices, effects of mobile devices on in-classroom interaction, and device satisfaction rates. The initial findings addressed that EFL learners commonly used smartphones or notebooks for online courses rather than desktop PCs and tablets. Secondly, our results revealed various limitations for each EFL course such as limited screen size, overheating, microphone problems, and internet connection efficiency. On the other hand, although mobile devices were reported to have worked fine for in-class communication, device satisfaction rates regarding online courses were low, especially for smartphones.
Measuring Rural Teachers’ Digital Competence to Communicate with the Educational Community

Teachers’ communication with the educational community (students, families and teachers) may be different depending on the place where they reside. We ask ourselves whether teachers in rural areas are sufficiently prepared to carry out this communication through digital media. Thus, this study had as its aims to: (1) ascertain teachers’ digital competence self-assessment about the utilisation of ICT resources to communicate with other teachers, students and families, according to gender, type of school, age and years of teaching experience; (2) analyse comparatively if significant differences exist in terms of digital competence level between the internal categories of each variable; (3) identify which digital resources are significant predictors. A non-experimental design was used with 847 rural teachers from different rural areas in Spain. The results showed an integrating attitude of every teacher-expert in their digital skills, regarding communication both with students and their families and with the other colleagues at the educational centre. We found differences in teachers' scores when comparing by gender and type of centre. Furthermore, we checked those applications such as Blogs, TikTok, Twitter and Moodle served were relevant predictors.
None Class Ranking Loss for Document-Level Relation Extraction

Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as "none" or "no relation". For good document-level RE performance, it is crucial to distinguish such none class instances (entity pairs) from those of pre-defined classes (relations). However, most existing methods only estimate the probability of pre-defined relations independently without considering the probability of "no relation". This ignores the context of entity pairs and the label correlations between the none class and pre-defined classes, leading to sub-optimal predictions. To address this problem, we propose a new multi-label loss that encourages large margins of label confidence scores between each pre-defined class and the none class, which enables captured label correlations and context-dependent thresholding for label prediction. To gain further robustness against positive-negative imbalance and mislabeled data that could appear in real-world RE datasets, we propose a margin regularization and a margin shifting technique. Experimental results demonstrate that our method significantly outperforms existing multi-label losses for document-level RE and works well in other multi-label tasks such as emotion classification when none class instances are available for training.