Reviewerly/exHarmony huggingface dataset:
English as a foreign language teachers’ perceptions regarding their pedagogical-technological knowledge and its implementation in distance learning during COVID-19

With the educational revolution driven by COVID-19, traditional face-to-face teaching methods have rapidly been transformed into accessible, reliable online distance education. This has meant revisiting and reinventing existing technology-based educational processes and models. This study investigates whether teachers of English as a Foreign Language (EFL) are confident that they have the requisite knowledge of how particular technologies are used for remote teaching, both during COVID-19 and as they look to the future. By adopting a mixed-method approach, this paper investigates teacher practices and perceptions regarding teaching online during Covid-19's emergency remote teaching. One hundred and twenty-nine participants were recruited through an online survey. All analyses were carried out using SPSS version 25. Data was based on ranking and non-parametric tests were used. Qualitative data from the open-ended question were analyzed using data-driven thematic analysis. Teachers reported significantly increased reliance on self-teaching, colleagues' knowledge, staff tutorials, and online school support. The gap between knowledge and usage of digital tools was found to be associated with the challenges facing EFL teachers with distance learning. Teachers who reported knowing more or roughly the same about the tools compared to their usage of them knew how to incorporate their knowledge into their practical teaching, took control over the management of their instruction, and had higher pupil engagement and motivation. However, teachers whose knowledge of digital tools was lower than their usage encountered technological difficulties that impaired their teaching. Some implications can be drawn from the study, such as the need for teacher education programs to improve teacher awareness of new pedagogical-technological learning methods, and the importance of providing opportunities to acquire digital competence and encourage teachers to adapt personally to new digital technologies within specific disciplinary contexts. Our findings have both theoretical and practical implications for pre- and in-service teacher training.
Just saying, just kidding: Liability for accountability-avoiding speech in ordinary conversation, politics and law

Mobsters and others engaged in risky forms of social coordination and coercion often communicate by saying something that is overtly innocuous but transmits another message ‘off record’. In both ordinary conversation and political discourse, insinuation and other forms of indirection, like joking, offer significant protection from liability. However, they do not confer blanket immunity: speakers can be held to account for an ‘off record’ message, if the only reasonable interpretations of their utterance involve a commitment to it. Legal liability for speech in the service of criminal behavior displays a similar profile of significant protection from indirection along with potential liability for reasonable interpretations. Specifically, in both ordinary and legal contexts, liability depends on how a reasonable speaker would expect a reasonable hearer to interpret their utterance in the context of utterance, rather than on the actual speaker’s claimed communicative intentions.
Easy—Ensemble Augmented-Shot-Y-Shaped Learning: State-of-the-Art Few-Shot Classification with Simple Components

Few-shot classification aims at leveraging knowledge learned in a deep learning model, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have seen a fair number of works in the field, each one introducing their own methodology. A frequent problem, though, is the use of suboptimally trained models as a first building block, leading to doubts about whether proposed approaches bring gains if applied to more sophisticated pretrained models. In this work, we propose a simple way to train such models, with the aim of reaching top performance on multiple standardized benchmarks in the field. This methodology offers a new baseline on which to propose (and fairly compare) new techniques or adapt existing ones.
Findings of the IWSLT 2022 Evaluation Campaign

The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.A total of 27 teams participated in at least one of the shared tasks.This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.
The impact of task workload on cybersickness

This study explored the impact of task workload on virtual reality (VR) cybersickness. Cybersickness is a negative side effect of using VR to which many users are susceptible. Previous research on the impact on task workload on cybersickness has yielded no consistent relationships, but given that task workload requires attentional resources, it is worth further investigation of how a demand on attention might increase or decrease cybersickness. In this study, mental workload of participants ( N = 151) was modified in three different task groups within the same virtual environment (VE). The Cybersickness Corn Maze VR testbed contained cybersickness-inducing stimuli and tasks with varying workload. The 0-Back group used a controller to select an object as a visual attention task. The 2-Back group performed the 2-Back memory detection task, using a controller to collect objects that matched the object presented two objects ago. The No-Task group passively moved through the environment and was not given a controller. Workload, cybersickness, dropout rate, presence, and task accuracy were compared across groups. Workload was found to be statistically significantly different in each group: highest in the 2-Back group, medium in the 0-Back group, and lowest in the No-Task group, validating the task design. Cybersickness in the 2-Back group was significantly higher than in the No-Task (140% higher) and 0-Back (54% higher) groups measured by the change in simulator sickness questionnaire (SSQ) total severity (Post SSQ - Pre SSQ). The rate of participants’ dropout due to cybersickness was significantly higher in 2-back (33%) as compared with 0-Back (10%), but 0-Back and No-Task dropout rates were not significantly different. These results indicate that 1 ) task workload affects cybersickness and 2 ) its effect could be based on a threshold of workload. Presence increased with the addition of a task but plateaued between the 0-Back and 2-Back groups, suggesting that presence can be affected by task workload but only to a certain extent. Task accuracy was shown to negatively correlate with cybersickness within the task groups. A relationship between workload and cybersickness was found and warrants further research into these concepts. This work highlights the need for task workload and attention to be studied as components of the mechanisms underlying cybersickness.
Emotionally Intelligent Chatbots: A Systematic Literature Review

Conversational technologies are transforming the landscape of human-machine interaction. Chatbots are increasingly being used in several domains to substitute human agents in performing tasks, answering questions, giving advice, and providing social and emotional support. Therefore, improving user satisfaction with these technologies is imperative for their successful integration. Researchers are leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to impart emotional intelligence capabilities in chatbots. This study provides a systematic review of research on developing emotionally intelligent chatbots. We employ a systematic approach to gather and analyze 42 articles published in the last decade. The review is aimed at providing a comprehensive analysis of past research to discover the problems addressed, the techniques used, and the evaluation measures employed by studies in embedding emotion in chatbot conversations. The study’s findings reveal that most studies are based on an open-domain generative chatbot architecture. Researchers mainly address the issue of accurately detecting the user’s emotion and generating emotionally relevant responses. Nearly 57% of the studies use an enhanced Seq2Seq encoding and decoding of the input of the conversational model. Almost all the studies use both the automatic and manual evaluation measures to evaluate the chatbots, with the BLEU measure being the most popular method for objective evaluation.
Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records

Electronic health records (EHR) represent a holistic overview of patients' trajectories.Their increasing availability has fueled new hopes to leverage them and develop accurate risk prediction models for a wide range of diseases.Given the complex interrelationships of medical records and patient outcomes, deep learning models have shown clear merits in achieving this goal.However, a key limitation of current study remains their capacity in processing long sequences, and long sequence modelling and its application in the context of healthcare and EHR remains unexplored.Capturing the whole history of medical encounters is expected to lead to more accurate predictions, but the inclusion of records collected for decades and from multiple resources can inevitably exceed the receptive field of the most existing deep learning architectures.This can result in missing crucial, long-term dependencies.To address this gap, we present Hi-BEHRT, a hierarchical Transformer-based model that can significantly expand the receptive field of Transformers and extract associations from much longer sequences.Using a multimodal largescale linked longitudinal EHR, the Hi-BEHRT exceeds the state-of-the-art deep learning models 1% to 5% for area under the receiver operating characteristic (AUROC) curve and 1% to 8% for area under the precision recall (AUPRC) curve on average, and 2% to 8% (AUROC) and 2% to 11% (AUPRC) for patients with long medical history for 5-year heart failure, diabetes, chronic kidney disease, and stroke risk prediction.Additionally, because pretraining for
Systematic-narrative hybrid literature review: A strategy for integrating a concise methodology into a manuscript

Attempting to build a house without a blueprint could result in a fundamentally unsound structure. A thorough investigation of a building's foundation is essential to assess its quality and fitness for purpose. This is also true of literature reviews which should be constructed on the foundation of a well-developed methodology that clearly outlines the search strategy, inclusion/exclusion criteria, sources of literature, and information synthesis techniques employed to develop the arguments and conclusions presented to the reader. This research note outlines a systematic-narrative hybrid strategy that can be incorporated into a literature review manuscript without overburdening the reader with unnecessary detail.
Can Alexa serve customers better? AI-driven voice assistant service interactions

Purpose This study aims to examine customers’ willingness to engage in service interactions enabled by artificial intelligence (AI) controlled voice assistants (VA). Drawing on the tenets of dual-factor theory, this study measures the impact of both enablers and inhibitors – mediated by trust in Alexa – on customers’ intentions to transact through VAs. Design/methodology/approach Data from a survey of 290 users of VAs from Japan was collected through “Macromill”. The authors used a covariance-based path analysis technique for data analysis after establishing the validity and reliability of the measures. Findings The results of this study demonstrate that convenience and status-seeking act as enablers and positively influence trust in VAs, whereas risk barrier acts as an inhibitor and negatively influence trust in VAs. In turn, trust in VAs positively influences the intention to use VAs for transactional service interactions. This association is positively moderated by technology comfort. Originality/value This study applies dual-factor theory to the context of VAs – a context that scholars have, to date, examined solely from a technology adoption perspective. For the first time, the authors adopt a dual-factor approach to identify a new set of antecedents for customers’ intentions to use VAs for transactional service interactions.
Impacts of blockchain-based digital transition on cold supply chains with a third-party logistics service provider

Blockchain is a driver for digitalization in the fresh industry, but the resulting improvements in each supply chain member’s performance are difficult to predict. In this study, we examine the effects of blockchain-based digital transition in a cold supply chain with a manufacturer, a retailer, and a third-party logistics service provider (3PL). Our study provides several managerial implications. First, blockchain adoption would lead to a preservation service level increase if the 3PL charges a low transportation fee, but a preservation service level decrease if the 3PL charges a high one. Second, the blockchain adoption may induce the manufacturer to increase its wholesale price, which does not cause the retailer to cut the order quantity but instead sets a lager one. Third, our results indicate that simply relying on the market mechanism may not enable the cold supply chain to make the optimal decision; thus, government intervention or an exogenous coordination mechanism must be introduced to drive the cold supply chain members to reach a consensus on blockchain-based digital transition. Finally, the main results in the basic model are proven to remain robust. Managerial implications are discussed and provided.
Technology Acceptance Model for 3D Virtual Reality System in Luxury Brands Online Stores

This paper presents an evaluation of a 3D semi-immersive virtual reality (VR) system for online luxury product visualization and customization service. This paper's objective is to present the results of the survey-based analysis using a technology acceptance model with ease of use, usefulness, perceived experience value, and perceived presence as independent variables, in testing the attitudes toward the system. The impact of product customization and personalization features on the perceived experience value and the attitudes toward the system was also tested. The result shows that the perceived presence, usefulness, ease of use, and the perceived experience value have a significant positive effect on the attitudes toward the 3D VR semi-immersive system. The result also revealed that utilizing the advantage of 3D VR systems with regard to real-time manipulation of the product and the flexibility in customizing the 3D models' features in real time has elevated the attitudes toward the aforementioned emerging technology within this specific context.
A Classification Method Based on Feature Selection for Imbalanced Data

Imbalanced data are very common in the real world, and it may deteriorate the performance of the conventional classification algorithms.In order to resolve the imbalanced classification problems, we propose an ensemble classification method that combines evolutionary under-sampling and feature selection.We employ the Bootstrap method in original data to generate many sample subsets.V -statistic is developed to measure the distribution of imbalanced data, and it is also taken as the optimization objective of the genetic algorithm for the under-sampling sample subsets.Moreover, we take F 1 and Gmean indicators as two optimization objectives and employ the multiobjective ant colony optimization algorithm for feature selection of resampled data to construct an ensemble system.Ten low-dimensional and four high-dimensional typical imbalanced datasets are used in experiments.The six state-of-the-art algorithms and four measures are taken for a fair comparison.The experimental results show that our proposed system has a better classification performance compared with other algorithms, especially for the high-dimensional imbalanced data.
Policy specification and verification for blockchain and smart contracts in 5G networks

Blockchain offers unprecedented opportunities for innovation in financial transactions. A whole new world of opportunities for banking, lending, insurance, money transfer, investments, and stock markets awaits. However, the potential for wide-scale adoption of blockchain is hindered with cybersecurity and privacy issues. We provide an overview of the risks and security requirements and give an outlook for future research that could be helpful in solving some of the challenges. We also present an approach for policy specification and verification of financial transactions based on smart contracts.
Deep convolutional neural networks as a geological image classification tool

A convolutional neural network (CNN) is a deep learning (DL) method that has been widely and successfully applied to computer vision tasks including object localization, detection, and image classification. DL for supervised learning tasks is a method that uses the raw data to determine the classification features, in contrast to other machine learning (ML) techniques that require pre-selection of the input features (or attributes). In the geosciences, we hypothesize that deep learning will facilitate the analysis of uninterpreted images that have been neglected due to a limited number of experts, such as fossil images, slabbed cores, or petrographic thin sections. We use transfer learning, which employs previously trained models to shorten the development time for subsequent models, to address a suite of geologic interpretation tasks that may benefit from ML. Using two different base models, MobileNet V2 and Inception V3, we illustrate the successful classification of microfossils, core images, petrographic photomicrographs, and rock and mineral hand sample images. ML does not replace the expert geoscientist. The expert defines the labels (interpretations) needed to train the algorithm and also monitors the results to address incorrect or ambiguous classifications. ML techniques provide a means to apply the expertise of skilled geoscientists to much larger volumes of data.
Sport-Related Human Activity Detection and Recognition Using a Smartwatch

As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method.
Permutation Equivariant Models for Compositional Generalization in Language

Humans understand novel sentences by composing meanings and roles of core language components. In contrast, neural network models for natural language modeling fail when such compositional generalization is required. The main contribution of this paper is to hypothesize that language compositionality is a form of group-equivariance. Based on this hypothesis, we propose a set of tools for constructing equivariant sequence-to-sequence models. Throughout a variety of experiments on the SCAN tasks, we analyze the behavior of existing models under the lens of equivariance, and demonstrate that our equivariant architecture is able to achieve the type compositional generalization required in human language understanding.
A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images

Pap smear is often employed as a screening test for diagnosing cervical pre-cancerous and cancerous lesions. Accurate identification of dysplastic changes amongst the cervical cells in a Pap smear image is thus essential for rapid diagnosis and prognosis. Manual pathological observations used in clinical practice require exhaustive analysis of thousands of cell nuclei in a whole slide image to visualize the dysplastic nuclear changes which make the process tedious and time-consuming. Automated nuclei segmentation and classification exist but are challenging to overcome issues like nuclear intra-class variability and clustered nuclei separation. To address such challenges, we put forward an application of instance segmentation and classification framework built on an Unet architecture by adding residual blocks, densely connected blocks and a fully convolutional layer as a bottleneck between encoder-decoder blocks for Pap smear images. The number of convolutional layers in the standard Unet has been replaced by densely connected blocks to ensure feature reuse-ability property while the introduction of residual blocks in the same attempts to converge the network more rapidly. The framework provides simultaneous nuclei instance segmentation and also predicts the type of nucleus class as belonging to normal and abnormal classes from the smear images. It works by assigning pixel-wise labels to individual nuclei in a whole slide image which enables identifying multiple nuclei belonging to the same or different class as individual distinct instances. Introduction of a joint loss function in the framework overcomes some trivial cell level issues on clustered nuclei separation. To increase the robustness of the overall framework, the proposed model is preceded with a stacked auto-encoder based shape representation learning model. The proposed model outperforms two state-of-the-art deep learning models Unet and Mask_RCNN with an average Zijdenbos similarity index of 97 % related to segmentation along with binary classification accuracy of 98.8 %. Experiments on hospital-based datasets using liquid-based cytology and conventional pap smear methods along with benchmark Herlev datasets proved the superiority of the proposed method than Unet and Mask_RCNN models in terms of the evaluation metrics under consideration.