Reviewerly/exHarmony huggingface dataset:
Inter-domain Deep Gaussian Processes

Inter-domain Gaussian processes (GPs) allow for high flexibility and lowcomputational cost when performing approximate inference in GP models. They areparticularly suitable for modeling data exhibiting global structure but arelimited to stationary covariance functions and thus fail to modelnon-stationary data effectively. We propose Inter-domain Deep GaussianProcesses, an extension of inter-domain shallow GPs that combines theadvantages of inter-domain and deep Gaussian processes (DGPs), and demonstratehow to leverage existing approximate inference methods to perform simple andscalable approximate inference using inter-domain features in DGPs. We assessthe performance of our method on a range of regression tasks and demonstratethat it outperforms inter-domain shallow GPs and conventional DGPs onchallenging large-scale real-world datasets exhibiting both global structure aswell as a high-degree of non-stationarity.
An Experimental Analysis of Model Compression Techniques for Object Detection

Recent research on Convolutional Neural Networks focuses on how to create models with a reduced number of parameters and a smaller storage size while keeping the model’s ability to perform its task, allowing the use of the best CNN for automating tasks in limited devices, with reduced processing power, memory, or energy consumption constraints. There are many different approaches in the literature: removing parameters, reduction of the floating-point precision, creating smaller models that mimic larger models, neural architecture search (NAS), etc. With all those possibilities, it is challenging to say which approach provides a better trade-off between model reduction and performance, due to the difference between the approaches, their respective models, the benchmark datasets, or variations in training details. Therefore, this article contributes to the literature by comparing three state-of-the-art model compression approaches to reduce a well-known convolutional approach for object detection, namely YOLOv3. Our experimental analysis shows that it is possible to create a reduced version of YOLOv3 with 90% fewer parameters and still outperform the original model by pruning parameters. We also create models that require only 0.43% of the original model’s inference effort.
Updating the DCC Curation Lifecycle Model

The DCC Curation Lifecycle Model has played a vital role in the field of data curation for over a decade. During that time, the scale and complexity of data have changed dramatically, along with the contexts of data production and use. This paper reports on a study examining factors impacting data curation practices and presents recommendations for updating the DCC Curation Lifecycle Model. The study was grounded in a review of other lifecycle models and informed by a site visit to the Digital Curation Centre and consultation with expert practitioners and researchers. Framed by contemporary conditions impacting the conduct of research and provision of data services, the analysis and proposed recommendations account for the prominence of machine-actionable data, the importance of machine learning for data processing and analytics, growth of integrated research workflows, and escalating concerns with fairness, accountability, and transparency of data and algorithms.
Jigsaw @ AMI and HaSpeeDe2: Fine-Tuning a Pre-Trained Comment-Domain BERT Model

The Google Jigsaw team produced submissions for two of the EVALITA 2020 (Basile et al. 2020) shared tasks, based in part on the technology that powers the publicly available PerspectiveAPI comment evaluation service. We present a basic description of our submitted results and a review of the types of errors that our system made in these shared tasks.
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions

Events in a narrative differ in salience: some are more important to the story than others.Estimating event salience is useful for tasks such as story generation, and as a tool for text analysis in narratology and folkloristics.To compute event salience without any annotations, we adopt Barthes' definition of event salience and propose several unsupervised methods that require only a pre-trained language model.Evaluating the proposed methods on folktales with event salience annotation, we show that the proposed methods outperform baseline methods and find fine-tuning a language model on narrative texts is a key factor in improving the proposed methods.
Analysis and Optimal Design of a Novel Actuator System for a Camera Module

Owing to the fourth industrial revolution and the recent trend for compact, high-performance, and multifunctional electrical devices, the demand for a small and high-performance camera module has dramatically increased. To address this, in this study, a novel, miniaturized, high-performance, low-cost multifunctional camera module with optical image stabilization (OIS) via a novel two-axis motional actuator with 1-Stage is proposed. To date, a systematic analysis and optimal design method for the actuators of camera modules has not been proposed yet. Accurate and rapid analysis and optimal design methods are proposed herein. Based on the proposed methods, it is possible to reduce the time and cost for the development of actuators for camera modules. The usefulness of the proposed actuator system for small, high performance, and multifunctional camera modules, and the proposed analysis and optimal design method were verified experimentally in this study.
A 3D Reactive Navigation Algorithm for Mobile Robots by Using Tentacle-Based Sampling

This paper introduces a reactive navigation framework for mobile robots in 3-dimensional (3D) space. The proposed approach does not rely on the global map information and achieves fast navigation by employing a tentacle-based sampling and their heuristic evaluations on-the-fly. This reactive nature of the approach comes from the prior arrangement of navigation points on tentacles (parametric contours) to sample the navigation space. These tentacles are evaluated at each timestep, based on heuristic features such as closeness to the goal, previous tentacle preferences and nearby obstacles in a robot-centered 3D grid. Then, the navigable sampling point on the selected tentacle is passed to a controller for the motion execution. The proposed framework does not only extend its 2D tentacle-based counterparts into 3D, but also introduces offline and online parameters, whose tuning provides versatility and adaptability of the algorithm to work in unknown environments. To demonstrate the superior performance of the proposed algorithm over a state-of-art method, the statistical results from physics-based simulations on various maps are presented. The video of the work is available at https://youtu.be/rrF7wHCz-0M.
PEDNet: A Persona Enhanced Dual Alternating Learning Network for Conversational Response Generation

Endowing a chatbot with a personality is essential to deliver more realistic conversations.Various persona-based dialogue models have been proposed to generate personalized and diverse responses by utilizing predefined persona information.However, generating personalized responses is still a challenging task since the leverage of predefined persona information is often insufficient.To alleviate this problem, we propose a novel Persona Enhanced Dual Alternating Learning Network (PEDNet) aiming at producing more personalized responses in various opendomain conversation scenarios.PEDNet consists of a Context-Dominated Network (CDNet) and a Persona-Dominated Network (PDNet), which are built upon a common encoder-decoder backbone.CDNet learns to select a proper persona as well as ensure the contextual relevance of the predicted response, while PDNet learns to enhance the utilization of persona information when generating the response by weakening the disturbance of specific content in the conversation context.CDNet and PDNet are trained alternately using a multi-task training approach to equip PEDNet with the both capabilities they have learned.Both automatic and human evaluations on a newly released dialogue dataset Persona-chat demonstrate that our method could deliver more personalized responses than baseline methods.
Federated Learning for Privacy-Preserving Data Access

Federated learning is a pioneering privacy-preserving data technology and also a new machine learning model trained on distributed data sets.Companies collect huge amounts of historic and real-time data to drive their business and collaborate with other organisations. However, data privacy is becoming increasingly important because of regulations (e.g. EU GDPR) and the need to protect their sensitive and personal data. Companies need to manage data access: firstly within their organizations (so they can control staff access), and secondly protecting raw data when collaborating with third parties. What is more, companies are increasingly looking to ‘monetize’ the data they’ve collected. However, under new legislations, utilising data by different organization is becoming increasingly difficult (Yu, 2016).Federated learning pioneered by Google is the emerging privacy- preserving data technology and also a new class of distributed machine learning models. This paper discusses federated learning as a solution for privacy-preserving data access and distributed machine learning applied to distributed data sets. It also presents a privacy-preserving federated learning infrastructure.
Descriptive Cataloging: The History and Practice of Describing Library Resources

Descriptive cataloging is the process of representing resources by recording their identifying traits and selecting specific names and titles to serve as access points. It is a key component of the larger cataloging process alongside subject cataloging, authority work, and encoding. Descriptive cataloging practices have existed for centuries and, over time, have become standardized through the use of cataloging codes. These documents guide this process by prescribing a consistent set of elements, providing directions on how to record these elements, and offering instructions on how to select and format access points. The goal of descriptive cataloging is not to create perfect representations but to provide data to serve users. The international cataloging standard Resource Description and Access (RDA) is now bringing more institutions under the same set of descriptive practices than ever before. This, along with recent technological developments, promises increased sharing and reuse of descriptive cataloging data.
Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to scenarios where the baseline prevalence of the disease is different. Given estimates of p(y|x) from a predictive model, Saerens et al. proposed an efficient maximum likelihood algorithm to correct for label shift that does not require model retraining, but a limiting assumption of this algorithm is that p(y|x) is calibrated, which is not true of modern neural networks. Recently, Black Box Shift Learning (BBSL) and Regularized Learning under Label Shifts (RLLS) have emerged as state-of-the-art techniques to cope with label shift when a classifier does not output calibrated probabilities, but both methods require model retraining with importance weights and neither has been benchmarked against maximum likelihood. Here we (1) show that combining maximum likelihood with a type of calibration we call bias-corrected calibration outperforms both BBSL and RLLS across diverse datasets and distribution shifts, (2) prove that the maximum likelihood objective is concave, and (3) introduce a principled strategy for estimating source-domain priors that improves robustness to poor calibration. This work demonstrates that the maximum likelihood with appropriate calibration is a formidable and efficient baseline for label shift adaptation; notebooks reproducing experiments available at this https URL
Multilingual Emoticon Prediction of Tweets about COVID-19

Emojis are a widely used tool for encoding emotional content in informal messages such as tweets,and predicting which emoji corresponds to a piece of text can be used as a proxy for measuring the emotional content in the text. This paper presents the first model for predicting emojis in highly multilingual text.Our BERTmoticon model is a fine-tuned version of the BERT model,and it can predict emojis for text written in 102 different languages.We trained our BERTmoticon model on 54.2 million geolocated tweets sent in the first 6 months of 2020,and we apply the model to a case study analyzing the emotional reaction of Twitter users to news about the coronavirus. Example findings include a spike in sadness when the World Health Organization (WHO) declared that coronavirus was a global pandemic, and a spike in anger and disgust when the number of COVID-19 related deaths in the United States surpassed one hundred thousand. We provide an easy-to-use and open source python library for predicting emojis with BERTmoticon so that the model can easily be applied to other data mining tasks.
EFFICIENCY OF MEDIA TOOLS USE BY PRIMARY SCHOOL TEACHER AT THE FOREIGN LANGUAGES LESSONS: FROM AWARENESS TO READINESS

The article actualizes the issue of the future primary school teacher’s training for the use of media at foreign language lessons. It is characterized as a dynamic process that confirms a certain readiness of a future specialist to use media in professional activities for the active students’ subject-subject cognitive activity. The result of this training is the future teacher’s readiness to perform the specified activity. The structural components of the future specialist’s readiness for the implementation of the specified type of activity are determined and described by the author. Motivational, cognitive, technological and reflexive components build the structure of the future primary school teacher’s readiness for the use of media at foreign languages lessons. All of them are interdependent and complementarity. The process of formation of structural components of readiness during the future primary school teacher’s professional training is analyzed in the paper. It is considered on the basis of Bloom’s taxonomy (taxonomy of the organization of cognitive personality skills). Diagnostic tools to measure the level of formation of structural components of readiness are also offered. They are criteria, indicators and levels. A personal purposeful criterion, a knowledgeable criterion, a methodical criterion, an evaluative resultant criterion determine the formation of each separate component of the future primary school teacher’s readiness for the use of media at foreign language lessons. The formation of each component of readiness is measured according to the defined indicators that are mentioned by the author. The level of the formation of each component of the future primary school teacher’s readiness for the use of media at foreign language lessons is measured according to the reproductive, sufficient and productive levels.
Aplikasi Standar Proses Keperawatan: Diagnosis, Outcome, dan Intervensi pada Asuhan Keperawatan

This study aims to look at the quality of nursing care documentation at Myria Hospital Palembang. Analytic surveys are used to obtain an overview of nursing documentation as secondary data. Nursing care documents used were 105 documents by cluster sampling and measured using Q-DIO instruments, and analyzed descriptively. The results showed most of the medium quality in all dimensions, namely diagnosis as a process of 70 documents (67%), diagnosis as a product as many as 56 documents (53%), intervention as many as 66 documents (63%), and nursing outcomes as many as 67 documents (64%). The conclusions in this study indicate that 67% of records have moderate quality on the dimensions of diagnosis as a process, 53% of documents have a reasonable condition on the aspects of diagnosis as a product, 63% of records have moderate quality on the dimensions of intervention, and 64% of documents have average quality on the dimensions of output nursing. The results of the nursing process that have been obtained in this study illustrate the change or improvement in the improvement of symptoms that have a moderate quality to integrate with the nursing process.&#x0D; &#x0D; Keywords: Diagnosis, Documentation, Intervention, Output
Edge Computing Implementation for Action Recognition Systems

Nowadays the deep learning has been improved to many different sectors, including human action recognition system. This system mostly needs a high computing resource to work on. In its implementation, it will be built under cloud computing architecture which requires sensors used to send whole raw data to the cloud which puts a load in the networks. Therefore, edge computing system exists to overcome that weakness. This paper presents a method to recognize human action using deep learning with edge computing architecture. With RGB image as the input, this system will detect all persons in the frame using SSD-Mobilenet V2 model with various threshold values, then recognize every personâ€™s action using our trained model with DetectNet architecture in various threshold too. The output of the system is detected personâ€™s RoI and its recognized action action, which a lot smaller than the whole frame. As a result, our proposed system yields the best accuracy of human detection at 64.06% with a threshold at 0.15 and the best accuracy of action recognition atÂ  37.8% with a threshold at 0.4.
Geometry Aware NMPC Scheme for Morphing Quadrotor Navigation in Restricted Entrances

Geometry-morphing Micro Aerial Vehicles (MAVs) are gaining more attention lately, since, their ability to modify their geometric morphology in-flight increases their versatility while expanding their application range. In this novel research field, most of the works focus on the platform design and on the low-level control for maintaining stability after changing its shape. Nevertheless, another aspect of geometry morphing MAVs is the association of its morphology with respect to the shape and structure of the environment. In this article, we propose a novel Nonlinear Model Predictive Control (NMPC) structure that modifies the morphology of a quadrotor based on the environment entrances’ geometrical shape. The proposed method considers restricted entrances as a constraint in the NMPC and modifies the arm configuration of the MAV to provide a collision-free path from the initial position to the desired goal while passing through the entrance. Multiple simulation results present the performance and efficiency of the proposed scheme in scenarios where the quadrotor is commanded to pass through narrow entrances.
Financial Openness and Economic Growth in Côte d’Ivoire: The Total Factor Productivity Channel

This study analyses the influence of financial openness on total factor productivity (TFP) in Cote d’Ivoire to examine one of the channels through which financial openness affects economic growth. To this end, the study uses data from the Penn World Table (PWT) 9.1 and the World Bank over the 1984-2018 periods. By using Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) methods, the paper shows that financial openness positively affects total factor productivity in Côte d’Ivoire. Therefore, total factor productivity is one of the channels through which financial openness affects economic growth. This result calls for a series of measures to strengthen the competitiveness of the country to attract a large inflow of foreign capital and to boost the level of the TFP which is an engine of economic growth. Moreover, the results reveal that financial development, macroeconomic stability (as measured by the inflation rate) and domestic investment are important determinants of total factor productivity growth.
Challenges in Neural Language Identification: NRC at VarDial 2020

We describe the systems developed by the National Research Council Canada for the Uralic language identification shared task at the 2020 VarDial evaluation campaign. Although our official results were well below the baseline, we show in this paper that this was not due to the neural approach to language identification in general, but to a flaw in the function we used to sample data for training and evaluation purposes. Preliminary experiments conducted after the evaluation period suggest that our neural approach to language identification can achieve state-of-the-art results on this task, although further experimentation is required.
Conversational Assistants and Gender Stereotypes: Public Perceptions and Desiderata for Voice Personas

Conversational voice assistants are rapidly developing from purely transactional systems to social companions with “personality”. UNESCO recently stated that the female and submissive personality of current digital assistants gives rise for concern as it reinforces gender stereotypes. In this work, we present results from a participatory design workshop, where we invite people to submit their preferences for a what their ideal persona might look like, both in drawings as well as in a multiple choice questionnaire. We find no clear consensus which suggests that one possible solution is to let people configure/personalise their assistants. We then outline a multi-disciplinary project of how we plan to address the complex question of gender and stereotyping in digital assistants.
A Rule-based Approach toward Automating the Assessments of Academic Curriculum Mapping

Curriculum mapping is the blueprint of a successful academic program.It is progressively utilised in higher education as a monitoring tool in the current age of standard-based regulations and empowers program leaders and course instructors to align their curricula for the offered courses and the corresponding learning outcomes.It is often depicted by a two-dimensional matrix expressing the relationship between the students learning outcomes (i.e., SOs) and the courses.However, its mapping remains a challenging exercise, even for experienced program leaders.The complexity stems from the fact that mistakes are prone to happen during the mapping, and program leaders need to be aware of the rules and the acceptable practices of curriculum-effective mapping.Besides, it is not straightforward to spot contradictions in the SO-course mappings.Consequently, this paper aims to tackle these challenges by investigating effectivemapping rules from existing curriculum mappings, which allows one to inspect the SO-course mappings, discover inefficiencies, and provide suggestions for improving the curriculum mapping.We identify the main mapping criteria and propose a rule-based algorithm for curriculum matrix assessments.This algorithm is implemented in an online application and evaluated using a userbased experiment, relying on curriculum mapping experts.The findings have shedded light on the promise of our approach.