Reviewerly/exHarmony huggingface dataset:
EMHIFormer: An Enhanced Multi-Hypothesis Interaction Transformer for 3D human pose estimation in video

Monocular 3D human pose estimation is a challenging task because of depth ambiguity and occlusion. Recent methods exploit spatio-temporal information and generate different hypotheses for simulating diverse solutions to alleviate these problems. However, these methods do not fully extract spatial and temporal information and the relationship of each hypothesis. To ease these limitations, we propose EMHIFormer (Enhanced Multi-Hypothesis Interaction Transformer) to model 3D human pose with better performance. In detail, we build connections between different Transformer layers so that our model is able to integrate spatio-temporal information from the previous layer and establish more comprehensive hypotheses. Furthermore, a cross-hypothesis model consisting of a parallel Transformer is proposed to strengthen the relationship between various hypotheses. We also design an enhanced regression head which adaptively adjusts the channel weights to export the final 3D human pose. Extensive experiments are conducted on two challenging datasets: Human3.6M and MPI-INF-3DHP to evaluate our EMHIFormer. The results show that EMHIFormer achieves competitive performance on Human3.6M and state-of-the-art performance on MPI-INF-3DHP. Compared with the closest counterpart, MHFormer, our model outperforms it by 0.6% P-MPJPE and 0.5% MPJPE on Human3.6M dataset and 46.0% MPJPE on MPI-INF-3DHP.
The Role of Environmental Policy in Influencing Governance and Sustainability Practices Among Nigerian Quoted Companies: A Proposed Framework

Purpose: This study aimed to introduce a moderator to strengthen the relationship between institutional factors and corporate managers’ immersion as governance attributes toward firms’ sustainability initiatives. Theoretical framework: A proposed model based on the developed hypothesis was formulated based on empirical and theoretical facts to support the concepts of institutional and legitimacy theories. Design/methodology/approach: The research uses a quantitative method that examines the quoted companies rated and ranked by Nigeria’s World CSR Consensus rating, including all Financial and Non-Financial sectors. It also assesses the companies’ roles in encouraging friendly environmental practices. Findings: The consequence of this report shows that Just 26 corporations have occupied a position on corporate social responsibility (CSR) initiatives, with Access Bank having the highest significant value of 99% and UAC Nigeria Plc having the lowest value of 1%. Additionally, only 90 out of 156 Nigeria’s publicly traded firms have been assessed and recognized by CSRHUB, having the lowest percentage. Research, Practical &amp; Social implications: since there are no standard ways of enhancing the institutional factors and corporate managers’ immersion rapport to add to the body of knowledge, Environmental Policy is proposed and recommended as essential in shaping future planning and sustainability practices. Originality/value: To strengthen and increase the sustainability practices success of these firms, they must pay more attention to the environmental policies and the factors that influence them. Further studies might consider improving the research by analyzing the variables in the proposed model based on the hypothesis formulated and the time-variant.
The Relationship Between Individual Competencies and Creativity of Cadet with Knowledge Sharing: A Case Study at the Indonesian Pilot Academy

Purpose: This study aims to analyze the effect of knowledge sharing on competency development, knowledge sharing on individual creativity, and competency development on individual creativity for cadets. Theoretical framework: Knowledge sharing is a culture of social interaction that involves knowledge, experience and skills between members in an organization. Competency is a factor in achieving an organizational goal so that individual performance can increase. For creativity can be assumed as human power or ability to create something and then become the final result of a person's thinking. Design/methodology/approach: This type of research is explanatory research conducted with the quantitative approach with 50 respondents from cadets at Politeknik Penerbangan Surabaya. The data analysis technique is used structural equation modeling (SEM) analysis with partial least square (PLS) tools. Findings: This results are knowledge sharing has positive effect on the Competency Development, knowledge sharing has positive effect on Individual Creativity, and competency development has positive effect on Individual Creativity for cadets. Research, Practical &amp; Social implications: This research contributes to the application of teacher performance in learning class to improve competency development on individual creativity so that learning is more effective. Originality/value: The results obtained in this study are innovative and relevant for lecture, in the context of knowledge sharing, competency development, and individual creativity for cadets.
Demonstration of machine-learning-enhanced Bayesian quantum state estimation

Abstract Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for Bayesian quantum state estimation methods that generally better conform to the physical properties of the underlying system than standard fixed prior distributions. Previously, researchers have looked to Bayesian quantum state tomography for advantages like uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography.
Rexstepper: a Reference Debugger for JavaScript Regular Expressions

Regular expressions are notoriously difficult to get right, with developers often having to resort to trial-and-error approaches. Even so, little attention has been given by the re search community to the development of effective debugging tools for regular expressions. We present RexStepper, a reference debugger for troubleshooting JavaScript regular expressions in the browser. Rexstepper is implemented on top of Rexref, a trusted reference implementation of JavaScript (ECMAScript 5) regular expressions, which works by transpiling the given reg-ular expression to a JavaScript function that recognises its expansions. We demonstrate the usefulness of Rexstepper by successfully using it to troubleshoot a benchmark of 18 faulty regular expressions obtained from the Stack Overflow and Stack Exchange websites.
Machine learning for English teaching: a novel evaluation method

This paper proposes a novel oral English scoring system based on machine learning. The system can be deployed on the end side (mobile app) through the internet and can be used to assist teachers in evaluating students' oral English pronunciation, fluency and the tunnel degree. An attention based Long-Short-Term Memory (LSTM) neural network is employed in the paper, which can process and analyse speech signals effectively. Meantime a large amount of training data is collected for network training. We compare the novel oral English evaluation system and the experts' evaluation results. The verification results show that the oral English evaluation system based on machine learning not only can achieve the ability of the English experts, but also has higher accuracy and can identify more oral pronunciation problems.
Sentiment Analysis of Students’ Perception towards the Implementation of Limited In-Person Learning: A Post-Pandemic Perspective

Purpose -This article is the second in a series on the opinions of students (also referred to as respondents).The preceding article in this series concentrated on sentiment analysis of students' online learning experiences.In this paper, the students' opinions about their learning condition and thoughts or feelings toward the implementation of limited inperson or face-to-face learning were investigated.Method -The sentiment analysis was utilized in the study particularly in obtaining the polarity, subjectivity, and presentation of visualization results.A total of 459 respondents answered the two open-ended questions were obtained 918 unstructured responses.A total of 27,316 words underwent data cleaning and data pre-processing.Text classification using sentiment analysis was performed.Results -The result revealed that the respondents' learning condition is combined with positive, negative, and neutral sentiments.The opinions to implement limited in-person learning were dominated by positive thoughts.Students' thoughts on the implementation of limited face-to-face learning were expressed based on their learning conditions, experiences, and expectations.Conclusion -Undoubtedly, the COVID-19 pandemic has challenged students' financial, social, and mental well-being.They have to deal with the major issues brought on by educational system disruptions, which decreased their interest and enthusiasm in learning.The learners' concern about returning to school was expressed as both positive and negative.Their negative views indicate valuing what is more important during a health crisis just like the Covid-19 pandemic.Recommendations -Educators need capability pieces of training where approaches and instructional methodologies are emphasized in the course syllabi which take health precautions into account for learners both in the face-to-face and online modality.Research Implications -The overall sentiments provided insights to administrators and educators of the learning institutions to re-think and make sound decisions in the execution of their plans in welcoming back the learners successfully.
Systematic review of training methods for conversational systems: the potential of datasets validated with user experience

The increasing maturity of artificial intelligence technologies such as Machine Learning algorithms, Natural Language Processing (NLP), Automatic Speech Recognition (ASR) and Natural Language generation are changing the way users interact with technology. Specifically, as voice interactions are becoming commonplace, it is important to understand how such systems are being trained. This systematic review investigates how human data is collected for training conversational agents, with specific interest on data sets directly obtained from human participation in real contexts of need and use. The work reported in this article was supported by PRISMA guidelines and search procedures were led in Scopus, Web of Science and ProQuest, in English and within the last 15-years (2005-2020), with pre-defined criteria to get a detailed holistic perspective of practices published until July 2020. From both search iterations, a total of 22 papers were considered for this review. The main contributions from these papers reveal a common use of learning from demonstration/observation and crowdsourcing methods, in system training and dataset cataloguing, alongside handwriting and sentence labelling and Wizard-of-Oz based studies.
Fixated Object Detection based on Saliency Prior in Traffic Scenes

From the perspective of Computer vision, both visual saliency and object detection have attracted hot attention in the field of traffic scene perception. However, these two tasks are often seen as independent missions, and their correlations have rarely been explored. In real driving scenarios, drivers mainly care about the salient objects closely related to the current driving task under the guidance of visual selective attention. This process highly integrates saliency perception and object detection, leading to efficient and quick decision-making, thus achieving safe driving. In this study, with reference to human drivers’ perception of traffic scenes, we focus on detecting fixated objects within the regions attracting the drivers’ attention. Firstly, we build a new fixated object detection dataset based on drivers’ fixations, which can serve as a benchmark for studying traffic object detection from the driver’s point of view. Then, we propose a fixated object detection model based on saliency prior, named FOD-Net. FOD-Net takes advantage of the predicted salient regions as saliency priors to guide the detection of the fixated objects that are closely relevant to the driving task, thus improving detection accuracy. Experimental results on the proposed dataset show that FOD-Net achieves a mAP value of 78.4% with small model parameters, which is higher than other state-of-the-art models. Our work combines the driver’s attention mechanism with object detection to narrow the gap between visual saliency and object detection in traffic scenes, showing potential supplemental or referential value for developing high-intelligence assisted/automatic driving systems. The dataset and code are available in https://github.com/YiShi701/Fixated-object-detection.
Politeness Strategies in WhatsApp Text Messaging

Lecturers and students frequently interact in both formal and semi-formal settings. Communication between students and professors must, of course, be maintained in such a way that it is always polite, even in informal situations. The availability of social media undoubtedly enhances contact between teachers and students. However, the use of social media has an impact on the language style of lecturers and students. This research aims to explain and examine the ethics of lecturers when responding to students’ text messages using the WhatsApp messaging application. Furthermore, this study examines violations from etiquette norms in short messages from the lecturer to the students. This study was qualitative in nature. The primary data of this research were the screenshots of interactions between instructors and students via the WhatsApp messaging application. This research also employed observation, note-taking, and interviews. According to the preliminary study, there are still numerous short messages between lecturers and students that do not match the courtesy standard. The unfulfilled value of politeness is the use of slang, impolite ways of expressing intentions or the use of inappropriate linguistic aspects.
WGT: Tools and algorithms for recognizing, visualizing, and generating Wheeler graphs

A Wheeler graph represents a collection of strings in a way that is particularly easy to index and query. Such a graph is a practical choice for representing a graph-shaped pangenome, and it is the foundation for current graph-based pangenome indexes. However, there are no practical tools to visualize or to check graphs that may have the Wheeler properties. Here, we present Wheelie, an algorithm that combines a renaming heuristic with a permutation solver (Wheelie-PR) or a Satisfiability Modulo Theory (SMT) solver (Wheelie-SMT) to check whether a given graph has the Wheeler properties, a problem that is NP-complete in general. Wheelie can check a variety of random and real-world graphs in far less time than any algorithm proposed to date. It can check a graph with 1,000s of nodes in seconds. We implement these algorithms together with complementary visualization tools in the WGT toolkit, available as open source software at https://github.com/Kuanhao-Chao/Wheeler_Graph_Toolkit.
Robust transitionless quantum driving: Concatenated approach

A concatenated approach is developed for achieving high-precision quantum control, accessible by using arbitrary time-dependent pulse shapes. The system evolution is robust against all kinds of errors without needing to satisfy adiabatic conditions.
PEAK: Privacy-Enhanced Incentive Mechanism for Distributed <i>K</i>-Anonymity in LBS

To motivate users' assistance for protecting others' location privacy by distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -anonymity in Location-Based Service (LBS), many incentive mechanisms have been proposed, where users obtain monetary compensation for their assistance. However, most existing distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -anonymity incentive mechanisms rely on trusted third parties and ignore users' malicious strategies, which destroys LBS's distributed structure as well as leads to users' privacy leakage and incentive ineffectiveness. To solve the above problems, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> nhanced incentive mech <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> nism for distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>K</u></i> -anonymity (PEAK). With determining the monetary transaction relationship and location transmission between users, PEAK enables the anonymous cloaking region construction without the trusted server. Meanwhile, PEAK devises role identification mechanism and accountability mechanism to restrain and punish malicious users, which protects users' location privacy and implements effective motivation on users' assistance. Theoretical analysis based on the game theory shows that PEAK constrains users' malicious strategies while satisfying individual rationality, computational efficiency, and satisfaction ratio. Extensive experiments based on the real-world dataset demonstrate that PEAK improves security and feasibility, especially reaching the success rate of anonymous cloaking region construction to more than 90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and decreasing the malicious users' utilities significantly.
Correlational study on novelty factor, immersive tendencies, purchase intention and memory in immersive VR e-commerce applications

E-commerce is a field that changed how consumers purchase and interact with products. Although, inherent limitations such as the difficulty of testing the products "first-hand" before a purchase can compromise consumers' trust in online purchases. Virtual Reality (VR) has been investigated as a tool to solve limitations in several fields and how we can harness its potential to improve the overall user experience. This study analysed how immersive VR (IVR) could solve these limitations by allowing consumers to test products beforehand. We have studied how the Novelty Factor (evaluated by the users' past VR experience) and Immersive Tendencies correlate with the users' Purchase Intention and Memory (how well they remember the product's characteristics). We have analysed a sample of 38 participants (21 males) from 18 to 28 years old. Participants experienced a refrigerator with an interactive touchscreen in an IVR setup and were guided through its functionalities. Results indicated that memory of the product's characteristics was positively correlated with how recently they experienced VR. No correlations were found in the female sample. A negative correlation between Purchase Intention and Memory of the product's characteristics was found in the male sample. We concluded that IVR applications could become helpful for both consumers and online shops in an e-commerce context regardless of the Novelty Factor and Immersive Tendencies of consumers. However, differences between genders should be further investigated.
adaptNMT: an open-source, language-agnostic development environment for neural machine translation

Abstract adaptNMT streamlines all processes involved in the development and deployment of RNN and Transformer neural translation models. As an open-source application, it is designed for both technical and non-technical users who work in the field of machine translation. Built upon the widely-adopted OpenNMT ecosystem, the application is particularly useful for new entrants to the field since the setup of the development environment and creation of train, validation and test splits is greatly simplified. Graphing, embedded within the application, illustrates the progress of model training, and SentencePiece is used for creating subword segmentation models. Hyperparameter customization is facilitated through an intuitive user interface, and a single-click model development approach has been implemented. Models developed by adaptNMT can be evaluated using a range of metrics, and deployed as a translation service within the application. To support eco-friendly research in the NLP space, a green report also flags the power consumption and kgCO $$_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> emissions generated during model development. The application is freely available ( http://github.com/adaptNMT ).
Codebase release 0.4 for pyBumpHunter

The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.
The Role of the Head in Creating the Socio-Psychological Climate of the Collective of the Educational Organization

The study defines the role of the head in creating the socio-psychological climate of the collective of an educational organization and develops recommendations for creating a favorable socio-psychological climate in the collective of an educational organization. The hypothesis of the study is the assumption that the head can play an important role in creating a socio-psychological climate of the collective of an educational organization. In a study involving 100 people aged 22 to 50 years, who are employees of the psychological and educational service, the following methods were used: analysis of the literature on the research problem, analysis of statistical data on the subject of the study. As a result of the study, it was revealed that there is a positive tendency for the head to change the usual leadership style as a factor that improves the socio-psychological climate of the staff of an educational.