Reviewerly/exHarmony huggingface dataset:
Machine Learning-Based Rapid Epicentral Distance Estimation from a Single Station

Abstract Rapid epicentral distance estimation is of great significance for earthquake early warning (EEW). To rapidly and reliably predict epicentral distance, we developed machine learning models with multiple feature inputs for epicentral distance estimation using a single station and explored the feasibility of three machine learning methods, namely, Random Forest, eXtreme Gradient Boosting, and Support Vector Machine, for epicentral distance estimation. We used strong-motion data recorded by the Japanese Kyoshin network within a range of 1° (∼112 km) from the epicenter to train machine learning models. We used 30 features extracted from the P-wave signal as inputs to the machine learning models and the epicentral distance as the prediction target of the models. For the same test data set, within 0.1–5 s after the P-wave arrival, the epicentral distance estimation results of these three machine learning models were similar. Furthermore, these three machine learning methods can obtain smaller mean absolute errors and root mean square errors, as well as larger coefficients of determination (R2), for epicentral distance estimation than traditional EEW epicentral distance estimation methods, indicating that these three machine learning models can effectively improve the accuracy of epicentral distance estimation to a certain extent. In addition, we analyzed the importance of different features as inputs to machine learning models using SHapley additive exPlanations. We found that using the top 15 important features as inputs, these three machine learning models can also achieve good results for epicentral distance estimation. Based on our results, we inferred that the machine learning models for estimating epicentral distance proposed in this study are meaningful in EEW.
Resonant Schrödinger cat states in circuit quantum electrodynamics

We propose a fast scheme to generate Schr\"odinger cat states in a superconducting resonator using a continuously driven qubit without resorting to the dispersive regime, two-photon drives, or engineered two-photon dissipation. We provide analysis for when the qubit is on and off resonance from the drive. We extend our analysis to account for a third level in a weakly anharmonic qutrit. We also discuss the case of a strongly anharmonic qutrit. Throughout the paper, we corroborate our analytical results with numerical simulations in the presence of energy relaxation and dephasing of the qubit and resonator using realistic experimental parameters.
Multiplex-free physical reservoir computing with an adaptive oscillator

Nonlinear oscillators can often be used as physical reservoir computers, in which the oscillator's dynamics simultaneously performs computation and stores information. Typically, the dynamic states are multiplexed in time, and then machine learning is used to unlock this stored information into a usable form. This time multiplexing is used to create virtual nodes, which are often necessary to capture enough information to perform different tasks, but this multiplexing procedure requires a relatively high sampling rate. Adaptive oscillators, which are a subset of nonlinear oscillators, have plastic states that learn and store information through their dynamics in a human readable form, without the need for machine learning. Highlighting this ability, adaptive oscillators have been used as analog frequency analyzers, robotic controllers, and energy harvesters. Here, adaptive oscillators are considered as a physical reservoir computer without the cumbersome time multiplexing procedure. With this multiplex-free physical reservoir computer architecture, the fundamental logic gates can be simultaneously calculated through dynamics without modifying the base oscillator.
Using Design Thinking to break social barriers: An experience report with former inmates

Abstract Design Thinking techniques have been widely used in software requirements elicitation to understand the necessities of stakeholders and end‐users. However, there is a lack of evidence of their effectiveness when applied to guide the development process of a system targeting vulnerable populations. What are the implications of using Design Thinking techniques to elicit requirements in a community of former inmates—and what would be the benefits of and challenges in this deployment? In this paper, we report our experience of using Design Thinking for requirements elicitation of a mobile application customized for the former inmates of the Brazilian prison system and their families. We applied techniques such as Brainstorming, Stakeholder Mapping, Personas Creation, Rapid Ethnography, and Interviews to obtain relevant data and create several prototypes. These techniques contribute to the development of an uncommon application that aims to help the reintegration process of former inmates into society. Our results validate the initial hypothesis that such techniques, when applied to a sensitive context, assist product development that meets the end‐users' needs by creating a higher quality product. The main limitation of the research was the lack of access to low‐literacy end‐users and/or former inmates without previous experience using mobile devices.
L2 English speaking syntactic complexity: Data preprocessing issues, reliability of automated analysis, and the effects of proficiency, L1 background, and topic

Abstract The effects of learner‐ and task‐related variables on second language (L2) writing syntactic complexity (SC) have been extensively investigated. However, previous research has rarely assessed the reliability of computational tools for analyzing the SC of L2 spoken production, and we know less about the effects of such variables on L2 speaking SC. Using data from the International Corpus Network of Asian Learners of English, this study explores data preprocessing issues for preparing L2 English speech samples for automated SC analysis, evaluates the reliability of L2 Syntactic Complexity Analyzer on preprocessed L2 English speech samples, and examines the effects of proficiency, first language (L1) background, and topic on L2 speaking SC. Our manual analysis of 30 random speech samples identified several issues that can be addressed through preprocessing to improve the accuracy of automated SC analysis. Results from multiple linear mixed‐effects models revealed significant effects of proficiency, L1 background, and topic on the mean length of clause, the number of complex AS‐units per AS‐unit, and the number of dependent clauses and complex nominals per clause in L2 learners’ spoken production. Our findings have useful implications for L2 speaking pedagogy and assessment as well as future L2 speaking SC research.
Organizational Changes in Adopting Agile Approaches: A Systematic Literature Review

Although agile approaches to project management were originally developed for use by small software development teams, agile methods quickly began to be used by entire department processes and, in some cases, to the entire organization. Despite this quick adoption, there is a lack of studies seeking a better understanding of the changes that an organization needs to carry out for agile implementation at the organizational level. Therefore, a systematic literature review was conducted to organize and synthesize knowledge in this field. The main changes reported by these studies were consolidated into four major dimensions, that together, provided a consolidated view on the topic. In addition to contributing to the theory of agile approaches, it is also intended that this research will support the organizations that plan to undertake this journey of transformation, helping them plan the actions needed to achieve the desired results more consistently.
The Structure of Arguments from Deontic Authority and How to Successfully Attack Them

Abstract Despite increasing interest in studying arguments from deontic authority of the general form “(1) $$\delta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>δ</mml:mi> </mml:math> is a deontic authority in institution $$\varOmega$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Ω</mml:mi> </mml:math> ; (2) according to $$\delta$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>δ</mml:mi> </mml:math> , I should do $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> , C : therefore, (3) I should do $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> ”, the state of the art models are not capable of grasping their complexity. The existing sets of critical questions assigned to this argumentation scheme seem to conflate two problems: whether a person is subject to an authority of an institution in the first place and whether the command issued within the context of a particular institution is eventually binding. For this reason, we introduce (1) a set of Basic Critical Questions to scrutinize the former issue, and (2) a set of more detailed questions related to specific features, also referred to as “parameters”, of institutional environments (Intra-Institutional Critical Questions). We identify major elements of institutional environments in which authoritative utterances are made and the crucial parameters of arguments from deontic authority. The selected evidence from the decisions of the Polish Supreme Administrative Court helps us show how these parameters may be used to reconstruct subtypes of this argument scheme, with their associated sets of critical questions. In specific institutional contexts, such detailed schemes are capable of grasping the complexity of appeals to deontic authority and thus should be used rather than general schemes. The reconstruction of argumentation schemes with critical questions shows how particular arguments may successfully be attacked.
Unveiling the Dynamics of Extrinsic Motivations in Shaping Future Experts’ Contributions to Developer Q&amp;A Communities

Developer question and answering communities rely on experts to provide helpful answers. However, these communities face a shortage of experts. To cultivate more experts, the community needs to quantify and analyze the rules of the influence of extrinsic motivations on the ongoing contributions of those developers who can become experts in the future (potential experts). Currently, there is a lack of potential expert-centred research on community incentives. To address this gap, we propose a motivational impact model with self-determination theory-based hypotheses to explore the impact of five extrinsic motivations (badge, status, learning, reputation, and reciprocity) for potential experts. We develop a status-based timeline partitioning method to count information on the sustained contributions of potential experts from Stack Overflow data and propose a multifactor assessment model to examine the motivational impact model to determine the relationship between potential experts’ extrinsic motivations and sustained contributions. Our results show that (i) badge and reciprocity promote the continuous contributions of potential experts while reputation and status reduce their contributions; (ii) status significantly affects the impact of reciprocity on potential experts’ contributions; (iii) the difference in the influence of extrinsic motivations on potential experts and active developers lies in the influence of reputation, learning, and status and its moderating effect. Based on these findings, we recommend that community managers identify potential experts early and optimize reputation and status incentives to incubate more experts.
Research on fault detection and diagnosis of carbon dioxide heat pump systems in buildings based on transfer learning

Nowadays, carbon dioxide (CO2) heat pump systems are widely used in fields such as refrigeration, heating, and hot water. However, the faulty operation of heat pump equipment may lead to higher energy consumption and even problems such as damaged components and systems that do not work properly. It is necessary to establish a fault detection and diagnosis (FDD) model for CO2 heat pumps to reduce energy waste caused by faults and restore the system to the expected performance level. Therefore, this study proposes four transfer learning methods based on feature transformations to establish an FDD model for CO2 heat pump systems. The models are also optimized by feature parameter screening, classifier tuning and training set occupancy. The results show that the accuracy of the methods based on transfer component analysis (TCA), joint distribution adaptation (JDA) and balanced distribution adaptation (BDA) increased from 97.79% to 98.89%, and the accuracy of the method based on correlation alignment (CORAL) increased from 15.67% to 74.23%. The runtime of the four methods TCA, JDA, BDA and CORAL are 0.07s,5.37s, 3.30s and 0.03s respectively. It is demonstrated that the transfer learning-based method proposed in this study can effectively diagnose common faults in CO2 heat pump systems.
German funders’ data sharing policies—A qualitative interview study

Background Data sharing is commonly seen as beneficial for science but is not yet common practice. Research funding agencies are known to play a key role in promoting data sharing, but German funders’ data sharing policies appear to lag behind in international comparison. This study aims to answer the question of how German data sharing experts inside and outside funding agencies perceive and evaluate German funders’ data sharing policies and overall efforts to promote data sharing. Methods This study is based on sixteen guided expert interviews with representatives of German funders and German research data experts from stakeholder organisations, who shared their perceptions of German’ funders efforts to promote data sharing. By applying the method of qualitative content analysis to our interview data, we categorise and describe noteworthy aspects of the German data sharing policy landscape and illustrate our findings with interview passages. Results We present our findings in five sections to distinguish our interviewees’ perceptions on a) the status quo of German funders’ data sharing policies, b) the role of funders in promoting data sharing, c) current and potential measures by funders to promote data sharing, d) general barriers to those measures, and e) the implementation of more binding data sharing requirements. Discussion and conclusion Although funders are perceived to be important promoters and facilitators of data sharing throughout our interviews, only few German funding agencies have data sharing policies in place. Several interviewees stated that funders could do more, for example by providing incentives for data sharing or by introducing more concrete policies. Our interviews suggest the academic freedom of grantees is widely perceived as an obstacle for German funders in introducing mandatory data sharing requirements. However, some interviewees stated that stricter data sharing requirements could be justified if data sharing is a part of good scientific practice.
Identifying the most important facilitators of open research data sharing and reuse in Epidemiology: A mixed-methods study

To understand how open research data sharing and reuse can be further improved in the field of Epidemiology, this study explores the facilitating role that infrastructural and institutional arrangements play in this research discipline. It addresses two research questions: 1) What influence do infrastructural and institutional arrangements have on open research data sharing and reuse practices in the field of Epidemiology? And 2) how could infrastructural and institutional instruments used in Epidemiology potentially be useful to other research disciplines? First, based on a systematic literature review, a conceptual framework of infrastructural and institutional instruments for open research data facilitation is developed. Second, the conceptual framework is applied in interviews with Epidemiology researchers. The interviews show that two infrastructural and institutional instruments have a very high influence on open research data sharing and reuse practices in the field of Epidemiology, namely (a) access to a powerful search engine that meets open data search needs and (b) support by data stewards and data managers. Third, infrastructural and institutional instruments with a medium, high, or very high influence were discussed in a research workshop involving data stewards and research data officers from different research fields. This workshop suggests that none of the influential instruments identified in the interviews are specific to Epidemiology. Some of our findings thus seem to apply to multiple other disciplines. This study contributes to Science by identifying field-specific facilitators and challenges for open research data in Epidemiology, while at the same time revealing that none of the identified influential infrastructural and institutional instruments were specific to this field. Practically, this implies that open data infrastructure developers, policymakers, and research funding organizations may apply certain infrastructural and institutional arrangements to multiple research disciplines to facilitate and enhance open research data sharing and reuse.
Local business environment and firm innovation: evidence from China

Purpose This paper aims to investigate the influence of the regional business environment on local firm innovation, considering various dimensions such as administrative, financial and legal environments. Design/methodology/approach Multiple regression analysis is employed to analyze archival data for firms listed on Chinese stock markets. Findings We find that the optimizations of the administrative and financial environments positively affect firm innovation, whereas the legal environment does not exert a similar impact. Our analysis also reveals that the business environment’s optimization significantly influences innovation in firms that are small, non-state-owned and operating in high-tech industries. Furthermore, the business environment acts as a moderating variable in the relationship between firm innovation and firm value. Research limitations/implications This study contributes to a more comprehensive understanding of institutional-level determinants of firm innovation, highlighting the nuances of the legal environment and the importance of context-specific analysis, especially in emerging markets like China. Practical implications Developing countries can significantly enhance firm innovation by improving the business environment, including the optimization of administrative and financial systems, reducing transaction costs and ensuring capital supply. Tailored legal frameworks and alternative institutional strategies may also be explored. Social implications This study explicitly emphasizes the governmental role in promoting firm innovation, shedding light on policy formulation and strategic alignment with local administrative policies. Originality/value To the best of our knowledge, this paper is the first to explore the relationship between the business environment and firm innovation using World Bank indicators in an emerging market context, providing novel insights into the unique dynamics of legal, financial and administrative sub-environments.
Understanding user perceptions toward marketing in the metaverse

Purpose This study develops a comprehensive research model and investigates the significant factors affecting positive marketing outcomes in the Metaverse through perceived social benefits and trust. Design/methodology/approach The authors propose a new research model based on social exchange theory (SET) and examine the impact of cost and reward factors. Using 327 survey samples collected from current Metaverse users in South Korea, dual-stage analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and an artificial neural network (ANN) were employed to test the study’s hypotheses. Findings The results showed that perceived social benefit and trust had significant mediating effects on marketing outcomes, such as loyalty to the seller, product/service attitude, and purchase intention. All antecedents, except perceived performance risk, had a crucial impact on the two mediators. The most interesting finding of this study is the positive influence of knowledge-seeking efforts on perceived social benefits. Originality/value This study is the first empirical research to examine the effectiveness of marketing in the Metaverse. It also proposes a new theoretical model based on SET to investigate users’ behavioral intentions regarding marketing in the Metaverse, and confirms its explanatory power. Moreover, the results of this study also offer suggestions to brands on how to market to consumers in the Metaverse.
Argo data anomaly detection based on transformer and Fourier transform

Argo data is multidimensional observational data from ocean floats, which has long been plagued by data anomalies. Currently, the anomaly detection problem in Argo data still faces many challenges, as anomalies may involve complex relationships between multiple variables, leading to suboptimal performance with traditional machine learning methods. Thanks to the advancement of deep learning, it has become the predominant methodology for anomaly detection, demonstrating notable performance. The Transformer model has shown significant potential in the field of data anomaly detection, with its core Self-Attention mechanism capable of learning relationships between variables. We introduce Fast Fourier Transform (FFT) into the Transformer model, enabling the model to better capture periodic patterns and complex relationships in multivariate data, learning normal data patterns to improve the method for Argo data anomaly detection. Through experiments conducted on three public datasets and the Argo dataset, the enhanced model outperforms the original model in terms of performance. This also demonstrates the potential of FFT in multidimensional data anomaly detection, providing new insights into addressing anomaly detection challenges in real-world complex datasets.
Integrating Egocentric and Robotic Vision for Object Identification Using Siamese Networks and Superquadric Estimations in Partial Occlusion Scenarios

This paper introduces a novel method that enables robots to identify objects based on user gaze, tracked via eye-tracking glasses. This is achieved without prior knowledge of the objects’ categories or their locations and without external markers. The method integrates a two-part system: a category-agnostic object shape and pose estimator using superquadrics and Siamese networks. The superquadrics-based component estimates the shapes and poses of all objects, while the Siamese network matches the object targeted by the user’s gaze with the robot’s viewpoint. Both components are effectively designed to function in scenarios with partial occlusions. A key feature of the system is the user’s ability to move freely around the scenario, allowing dynamic object selection via gaze from any position. The system is capable of handling significant viewpoint differences between the user and the robot and adapts easily to new objects. In tests under partial occlusion conditions, the Siamese networks demonstrated an 85.2% accuracy in aligning the user-selected object with the robot’s viewpoint. This gaze-based Human–Robot Interaction approach demonstrates its practicality and adaptability in real-world scenarios.