Reviewerly/exHarmony huggingface dataset:
Playing Extensive Games with Learning of Opponent’s Cognition

Authors:
raw_author_name: Chanjuan Liu
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
raw_author_name: Jinmiao Cong
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
raw_author_name: Weihong Yao
raw_affiliation_string: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
raw_author_name: Enqiang Zhu
raw_affiliation_string: Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China

Abstract: Decision-making is a basic component of agents’ (e.g., intelligent sensors) behaviors, in which one’s cognition plays a crucial role in the process and outcome. Extensive games, a class of interactive decision-making scenarios, have been studied in diverse fields. Recently, a model of extensive games was proposed in which agent cognition of the structure of the underlying game and the quality of the game situations are encoded by artificial neural networks. This model refines the classic model of extensive games, and the corresponding equilibrium concept—cognitive perfect equilibrium (CPE)—differs from the classic subgame perfect equilibrium, since CPE takes agent cognition into consideration. However, this model neglects the consideration that game-playing processes are greatly affected by agents’ cognition of their opponents. To this end, in this work, we go one step further by proposing a framework in which agents’ cognition of their opponents is incorporated. A method is presented for evaluating opponents’ cognition about the game being played, and thus, an algorithm designed for playing such games is analyzed. The resulting equilibrium concept is defined as adversarial cognition equilibrium (ACE). By means of a running example, we demonstrate that the ACE is more realistic than the CPE, since it involves learning about opponents’ cognition. Further results are presented regarding the computational complexity, soundness, and completeness of the game-solving algorithm and the existence of the equilibrium solution. This model suggests the possibility of enhancing an agent’s strategic ability by evaluating opponents’ cognition.
Machine Learning-Based Rapid Epicentral Distance Estimation from a Single Station

Authors:
raw_author_name: Jingbao Zhu
raw_affiliation_string: 1Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China; 2Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin, China
raw_author_name: Wentao Sun
raw_affiliation_string: 3Railway Science and Technology Research and Development Center, China Academy of Railway Sciences Corporation Limited, Beijing, China
raw_author_name: Shanyou Li
raw_affiliation_string: 1Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China; 2Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin, China
raw_author_name: Jindong Song
raw_affiliation_string: 1Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin, China; 2Key Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin, China

Abstract: 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

Authors:
raw_author_name: M. Mariantoni
raw_affiliation_string: Institute for Quantum Computing, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada and Department of Physics and Astronomy, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada

Abstract: 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

Authors:
raw_author_name: Md Raf E Ul Shougat
raw_affiliation_string: Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA
raw_author_name: XiaoFu Li
raw_affiliation_string: LAB2701: Nonlinear Dynamics Laboratory, Atwood, Oklahoma 74827, USA
raw_author_name: Edmon Perkins
raw_affiliation_string: LAB2701: Nonlinear Dynamics Laboratory, Atwood, Oklahoma 74827, USA

Abstract: 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

Authors:
raw_author_name: Edna Dias Canedo
raw_affiliation_string: Department of Computer Science University of Brasília (UnB)  Brasília Federal District Brazil
raw_author_name: Emille Catarine Rodrigues Cançado
raw_affiliation_string: Department of Computer Science University of Brasília (UnB)  Brasília Federal District Brazil
raw_author_name: Ian Nery Bandeira
raw_affiliation_string: Department of Computer Science University of Brasília (UnB)  Brasília Federal District Brazil
raw_author_name: Pedro Henrique Teixeira Costa
raw_affiliation_string: Department of Computer Science University of Brasília (UnB)  Brasília Federal District Brazil
raw_author_name: Rodrigo Bonifácio
raw_affiliation_string: Department of Computer Science University of Brasília (UnB)  Brasília Federal District Brazil

Abstract: 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

Authors:
raw_author_name: Xiaofei Lu
raw_affiliation_string: Department of Applied Linguistics, The Pennsylvania State University, University Park, Pennsylvania, USA

Abstract: 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.
The Structure of Arguments from Deontic Authority and How to Successfully Attack Them

Authors:
raw_author_name: Michał Araszkiewicz
raw_affiliation_string: Department of Legal Theory, Jagiellonian University, Kraków, Poland
raw_author_name: Marcin Koszowy
raw_affiliation_string: Laboratory of The New Ethos, Warsaw University of Technology, Warsaw, Poland

Abstract: 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

Authors:
raw_author_name: Yi Yang
raw_affiliation_string: College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China; College of Information Science and Engineering, Hunan Women’s University, Changsha 410004, China
raw_author_name: Menghan Wu
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China

Abstract: 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

Authors:
raw_author_name: Yaxin Liu
raw_affiliation_string: School of Civil Engineering, Zhengzhou University, Zhengzhou, China
raw_author_name: Zheng Zhang
raw_affiliation_string: School of Civil Engineering, Zhengzhou University, Zhengzhou, China
raw_author_name: Yuhua Wang
raw_affiliation_string: School of Civil Engineering, Zhengzhou University, Zhengzhou, China
raw_author_name: Weilin Li
raw_affiliation_string: Huadian Zhengzhou Mechanical Design Institute Co, Ltd, Zhengzhou, China; School of Civil Engineering, Zhengzhou University, Zhengzhou, China

Abstract: 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

Authors:
raw_author_name: Michael Anger
raw_affiliation_string: Section for Translational Medical Ethics, Clinical Cooperation Unit Applied Tumor Immunity, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Christian Wendelborn
raw_affiliation_string: Section for Translational Medical Ethics, Clinical Cooperation Unit Applied Tumor Immunity, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany
raw_author_name: Christoph Schickhardt
raw_affiliation_string: Section for Translational Medical Ethics, Clinical Cooperation Unit Applied Tumor Immunity, National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ), Heidelberg, Germany

Abstract: 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

Authors:
raw_author_name: Anneke Zuiderwijk
raw_affiliation_string: Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands
raw_author_name: Frances Brazier
raw_affiliation_string: Faculty of Technology, Policy and Management, Delft University of Technology, Delft, the Netherlands

Abstract: 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.