Reviewerly/exHarmony huggingface dataset:
Multiple Factors Drive the Development of Scientific Thinking in Urban Primary School Students of China: FsQCA Analysis Based on the Ecological Systems Theory

Authors:
raw_author_name: Lin Lin
raw_affiliation_string: Harbin Normal University, Heilongjiang, China
raw_author_name: Xinyi Hu
raw_affiliation_string: Beijing Normal University, China
raw_author_name: Yu Wang
raw_affiliation_string: Beijing Normal University, China

Abstract: With the introduction and implementation of core literacy, scientific thinking (ST) has become an essential goal and key dimension of science teaching. At present, there is no agreement on how to cultivate students’ ST. This study took 238 sixth grade students from a public primary school in urban China as research sample, built a theoretical model of scientific thinking development based on the theory of the Bronfenbrenner’ ecological systems theory, and used multiple data to explore and analyze the impact path of ST development of primary school students using the fuzzy sets of qualitative comparative analysis method (fsQCA). The development of urban primary school students’ ST is the result of multiple factors at the level of individual drive, family environment, school teaching, and social resources. The result of data analysis showed that the influence path of ST development of urban primary school students driven by multiple factors includes three paths: parent participation leading, scientific practice leading, and home-school-community integration. We have interpreted the influence mechanism of each path in detail and put forward the enlightenment for science education policy and practice.
Real human touch: performer-facilitated touch enhances presence and embodiment in immersive performance

Authors:
raw_author_name: John Desnoyers-Stewart
raw_affiliation_string: School of Interactive Arts and Technology, Simon Fraser University, Burnaby, BC, Canada
raw_author_name: Ekaterina R. Stepanova
raw_affiliation_string: School of Interactive Arts and Technology, Simon Fraser University, Burnaby, BC, Canada
raw_author_name: Bernhard E. Riecke
raw_affiliation_string: School of Interactive Arts and Technology, Simon Fraser University, Burnaby, BC, Canada

Abstract: Despite being an embodied medium, virtual reality (VR) prioritizes vision and sound over the other senses. While touch has been demonstrated to foster a sense of presence and embodiment, most haptic research in VR focuses on uncanny vibration motors or limited experiences of touch with simple props. Meanwhile, immersive performances such as Eve 3.0 incorporate performer-facilitated touch in novel ways to evoke a complete and social experience of human touch in VR. In response, we conducted a mixed-methods study to investigate the experience of performer-facilitated touch in a 360° video segment from the immersive performance Eve 3.0 . Using a 3 × 2 factorial design, we compared touch from a diary prop and performer in festival and laboratory settings. We found that performer-facilitated touch increased realistic behaviours and questionnaire measures of social presence, embodiment, and tactile realism. The setting also had a significant effect with festival participants demonstrating significantly more behaviours indicating presence, particularly in the no-touch condition. Participant descriptions reveal that in addition to touch, a rich narrative and vivid visuals of social interaction were just as important in immersing participants in the experience and making them feel present. We find that participant experiences are a co-creation situated at the intersection of artefact and context that require a willing suspension of disbelief. The authentic setting and performance artefact afforded a deep understanding of the rich and complex experience of human touch in immersive performance.
Assisted design of data science pipelines

Authors:
raw_author_name: Sergey Redyuk
raw_affiliation_string: German Research Center for Artificial Intelligence (DFKI), Berlin, Germany
raw_author_name: Zoi Kaoudi
raw_affiliation_string: IT University of Copenhagen, Copenhagen, Denmark
raw_author_name: Sebastian Schelter
raw_affiliation_string: University of Amsterdam, Amsterdam, The Netherlands
raw_author_name: Volker Markl
raw_affiliation_string: Technical University Berlin, Berlin, Germany

Abstract: Abstract When designing data science (DS) pipelines, end-users can get overwhelmed by the large and growing set of available data preprocessing and modeling techniques. Intelligent discovery assistants (IDAs) and automated machine learning (AutoML) solutions aim to facilitate end-users by (semi-)automating the process. However, they are expensive to compute and yield limited applicability for a wide range of real-world use cases and application domains. This is due to (a) their need to execute thousands of pipelines to get the optimal one, (b) their limited support of DS tasks, e.g., supervised classification or regression only, and a small, static set of available data preprocessing and ML algorithms; and (c) their restriction to quantifiable evaluation processes and metrics, e.g., tenfold cross-validation using the ROC AUC score for classification. To overcome these limitations, we propose a human-in-the-loop approach for the assisted design of data science pipelines using previously executed pipelines. Based on a user query, i.e., data and a DS task, our framework outputs a ranked list of pipeline candidates from which the user can choose to execute or modify in real time. To recommend pipelines, it first identifies relevant datasets and pipelines utilizing efficient similarity search. It then ranks the candidate pipelines using multi-objective sorting and takes user interactions into account to improve suggestions over time. In our experimental evaluation, the proposed framework significantly outperforms the state-of-the-art IDA tool and achieves similar predictive performance with state-of-the-art long-running AutoML solutions while being real-time, generic to any evaluation processes and DS tasks, and extensible to new operators.
The case for open research in entomology: Reducing harm, refining reproducibility and advancing insect science

Authors:
raw_author_name: Charles Fox
raw_affiliation_string: Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, Indiana, USA

Abstract: Abstract Open research is an increasingly developed and crucial framework for the advancement of science and has seen successful adoption across a broad range of disciplines. Entomology has, however, been slow to adopt these practices compared to many adjacent fields despite ethical and practical imperatives to do so. The grand challenges facing entomology in the 21st century require the synthesis of evidence at global scales, necessitating open sharing of data and research at a pace and scale incompatible with the slow adoption of open research practices. Open science also plays a vital role in fostering trust in research and maximizing use of research outputs, which is ethically crucial for reducing harms to insects. We outline these imperatives and how open research practices can enhance entomological research across a range of contexts. We also highlight the holistic nature of open science across the full research lifecycle through several specific examples of open research practices, which can be adopted easily by individual entomologists. We do, however, argue that the responsibility of promoting, integrating and encouraging open research is most crucially held by publishers, including scholarly societies, which have leveraged widespread adoption in adjacent fields. Entomology must advance quickly to become a leading discipline in the open research transition.
Open‐domain event schema induction via weighted attentive hypergraph neural network

Authors:
raw_author_name: Hao Wang
raw_affiliation_string: School of Computer Engineering and Science Shanghai University  Shanghai China
raw_author_name: Xiangfeng Luo
raw_affiliation_string: School of Computer Engineering and Science Shanghai University  Shanghai China

Abstract: Summary Event schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.
A classification and recognition algorithm of key figures in public opinion integrating multidimensional similarity and K-shell based on supernetwork

Authors:
raw_author_name: Guanghui Wang
raw_affiliation_string: Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China
raw_author_name: Yushan Wang
raw_affiliation_string: The Institute for Sustainable Development, Macau University of Science and Technology, Macau, China
raw_author_name: Kaidi Liu
raw_affiliation_string: Qingdao Institute of Humanities and Social Sciences, Shandong University, Qingdao, China
raw_author_name: Shu Sun
raw_affiliation_string: School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou, China

Abstract: Abstract In online public opinion events, key figures are crucial to the formation and diffusion of public opinion, to the evolution and dissemination of topics, and to the guidance and transformation of the direction of public opinion. Based on the four-dimensional public opinion communication supernetwork (social-psychology-opinion-convergent), this study proposes a classification and recognition algorithm of key figures in online public opinion that integrates multidimensional similarity and K-shell to identify the key figures with differentiation in online public opinion events. The research finds that the evolutionary process of public opinion events is the joint action of key figures with different roles. The opinion leader is the key figure in the global communication of public opinion. The focus figure is the core figure that promotes the dissemination of public opinion on local subnetworks. The communication figure is the “bridge” node in the cross-regional communication of public opinion. Through the algorithm verification of the case “China Eastern Airlines Passenger Plane Crash Event”, we find that the algorithm proposed in this paper has advantages in feasibility, sensitivity, and effectiveness, compared with traditional algorithms such as CI, forwarding volume, degree centrality, K-shell, and multidimensional similarity. The classification and recognition algorithm proposed in this study can not only identify multirole key figures simultaneously but also improve the recognition granularity and eliminate the interference of core-like nodes.
A cooperative stochastic configuration network based on differential evolutionary sparrow search algorithm for prediction

Authors:
raw_author_name: Wenhao Fang
raw_affiliation_string: College of Information Science and Technology, Donghua University, Shanghai People's Republic of China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai People's Republic of China
raw_author_name: Bo Shen
raw_affiliation_string: College of Information Science and Technology, Donghua University, Shanghai People's Republic of China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai People's Republic of China
raw_author_name: Anqi Pan
raw_affiliation_string: College of Information Science and Technology, Donghua University, Shanghai People's Republic of China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai People's Republic of China
raw_author_name: Lei Zou
raw_affiliation_string: College of Information Science and Technology, Donghua University, Shanghai People's Republic of China; Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai People's Republic of China
raw_author_name: Baoye Song
raw_affiliation_string: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao People's Republic of China

Abstract: Stochastic configuration network (SCN) is a powerful prediction model whose performance is significantly influenced by the configuration of the network parameters. To improve the prediction accuracy of the network, a cooperative stochastic configuration network (CSCN) based on a novel differential evolutionary sparrow search algorithm (DESSA), termed as DESSA-CSCN, is proposed. In the CSCN, the number of hidden layer nodes is adaptively adjusted according to the number of iterations, and the parameters of hidden nodes are cooperatively optimized by using a population-based metaheuristic algorithm. A sparse matrix is introduced to mitigate parameter overfitting caused by the increased number of hidden layer nodes. During parameter optimization, the fitness function is constructed by using the supervision mechanism of the SCN, and the DESSA is utilized as the metaheuristic algorithm to update the weights and biases. In order to verify the effectiveness of the DESSA-CSCN, several simulation experiments have been conducted. The performance of the DESSA is evaluated by the CEC2017 test suit, and the simulation results show that the DESSA exhibits better convergence accuracy and can jump out of local optima more effectively than other algorithms. The performance of the DESSA-CSCN is evaluated by 4 datasets from KEEL, and the simulation results indicate that the DESSA-CSCN achieves better prediction accuracy and faster prediction speed than other models.
Out-of-Distribution Detection in Dependent Data for Cyber-Physical Systems with Conformal Guarantees

Authors:
raw_author_name: Ramneet Kaur
raw_affiliation_string: University of Pennsylvania, Philadelphia, USA
raw_author_name: Yahan Yang
raw_affiliation_string: University of Pennsylvania, Philadelphia, USA
raw_author_name: Oleg Sokolsky
raw_affiliation_string: University of Pennsylvania, Philadelphia, USA
raw_author_name: Insup Lee
raw_affiliation_string: University of Pennsylvania, Philadelphia, USA

Abstract: Uncertainty in the predictions of learning-enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning-enabled component (LEC) is one source of uncertainty in the LEC’s predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher’s method leads to the proposed detector CODiT with bounded false alarms. CODiT performs OOD detection on fixed-length windows of consecutive time-series datapoints by using Fisher value of the input window. We further propose performing OOD detection on real-time time-series traces of variable lengths with bounded false alarms. This can be done by using CODiT to compute Fisher values of the sliding windows in the input trace and combining these values by a merging function. Merging functions such as Harmonic Mean, Arithmetic Mean, Geometric Mean, and Bonferroni Method, etc. can be used to combine Fisher values of the sliding windows in the input trace, and the combined value can be used for OOD detection on the trace with bounded false alarm rate guarantees. We illustrate the efficacy of CODiT by achieving state-of-the-art results in two case studies for OOD detection on fixed-length windows. The first one is on an autonomous driving system with perception (or vision) LEC. The second case study is on a medical CPS for walking pattern or GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject’s body. For OOD detection on variable length traces, we consider the same case studies on the autonomous driving system and medical CPS for GAIT analysis. We report our results with four merging functions on the Fisher values computed by CODiT on the sliding windows of the input trace. We also compare the false alarm rate guarantees by these four merging functions in the autonomous driving system case study. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD.
Reconstructing bifurcation diagrams of chaotic circuits with reservoir computing

Authors:
raw_author_name: Yao Du
raw_affiliation_string: School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China
raw_author_name: Huawei Fan
raw_affiliation_string: School of Science, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Xuan Wang
raw_affiliation_string: School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China
raw_author_name: Xingang Wang
raw_affiliation_string: School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China

Abstract: Model-free reconstruction of bifurcation diagrams of Chua's circuits using the technique of parameter-aware reservoir computing is investigated. We demonstrate that (1) reservoir computer can be utilized as a noise filter to restore the system dynamics from noisy signals; (2) for a single Chua circuit, a machine trained by the noisy time series measured at several sampling states is capable of reconstructing the whole bifurcation diagram of the circuit with a high precision; and (3) for two coupled chaotic Chua circuits with mismatched parameters, the machine trained by the noisy time series measured at several coupling strengths is able to anticipate the variation of the synchronization degree of the coupled circuits with respect to the coupling strength over a wide range. Our studies verify the capability of the technique of parameter-aware reservoir computing in learning the dynamics of chaotic circuits from noisy signals, signifying the potential application of this technique in reconstructing the bifurcation diagram of real-world chaotic systems.
Bidirectional Complementary Correlation-Based Multimodal Aspect-Level Sentiment Analysis

Authors:
raw_author_name: Yujie Xiong
raw_affiliation_string: Shanghai University of Engineering and Technology, China

Abstract: Aspect-based sentiment analysis is the key to natural language processing, and it focuses on the polarity of emotions associated with specific text aspects. Traditional models that combine text and visual data tend to ignore the deeper interconnections between patterns. To solve this problem, the authors propose a multimodal sentiment-oriented analysis (BiCCM-ABSA) model based on bidirectional complementary correlation. The model utilizes text-image synergy through a novel cross-modal attention mechanism to align text with image features. With the transformer architecture, it is not only a simple fusion, but also ensures the complex alignment of multi-modal features and gating mechanisms. Experiments were conducted on the Twitter-15 and Twitter-17 datasets, achieving 69.28 accuracy and 67.54% F1 score, respectively. The experimental results demonstrate the advantages of BiCCM-ABSA, the bidirectional approach of the model and the effective cross-modal correlation set a new benchmark in the field of multimodal emotion recognition, providing insights beyond traditional single-modal analysis.
Scalable approach to quantum simulation via projection-based embedding

Authors:
raw_author_name: Alexis Ralli
raw_affiliation_string: Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom
raw_author_name: Peter V. Coveney
raw_affiliation_string: Centre for Computational Science, Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom; Informatics Institute, University of Amsterdam, Amsterdam 1098 XH, Netherlands

Abstract: Owing to the computational complexity of electronic structure algorithms running on classical digital computers, the range of molecular systems amenable to simulation remains tightly circumscribed even after many decades of work. Many believe quantum computers will transcend such limitations although in the current era the size and noise of these devices militates against significant progress. Here we describe a chemically intuitive approach that permits a subdomain of a molecule's electronic structure to be calculated accurately on a quantum device, while the rest of the molecule is described at a lower level of accuracy using density functional theory running on a classical computer. We demonstrate that this approach produces improved results for molecules that cannot be simulated fully on current quantum computers but which can be resolved classically at a cheaper level of approximation. The algorithm is tunable, so that the size of the quantum simulation can be adjusted to run on available quantum resources. Therefore, as quantum devices become larger, this method will enable increasingly large subdomains to be studied accurately.
On the (relation between) efficiency and secret key rate of QKD

Authors:
raw_author_name: Georgi Bebrov
raw_affiliation_string: Telecommunications Department, Technical University of Varna, 1 Studentska Street, 9010, Varna, Bulgaria

Abstract: The processes of evaluation and comparison play a vital role in the development of a scientific field. In the field of quantum cryptography (especially quantum key distribution, QKD), the so-called secret key rate is used for characterizing the performance of a protocol (scheme). However the current definition of this quantity is incomplete. It does not consider the classical communication process taking place in a QKD protocol. There exists a quantity that involves all the procedures (resources) in a communication process: it is the efficiency (total efficiency). This paper reports a definition of this parameter. Also the relation between the total efficiency and key rate is found. By means of this relation, the total secret key rate of a QKD protocol is expressed. An application of the total key rate is demonstrated: the original twin-field QKD (TF-QKD) is evaluated in terms of this rate. The paper also shows a comparison between the total key rate and the standard key rate of a TF-QKD.