Reviewerly/exHarmony huggingface dataset:
INFORMATE: When Are the Data?

Authors:
raw_author_name: Ted Habermann
raw_affiliation_string: Metadata Game Changers
raw_author_name: Jamaica Jones
raw_author_name: Howard Ratner
raw_author_name: Tara Packer

Abstract: In a recent Upstream blog post we explored where data connected to papers funded by several U.S. Federal Agencies are published. Different data sharing practices across these agencies led to very different distributions of datasets across various repositories. We used CHORUS reports that combine linked article and dataset metadata as input for that work.
A design team’s perspective-mixing for shared empathic understandings

Authors:
raw_author_name: Eunhye Kim
raw_affiliation_string: School of Engineering Education, Purdue University, West Lafayette, IN, USA
raw_author_name: Robin S. Adams
raw_affiliation_string: School of Engineering Education, Purdue University, West Lafayette, IN, USA
raw_author_name: Senay Purzer
raw_affiliation_string: School of Engineering Education, Purdue University, West Lafayette, IN, USA

Abstract: Empathic design involves two social practices – collaboration with users and intra team collaboration forshared empathic understandings. This study is focused on the latter phenomenon, aiming to characterise a social mechanism underlying it. To achieve this objective, we used a framework of perspective-mixing in empathic design, including three distinct perspectives: first, second, and third-person perspectives. With this framework, we traced how team members share and integrate individuals' user knowledge and insights to develop and apply their shared empathic understandings. We conducted conversation analysis to examine one design team's conversations over a design journey from need-finding to initial ideation to prototyping and testing. To characterise this team's perspective-mixing during their conversations, we analysed how team members took and combined the three perspectives within and between their utterances (perspective transitions). Our analysis revealed that the team used the third-person perspective as an underlying mechanism for perspective-mixing. In addition, the nature of perspective-mixing was distinct for specific design activities – the transitions involving the first and second-person perspectives were prominent during need-finding and initial ideation, while the third-person perspective was predominant during prototyping and testing. Based on these findings, we discuss what and how perspective-mixing can support intrateam collaboration for shared empathic understandings.
Physics-informed differentiable method for piano modeling

Authors:
raw_author_name: Stefano Fasciani
raw_affiliation_string: Department of Musicology, University of Oslo, Oslo, Norway

Abstract: Numerical emulations of the piano have been a subject of study since the early days of sound synthesis. High-accuracy sound synthesis of acoustic instruments employs physical modeling techniques which aim to describe the system’s internal mechanism using mathematical formulations. Such physical approaches are system-specific and present significant challenges for tuning the system’s parameters. In addition, acoustic instruments such as the piano present nonlinear mechanisms that present significant computational challenges for solving associated partial differential equations required to generate synthetic sound. In a nonlinear context, the stability and efficiency of the numerical schemes when performing numerical simulations are not trivial, and models generally adopt simplifying assumptions and linearizations. Artificial neural networks can learn a complex system’s behaviors from data, and their application can be beneficial for modeling acoustic instruments. Artificial neural networks typically offer less flexibility regarding the variation of internal parameters for interactive applications, such as real-time sound synthesis. However, their integration with traditional signal processing frameworks can overcome this limitation. This article presents a method for piano sound synthesis informed by the physics of the instrument, combining deep learning with traditional digital signal processing techniques. The proposed model learns to synthesize the quasi-harmonic content of individual piano notes using physics-based formulas whose parameters are automatically estimated from real audio recordings. The model thus emulates the inharmonicity of the piano and the amplitude envelopes of the partials. It is capable of generalizing with good accuracy across different keys and velocities. Challenges persist in the high-frequency part of the spectrum, where the generation of partials is less accurate, especially at high-velocity values. The architecture of the proposed model permits low-latency implementation and has low computational complexity, paving the way for a novel approach to sound synthesis in interactive digital pianos that emulates specific acoustic instruments.
The Use of Pronoun Interpretation Biases in L1 Spanish: The Role of Language Proficiency, Exposure, and Use in Heritage Speakers

Authors:
raw_author_name: Carla Contemori
raw_affiliation_string: Associate Professor, Department of Languages and Linguistics, University of Texas at El Paso https://dx.doi.org/601939 El Paso, Texas USA

Abstract: Abstract In an offline sentence comprehension task, we test the interpretation biases for null and overt pronouns and anaphoric and cataphoric pronouns in heritage speakers ( HS s) whose first language (L1) is Spanish (L2 English). Previous studies have shown that Spanish HS s can interpret overt anaphoric pronouns differently than monolingual speakers. A question that remains unexplored is how HS s interpret cataphoric pronouns and how L1 experience affects pronoun resolution biases in these individuals. We recruited 48 HS s and 48 monolingual Spanish speakers. The results show that pronoun interpretation in HS s differs from monolingual biases, with HS s demonstrating a higher subject antecedent preference for anaphoric and cataphoric pronouns. The results further show that a continuous measure of language proficiency, exposure, and use may contribute to explaining pronoun interpretation biases in heritage Spanish. We discuss the findings in light of recent research demonstrating that language experience can contribute to shape discourse patterns.
End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Authors:
raw_author_name: Homayoun Najjaran
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada; Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada

Abstract: Abstract Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.
Wansing's bi-intuitionistic logic: semantics, extension and unilateralisation

Authors:
raw_author_name: Juan C. Agudelo-Agudelo
raw_affiliation_string: Instituto de Matemáticas, Universidad de Antioquia, Medellín, Colombia

Abstract: The well-known algebraic semantics and topological semantics for intuitionistic logic (Int) is here extended to Wansing's bi-intuitionistic logic (2Int). The logic 2Int is also characterised by a quasi-twist structure semantics, which leads to an alternative topological characterisation of 2Int. Later, notions of Fregean negation and of unilateralisation are proposed. The logic 2Int is extended with a ‘Fregean negation’ connective ∼, obtaining 2Int∼, and it is showed that the logic N4⋆ (an extension of Nelson's paraconsistent logic) results to be the unilateralisation of 2Int∼ via ∼. The logic 2Int∼ is also characterised by a Kripke-style semantics, a twist structure semantics and a topological semantics.
Incorporating Intensity Distance Attenuation Into PLUM Ground‐Motion‐Based Earthquake Early Warning in the United States: The APPLES Configuration

Authors:
raw_author_name: Jessie K. Saunders
raw_affiliation_string: California Institute of Technology, Pasadena, CA, USA
raw_author_name: Elizabeth S. Cochran
raw_affiliation_string: U.S. Geological Survey, Earthquake Science Center, Pasadena, CA, USA
raw_author_name: Julian J. Bunn
raw_affiliation_string: California Institute of Technology, Pasadena, CA, USA
raw_author_name: Annemarie S. Baltay
raw_affiliation_string: U.S. Geological Survey, Earthquake Science Center, Moffett Field, CA, USA
raw_author_name: Sarah E. Minson
raw_affiliation_string: U.S. Geological Survey, Earthquake Science Center, Moffett Field, CA, USA
raw_author_name: Colin T. O’Rourke
raw_affiliation_string: U.S. Geological Survey, Earthquake Science Center, Seattle, WA, USA

Abstract: Abstract We develop Attenuated ProPagation of Local Earthquake Shaking (APPLES), a new configuration for the United States West Coast version of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm that incorporates attenuation into its ground‐motion prediction procedures. Under APPLES, instead of using a fixed radius to forward‐predict observed peak ground shaking to the area surrounding a seismic station, the forward‐predicted intensity at a location depends on the distance from the station using an intensity prediction relationship. We conduct conceptual tests of maximum intensity distribution predictions in APPLES and PLUM using a catalog of ShakeMaps to confirm that the attenuation relationship in APPLES is appropriately modeling shaking distributions for West Coast earthquakes. Then, we run APPLES and PLUM in simulated real‐time tests to determine warning time performance. Finally, we compare real‐time alert behavior during the 2022 M 6.4 Ferndale, California, earthquake and other recent events. We find that APPLES presents two potential improvements to PLUM by reducing over‐alerting during smaller magnitude earthquakes and by increasing warning times in some locations during larger earthquakes. APPLES can produce missed and late alerts in locations that experience shaking intensities close to the level used to issue alerts, so preferred alerting strategies with APPLES would use alert thresholds that are lower than the intensities targeted for EEW alerts. We find alerts using APPLES are also similar to those for the source‐based approaches currently used in the ShakeAlert EEW system, which will make APPLES easier to integrate into the system.
Sparse Embedded Convolution Based Dual Feature Aggregation 3D Object Detection Network

Authors:
raw_author_name: Hai-Sheng Li
raw_affiliation_string: School of Electronic and Information Engineering, Guangxi Normal University, Guilin, 541004, Guangxi, P. R. China
raw_author_name: Yan-Ling Lu
raw_affiliation_string: College of Electronic and Information Engineering, Guangxi Normal University, Guilin, 541004, Guangxi, P. R. China

Abstract: Abstract The algorithm design of compatible detection speed and accuracy based on LiDAR point clouds is a challenging issue in various practical applications of 3D object detection, including the field of autonomous driving. This paper designs a single-stage object detection algorithm that is lightweight and compatible with detection speed and accuracy for the above issue. To achieve these objectives, we propose a framework for a 3D object detection algorithm using a single-stage detection network as the backbone network. Firstly, we design a dual feature extraction module to reduce the occurrence of vehicle miss and error detection problems. Then, we use a multi-scale feature fusion scheme to fuse feature information with different scales. Furthermore, we design a data enhancement scheme suitable for this network architecture. Experimental results in the KITTI dataset show that the proposed method achieves improvement ratios of 38.5% for the detection speed and 2.88% $$\sim $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>∼</mml:mo> </mml:math> 13.65% in terms of the average precision of vehicle detection compared to the existing algorithm based on single-stage object detection (SECOND).
Chain-of-event prompting for multi-document summarization by large language models

Authors:
raw_author_name: Songlin Bao
raw_affiliation_string: Department of Computer Science, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Tiantian Li
raw_affiliation_string: Department of Computer Science, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Bin Cao
raw_affiliation_string: Department of Computer Science, Zhejiang University of Technology, Hangzhou, China

Abstract: Purpose In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task. Design/methodology/approach To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning. Findings Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets. Originality/value This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.
Time Series Prediction of ESN Based on Chebyshev Mapping and Strongly Connected Topology

Authors:
raw_author_name: Qianxue Wang
raw_affiliation_string: School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
raw_author_name: Simin Yu
raw_affiliation_string: School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China

Abstract: Abstract This paper introduces a novel approach called Chebyshev mapping and strongly connected topology for optimization of echo state network (ESN). To enhance the predictive performance of ESNs for time series data, Chebyshev mapping is employed to optimize the irregular input weight matrix. And the reservoir of the ESN is also replaced using an adjacency matrix derived from a digital chaotic system, resulting in a reservoir with strong connectivity properties. Numerical experiments are conducted on various time series datasets, including the Mackey–Glass time series, Lorenz time series and solar sunspot numbers, validating the effectiveness of the proposed optimization methods. Compared with the traditional ESNs, the optimization method proposed in this paper has higher predictive performance, and effectively reduce the reservoir’s size and model complexity.
The Salient360! toolbox: Handling gaze data in 3D made easy

Authors:
raw_author_name: Erwan David
raw_affiliation_string: Scene Grammar Lab, Department of Psychology, Goethe University, Frankfurt-am-Main, 60323, Germany
raw_author_name: Jesús Gutiérrez
raw_affiliation_string: Grupo de Tratamiento de Imágenes, Universidad Politécnica de Madrid, Madrid, 28040, Spain
raw_author_name: Melissa Lè-Hoa Võ
raw_affiliation_string: Scene Grammar Lab, Department of Psychology, Goethe University, Frankfurt-am-Main, 60323, Germany
raw_author_name: Antoine Coutrot
raw_affiliation_string: LIRIS, CNRS, University of Lyon, Lyon, 69622, France
raw_author_name: Matthieu Perreira Da Silva
raw_affiliation_string: Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, 44000, France
raw_author_name: Patrick Le Callet
raw_affiliation_string: Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, 44000, France

Abstract: Eye tracking has historically been a very popular tool. The data it records allow us to understand how people behave and what they attend to within our visual world; under this perspective the experiments, applications and use-cases are endless. Therefore, it is not surprising to witness a strong rise in the use of eXtended Reality (XR) devices with embedded eye trackers in research. These devices allow for less obtrusive experimenting conditions, and a significantly higher experimental control compared to traditional desktop testing. The use of eye tracking in XR is increasing and so is the need for a toolbox enabling consensus about eye tracking methods in 3D. We present the Salient360! toolbox: it implements functions to identify saccades and fixations and output gaze features (e.g., saccade directions) to generate saliency maps, fixation maps, and scanpath data. It implements comparisons of gaze data with methods adapted to 3D. We plan continuous improvements of the toolbox as the community develops new tools and methods dedicated to 360°gaze tracking. We hope that this toolbox will spark discussions about the methodology of 3D gaze processing, facilitate running experiments, and improve studying gaze in 3D. https://github.com/David-Ef/salient360Toolbox
English Code-Mixings in WhatsApp interactions among Spanish adolescents and their orthographic competence

Authors:
raw_author_name: Francisco Núñez-Román
raw_author_name: Alejandro Gómez-Camacho

Abstract: Aims: The widespread use of computer-mediated communication (CMC) among adolescents has favored the creation of a newly written code called digitalk. This new code includes, among other characteristics, the use of foreign words as textisms, mainly anglicisms. These textisms also serve as a mark of identity among young speakers. The aim of this paper is twofold: first, to describe which are the more frequent anglicisms used by Spanish adolescents in their CMC and the code-mixing pathways of inclusion; second, to examine the possible influence between the use of anglicisms in the CMC of Spanish adolescents and their orthographic competence. Method: Based on an exploratory methodology, a corpus of interactions through WhatsApp of a group of adolescents attending Compulsory Secondary Education in a region of southern Spain has been analyzed. Data analysis: Sketch Engine software was used for corpus analysis, presenting the normalized frequency (1/1,000) of textisms, misspellings, and multimodal elements. Finally, for the descriptive statistical analysis of the data and for the bivariate correlation analysis applying Pearson’s coefficient, IBM SPSS v.26 software was used. Findings: The results indicate that young people use English Code-Mixings (ECM) as discursive framers with an expressive aim and as a sign of belonging to a specific community of speakers. Moreover, no positive correlation is observed between the use of these ECMs and a lower orthographic competence. Implications: ECMs show an important pragmatic function in CMC among Spanish adolescents. Furthermore, they are one of the elements that have been incorporated into the digital norm of Spanish and can be used in Compulsory Education as a resource for the acquisition of communicative competence in adolescents along with other textisms. Originality: This is the first research that analyzes the frequency and function of anglicisms in Spanish CMC and their impact on the orthographic competence of Spanish adolescents.
Examining successful and unsuccessful time management through process data: two novel indicators of test-taking behaviors

Authors:
raw_author_name: Elena C. Papanastasiou
raw_affiliation_string: Department of Education, School of Education, University of Nicosia, 46 Makedonitissas Avenue, 2417, Nicosia, Cyprus

Abstract: Abstract Test-taking behavior is a potential source of construct irrelevant variance for test scores in international large-scale assessments where test-taking effort, motivation, and behaviors in general tend to be confounded with test scores. In an attempt to disentangle this relationship and gain further insight into examinees’ test-taking processes, researchers can now utilize process and timing data to obtain a more comprehensive view of test-taking behaviors, such as test-taking effort. The purpose of this study is to propose and evaluate two novel response-based, standardized indicators of test-taking behaviors that utilize a combination of examinee response and process (timing) data to better understand and describe test-taking effort in ILSAs. These indices were empirically estimated with USA data from two booklets from e-TIMSS 2019 in mathematics for grade 4. In addition, their predictive validity was examined with respect to achievement estimates. Their network of associations with other relevant variables such as motivation, interest in the subject, as well as across subjects were also examined to test their intra-individual stability in e-TIMSS.
Toward Redefining Library Research Support Services in Australia and Aotearoa New Zealand: An Evidence-Based Practice Approach

Authors:
raw_author_name: Eleanor Colla
raw_affiliation_string: Library, University of Melbourne, Melbourne, Australia

Abstract: An increasingly complex and demanding research landscape has seen university libraries rapidly evolve their services. While research data management, bibliometrics, and research impact services have predominantly featured in the literature to date, the full scope of support libraries are currently providing to their institutions is unknown. This paper aims to present an up-to-date view of the scope and extent of research support services by university libraries across Australia and Aotearoa New Zealand. A coding process analyzed content data from university library websites. Eleven research support areas were identified. Service delivery is split between synchronous and asynchronous modes. This paper describes a lived experience of an evidence-based library and information practice approach to improving research support services at two Australian university libraries, and while it highlights continued maturation of research support services, more research is needed to better understand influences on service development.