Reviewerly/exHarmony huggingface dataset:
Incorporating Intensity Distance Attenuation Into PLUM Ground‐Motion‐Based Earthquake Early Warning in the United States: The APPLES Configuration

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

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

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

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.
Video dataset of Balinese dance basic movement for action recognition

In the convergence of cultural heritage preservation and computational research, this paper presents a comprehensive dataset capturing the nuanced artistry of traditional Balinese dancing. This endeavour not only documents but also aims to protect these intricate dance movements, bridging technological advancements with rich cultural traditions. The dataset includes 1740 and 828 high-definition video recordings of female and male dancers, respectively, showcasing 24 unique dance movements. These recordings, made using multiple smartphone cameras from various perspectives, reflect the dance's complex nature and emphasize gender-based movement variations, key for detailed cultural analysis. Our systematic approach involved three strategically placed cameras, capturing diverse angles for a holistic view. The subsequent preprocessing stage, including segmenting and labelling, has enhanced the dataset's clarity, making it a valuable resource for cultural studies and computational analysis in preserving intangible cultural heritage. The videos, stored in MP4 format, are categorized by dancer gender, dance type, and camera angle, offering researchers a rich, multifaceted tool for exploring this traditional art form.
The Salient360! toolbox: Handling gaze data in 3D made easy

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

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

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.
The persistent attractions of low-tech: Challenging the efficiency paradigm of forensic technology

The promise of efficiency is a catalyst for technological development in forensic science and crime scene investigation practices. Crime scene investigators aim to locate and document every potential trace for evidence and investigate crime scenes as quickly, accurately and objectively as possible. This narrative suggests that performance leads the police to adopt new efficient technologies. But to what extent is this narrative reflective of actual practices? Through examining how crime scene investigators use existing technologies in practice, i.e. traditional or ‘low-tech’ methods and tools such as DNA detection dogs and manual documentation tools, this article shows that the use of low-tech persists even under the pressure to perform and achieve. The study finds that low-tech remains attractive as a less-restraining alternative to digital and high-tech solutions. It is also contrast to, integrated with or necessary in co-existence with high-tech for crime scene investigations practices to function. The study draws on fieldwork conducted at police stations in Norway, including interviews and participatory observation of investigations in practice, to discuss the persistent attractions of low-tech and what the implications are for research into policing and technology.
Toward Redefining Library Research Support Services in Australia and Aotearoa New Zealand: An Evidence-Based Practice Approach

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.
Addressing quantum’s “fine print” with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks

Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.
Effective Combination of Oral Reading and Retrieval Practice Strategies: The Moderating Effect of Reading Model Development

The complementary view of strategy combinations points to the fact that a combination of strategies that promote memory encoding and retrieval practice strategies can be effective in improving memory retention. This study examines the effectiveness of the combination of oral reading and retrieval practice strategies and the effect of the development of reading patterns on their effectiveness. This study designed a mixed experimental design of 3 (grades: Grade three, Grade five, college students) × 2 (reading mode: oral reading and silent reading) × 2 (learning strategies: re-learning and retrieval practice). Among them, reading mode is the within-subject variable, and learning strategy and grade are the between-subjects variables. The dependent variable is the ratio of specific words recalled in the final test. The results show that the oral and retrieval practice is a significant and effective combination of memory strategies for primary school students who are mainly oral reading or in the transition from oral reading to silent reading. It is significantly better than the three strategy combinations of oral reading combined with re-leaning, silent reading combined with re-learning, and silent reading combined with retrieval practice. For adult students who dominated silent reading, silent reading combined with retrieval practice is the best memory strategy.
Do redundant graphical attributes reduce decision-making efficiency?

Multiple time-series graphs are commonly used for data visualisation, but few scholars have investigated the impact of graphical attributes on decision-making efficiency. This study explores the effects of graphical attributes of varying redundancy conditions on decision-making efficiency. Two experimental conditions were developed for the experiment: non-redundant (independent graphical attributes: colour, linear and marker) and redundant (combinations of two and more graphical attributes: colour and linear, colour and marker, etc.). A total of 60 people took part in both experiments and performed two tasks: maximisation and discrimination. The experiments revealed that the addition of attributes, such as colour, marker or linear, decreased response time (RT), but the combination of colour & linear & marker increased RT. This is more significant in discrimination tasks. We provide empirical evidence for the design of time-series data visualisations and encourage the combination of two of these graphical attributes, such as colour & linear, colour & marker or linear & marker, when conditions allow, to improve decision-making efficiency.Few scholars have studied the impact of graphical attributes on decision-making efficiency in data visualisation. This study explores the effect of graphical attributes with different redundancy levels on decision-making efficiency through behavioural experiments. It has been found that moderately redundant graphical attributes in difficult tasks can significantly improve decision-making efficiency.
Application of multi-scale feature fusion algorithm based on motion wearable sensors in feature extraction of sports images

The utilization of moving image feature extraction in sports teaching has garnered increasing attention. However, traditional feature extraction algorithms often struggle to meet the diverse and complex demands of moving images. To address this challenge, this paper proposes a multi-scale feature fusion algorithm aimed at improving feature extraction in moving images. The algorithm begins by decomposing the moving image into multiple scales, followed by extracting features from each scale using a feature extraction network. To obtain a more comprehensive and accurate feature representation, feature fusion technology is employed to merge the features from different scales. The proposed algorithm, based on multi-scale feature fusion, exhibits a significant improvement in both accuracy and stability when compared to traditional feature extraction algorithms. Byaccurately extracting and representing the crucial features within moving images, the algorithm contributes to an improved understanding of athletes' movements, enabling instructors to provide more targeted and insightful feedback. This algorithm effectively captures key features within the moving images, providing robust support for tasks such as movement analysis and skill evaluation in sports teaching.
Regulating speech: harm, norms, and discrimination

Mary Kate McGowan's Just Words offers an interesting account of exercitives. On McGowan's view, one of the things we do with words is change what's permitted, and we do this ubiquitously, without any special authority or specific intention. McGowan's account of exercitives is meant to identify a mechanism by which ordinary speech is harmful, and which justifies the regulation of such speech. It is here that I part ways. I make three main arguments. First, McGowan's focus on harm is misguided; that ordinary speech is harmful is harder to support, and in turn does not less to support conclusions about whether it is wrong and warrants regulation, than Just Words suggests. Second, if the speech is harmful, McGowan's account of exercitives is ill-suited to explain why; the relevant instances of ordinary speech seem to express speakers' views about moral norms, not change social norms. Third, McGowan's argument for why ordinary speech should be regulated because it is a form of discrimination presupposes a great deal that is controversial about what makes discrimination wrong and legally actionable, and even if it is right it may license the regulation of more speech than many, including McGowan, would deem acceptable.
PAC: A monitoring framework for performance analysis of compression algorithms in Spark

In Spark, a massive amount of immediate data inevitably leads to excessive I/O overhead. To mitigate this issue, Spark incorporates four compression algorithms to reduce the size of the data for better performance. However, compression and decompression only constitute a portion of the overall logical flows of Spark applications. This indicates a potential considerable interaction between compression algorithms and Spark applications regarding performance. Consequently, identifying factors that significantly impact the performance of compression algorithms in Spark, and subsequently, determining the actual performance benefits these algorithms provide to Spark applications, remains a significant challenge. To address the challenge, this paper presents a monitoring framework, named PAC, for conducting in-depth and systematic performance analysis of compression algorithms in Spark. As the pioneer of such monitoring frameworks, PAC is built on top of Spark core and collaborates with multiple monitors to collect various types of performance metrics of compressors, correlates and integrates them into structured tuples by the data transformer in PAC. This makes it easier to diagnosis of factors that have a significant influence on the performance of compression algorithms in Spark. Upon utilizing PAC, our experiments reveal that new determinants include the input/output data sizes and types of compression/decompression invocations, CPU consumption for compressing a massive amount of data, and hardware utilization, besides traditional determinants. Moreover, these experiments demonstrate that ZSTD is more susceptible to performance issues when compressing and decompressing small data, despite the overall input and output data being huge. In terms of performance, LZ4 serves as a viable alternative to ZSTD. These findings not only benefit researchers and developers in making more informed decisions in terms of configuring and tuning Spark execution environments but also sustainably boost the optimization of compression algorithms for Spark.