Reviewerly/exHarmony huggingface dataset:
BC-MetaCast: A Blockchain-Enhanced Intelligent Computing Framework for Metaverse Livecast

Livecast in metaverse, such as Metaverse Concert, is gaining momentum, however, its extremely compute-intensive and delay-sensitive features challenge the ability of current computing paradigm. Besides, decentralization, which is one of the core ideas of the metaverse, drives the move away from cloud computing and edge computing. Hence, it urges a trustworthy, robust, and efficient intelligent computing paradigm to meet these requirements. In this article, we discuss the problems related to livecast in metaverse, and propose a blockchain-enhanced intelligent computing framework for metaverse livecast (BC-MetaCast). With smart contracts built upon a hybrid blockchain with multiple regions, BC-MetaCast offers a decentralized and truthful framework. Besides, we design On&Off-Chain Superimposed DRL (Sup-DRL) algorithm based on deep reinforcement learning in BC-MetaCast to achieve efficient computational task. Finally, experimental results are provided to illustrate that BC-MetaCast is reliable, scalable, and can provide superior service quality compared to state-of-art schemes.
LiberRoad: Probing into the Journey of Chinese Classics through Visual Analytics

Books act as a crucial carrier of cultural dissemination in ancient times. This work involves joint efforts between visualization and humanities researchers, aiming at building a holistic view of the cultural exchange and integration between China and Japan brought about by the overseas circulation of Chinese classics. Book circulation data consist of uncertain spatiotemporal trajectories, with multiple dimensions, and movement across hierarchical spaces forms a compound network. LiberRoad visualizes the circulation of books collected in the Imperial Household Agency of Japan, and can be generalized to other book movement data. The LiberRoad system enables a smooth transition between three views (Location Graph, map, and timeline) according to the desired perspectives (spatial or temporal), as well as flexible filtering and selection. The Location Graph is a novel uncertainty-aware visualization method that employs improved circle packing to represent spatial hierarchy. The map view intuitively shows the overall circulation by clustering and allows zooming into single book trajectory with lenses magnifying local movements. The timeline view ranks dynamically in response to user interaction to facilitate the discovery of temporal events. The evaluation and feedback from the expert users demonstrate that LiberRoad is helpful in revealing movement patterns and comparing circulation characteristics of different times and spaces.
Stacked Network to Realize Spectral Clustering With Adaptive Graph Learning

Spectral clustering with graph learning usually performs eigen-decomposition on the adaptive graph to obtain embedded representation for clustering. In terms of adaptive graph learning, the embedded representation is usually treated as the principal component of the graph to help improve graph structure. However, most adaptive graph learning methods only use a single graph layer. Therefore, the extraction power of embedded representation is restricted to single graph layer and insufficient to explore the intrinsic information. To break through this limitation, this article proposes a stacked network to realize spectral clustering with adaptive graph learning (SCnet-AGL). Specifically, the network allows the development of latent embedded representation underlying the multiple graph layers to reveal the intrinsic information. Meanwhile, we have designed an adaptive graph learning scheme to exploit the latent embedded representation for graph learning. With the advantage of the network, an augmented graph is obtained by incorporating the representation information for graph learning layer by layer. Finally, an efficient algorithm with feedback training scheme is proposed for network training. Experiments on real datasets demonstrate the effectiveness of the proposed network, and show that it is feasible to develop latent embedded representation to improve clustering performance. For code access, please visit: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/scutbioinformatics/SCnet-AGL</uri> .
Metrics-Based Evaluation and Comparison of Visualization Notations

A visualization notation is a recurring pattern of symbols used to author specifications of visualizations, from data transformation to visual mapping. Programmatic notations use symbols defined by grammars or domain-specific languages (e.g., ggplot2, dplyr, Vega-Lite) or libraries (e.g., Matplotlib, Pandas). Designers and prospective users of grammars and libraries often evaluate visualization notations by inspecting galleries of examples. While such collections demonstrate usage and expressiveness, their construction and evaluation are usually ad hoc, making comparisons of different notations difficult. More rarely, experts analyze notations via usability heuristics, such as the Cognitive Dimensions of Notations framework. These analyses, akin to structured close readings of text, can reveal design deficiencies, but place a burden on the expert to simultaneously consider many facets of often complex systems. To alleviate these issues, we introduce a metrics-based approach to usability evaluation and comparison of notations in which metrics are computed for a gallery of examples across a suite of notations. While applicable to any visualization domain, we explore the utility of our approach via a case study considering statistical graphics that explores 40 visualizations across 9 widely used notations. We facilitate the computation of appropriate metrics and analysis via a new tool called NotaScope. We gathered feedback via interviews with authors or maintainers of prominent charting libraries (n = 6). We find that this approach is a promising way to formalize, externalize, and extend evaluations and comparisons of visualization notations.
Exploring the potential of eye tracking on personalized learning and real-time feedback in modern education

Eye tracking is one of the techniques used to investigate cognitive mechanisms involved in the school context, such as joint attention and visual perception. Eye tracker has portability, straightforward application, cost-effectiveness, and infant-friendly neuroimaging measures of cognitive processes such as attention, engagement, and learning. Furthermore, the ongoing software enhancements coupled with the implementation of artificial intelligence algorithms have improved the precision of collecting eye movement data and simplified the calibration process. These characteristics make it plausible to consider eye-tracking technology a promising tool to assist the teaching-learning process in school routines. However, eye tracking needs to be explored more as an educational instrument for real-time classroom activities and teachers' feedback. This perspective article briefly presents the fundamentals of the eye-tracking technique and four illustrative examples of employing this method in everyday school life. The first application shows how eye tracker information may contribute to teacher assessment of students' computational thinking in coding classes. In the second and third illustrations, we discuss the additional information provided by the eye-tracker to the teacher assessing the student's strategies to solve fraction problems and chart interpretation. The last illustration demonstrates the potential of eye tracking to provide Real-time feedback on learning difficulties/disabilities. Thus, we highlight the potential of the eye tracker as a complementary tool to promote personalized education and discuss future perspectives. In conclusion, we suggest that an eye-tracking system could be helpful by providing real-time student gaze leading to immediate teacher interventions and metacognition strategies.
Encoding, Processing and Interpreting Vagueness and Uncertainty in Historical Texts – A Pilot Study Based on Multilingual 18<sup>th</sup> Century Texts of Dimitrie Cantemir (HerCoRe)

Encoding, Processing and Interpreting Vagueness and Uncertainty in Historical Texts – A Pilot Study Based on Multilingual 18th Century Texts of Dimitrie Cantemir (HerCoRe) was published in Mixing Methods on page 239.
Federated Latent Dirichlet Allocation for User Preference Mining

In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.
Rapidly Evolving Soft Robots via Action Inheritance

The automatic design of soft robots characterizes as jointly optimizing structure and control. As reinforcement learning is gradually used to optimize control, the time-consuming controller training makes soft robots design an expensive optimization problem. Although surrogate-assisted evolutionary algorithms have made a remarkable achievement in dealing with expensive optimization problems, they typically suffer from challenges in constructing accurate surrogate models due to the complex mapping among structure, control, and task performance. Therefore, we propose an action inheritance-based evolutionary algorithm to accelerate the design process. Instead of training a controller, the proposed algorithm uses inherited actions to control a candidate design to complete a task and obtain its approximated performance. Inherited actions are near-optimal control policies that are partially or entirely inherited from optimized control actions of a real evaluated robot design. The action inheritance plays the role of surrogate models where its input is the structure and output is the near-optimal control actions. We also propose a random perturbation operation to estimate the error introduced by inherited control actions. The effectiveness of our proposed method is validated by evaluating it on a wide range of tasks, including locomotion and manipulation. Experimental results show that our algorithm is better than the other three state-of-the-art algorithms on most tasks when only a limited computational budget is available. Compared with the algorithm without surrogate models, our algorithm saves about half the computing cost.
Comparative study of the strain gauge and 3D digital image correlation testing methods under hydrostatic conditions of composite shells

Experimental strain testing of composite shells under 15MPa hydrostatic pressure was conducted using both strain gauges and underwater 3D-DIC (Digital Image Correlation) testing technology. The experimental results showed a large error in the measurement results of strain gauges when small-sized components were present in the test area of the experimental object. The average standard deviation of the strain results was 716.1 με and the average standard deviation of the Mises stress results was 120.9 MPa. However, the strain results of the underwater 3D-DIC test system in the circumferential direction of the composite shell were generally consistent. This proves that the system can maintain good test accuracy even under high hydrostatic pressure environments.
Broad-UNet-diff: Diffeomorphic Deformable Medical Image Registration based on Multi-Scale Feature Learning

Introduction: To propose a medical image registration method with significant performance improvement. The spatial transformation obtained by the traditional deformable image registration technology is not smooth enough, and the calculation amount is too large to solve the optimization parameters. The network model proposed based on deep learning medical image registration technology has some limitations, which cannot guarantee the registration of topological structures, resulting in the loss of spatial features. It makes the model have topological conservation and transform reversibility, has the ability to learn more multi-scale features and complex image structures, and captures finer changes while clearly encoding global correlation. Method: Based on the core UNet model, a deformable image registration method with a new architecture Broad-UNet-diff is proposed. The model is equipped with asymmetric parallel convolution and uses diffeomorphism mapping. Result: Compared with the seven classical registration methods under the brain MRI datasets, the proposed method has significantly improved the registration performance. In particular, compared with the advanced TransMorph-diff registration method, the Dice score can be improved by 12 %, but only the 1/10 parameters are needed. Conclusion: This method confirms its convincing effectiveness and accuracy.
A Multi-label Feature Selection Method Based on Multi-objectives Optimization by Ratio Analysis

Multi-label learning affected from curse of dimensionality, therefore, feature selection is vital. In this paper, we propose feature ranking method using one of the well-known Multi Attribute Decision Making (MADM) technique. This method formulates Multi-Objectives Optimization by Ratio Analysis (MOORA) for multi-label feature selection. The proposed MOORA-Based Multi-Label Feature Selection (MBMLFS) uses Feature-Feature correlation as non-beneficial criteria and Feature-Label correlation as beneficial criteria to form the decision matrix. Person’s correlation and cosine similarity is used to compute correlation of features with features and labels, respectively. The MBMLFS uses decision matrix to rank the features. Six benchmark datasets (Bibtex, Birds, Corel5k, Scence, Enron, and Medical) are used to evaluate the MBMLFS. The MBMLFS is compared with five baseline feature selection methods. Results are statistically (Wilcoxon) tested to show the significance. The experiment shows that the proposed MBMLFS outperforms. The proposed MBMLFS is taking less execution time and won 83% of the time when compared to other well-known methods.
Passthrough mixed reality capability of VR HMD extended by wearable force-feedback mechanism for free-range haptic experience

This chapter presents the development of a wearable force-feedback mechanism designed to provide a free-range haptic experience within the spectrum of Extended Reality (XR). The proposed system offers untethered six degrees-of-freedom and small- to medium-scale force-feedback, enabling users to immerse themselves in haptic interactions within virtual environments. The hardware comprises a modified 3D Systems Touch haptic device, driven by software that allows for ambulatory exploration of various haptic aspects. Two experiments were conducted to evaluate the precision, ergonomics, stability, usability, user experience, and performance of the system. Despite indication of software and hardware deficiencies, the results highlight the potential of combining haptic force-feedback and ambulatory XR to enhance immersion in free-range virtual environments. Furthermore, the integration of Mixed Reality pass-through enables users to seamlessly merge real-world environments with augmenting virtual elements. This extension contributes to the exploration of new possibilities for immersive and interactive experiences within mixed reality applications. Future research can delve deeper into the prototype’s potential, further unlocking opportunities for haptic-enabled ambulatory XR experiences and pushing the boundaries of immersive technologies.
On the Cost-Effectiveness of Stacking of Neural and Non-Neural Methods for Text Classification: Scenarios and Performance Prediction

Nowadays Neural Network algorithms have excelled in Automatic Text Classification (ATC). However, such enhanced performance comes at high computational costs. Stacking of simpler classifiers that exploit algorithmic and representational complementarity has also been shown to produce superior performance in ATC, enjoying high effectiveness and potentially lower computational costs than complex neural networks. In this master’s thesis, we present the first and largest comparative study to exploit the cost-effectiveness of Stacking in ATC, consisting of Transformers and non-neural algorithms. We investigate cost-effective ensemble vs. the best model and propose a low-cost oracle-based prediction method.
Parafoveal processing in bilingual readers: Semantic access within but not across languages.

Prior research has investigated the quality of information a reader can extract from upcoming parafoveal words. However, very few studies have considered parafoveal processing in bilingual readers, who may differ from monolinguals due to slower lexical access and susceptibility to cross-language activation. This eye-tracking experiment, therefore, investigated how bilingual readers process parafoveal semantic information within and across languages. We used the boundary technique to replace a preview word in a sentence with a different target word during the first rightward saccade from the pretarget region. We manipulated both preview language (nonswitch vs. code-switch) and semantic relatedness (synonym/translation vs. unrelated) between previews and targets. Upon fixation, target words always appeared in the same language as the rest of the sentence to create an essentially monolingual language context. Semantic preview benefits emerged for nonswitched synonym previews but not for code-switched translation previews. Furthermore, participants skipped code-switched previews less often than nonswitched previews and no more often than previews that were unfamiliar to them. These data suggest that bilinguals can extract within-language semantic information from the parafovea in both native and nonnative languages, but that cross-language words are not accessible while reading in a monolingual language mode, as per the partial selectivity hypothesis of bilingual language control. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Merge Tree Geodesics and Barycenters with Path Mappings

Comparative visualization of scalar fields is often facilitated using similarity measures such as edit distances. In this paper, we describe a novel approach for similarity analysis of scalar fields that combines two recently introduced techniques: Wasserstein geodesics/barycenters as well as path mappings, a branch decomposition-independent edit distance. Effectively, we are able to leverage the reduced susceptibility of path mappings to small perturbations in the data when compared with the original Wasserstein distance. Our approach therefore exhibits superior performance and quality in typical tasks such as ensemble summarization, ensemble clustering, and temporal reduction of time series, while retaining practically feasible runtimes. Beyond studying theoretical properties of our approach and discussing implementation aspects, we describe a number of case studies that provide empirical insights into its utility for comparative visualization, and demonstrate the advantages of our method in both synthetic and real-world scenarios. We supply a C++ implementation that can be used to reproduce our results.
Strategies for Promoting Reading Competence: Teaching Practices and Enjoyment of Reading.

Reading literacy is key for personal development and educational success. Previous studies have examined variables that influence and enhance development of reading literacy in specific contexts. However, there is no consensus about which teaching practices encourage development of reading in different settings. The aim of this study was to evaluate how educational strategies influence the development of reading literacy and to analyse their predictive capacity in various cultural, educational and social contexts.The study used data from 294,527 students from 37 countries collected as part of the PISA 2018 study. The data were analysed using an adaptation of the Difference in Differences methodology, which allowed us to isolate the effects of the factors on the acquisition of reading literacy.Students who enjoyed reading and explicitly used an effective reading strategy had reading scores that outperformed their mathematics results on the PISA scale by 4 to 9 points on average.Identifying key factors in the acquisition of reading literacy—such as enjoyment of reading—and the use of effective strategies—such as summarizing texts—underscores the need for suitably targeted educational policies.
Unraveling the Design Space of Immersive Analytics: A Systematic Review

Immersive analytics has emerged as a promising research area, leveraging advances in immersive display technologies and techniques, such as virtual and augmented reality, to facilitate data exploration and decision-making. This paper presents a systematic literature review of 73 studies published between 2013-2022 on immersive analytics systems and visualizations, aiming to identify and categorize the primary dimensions influencing their design. We identified five key dimensions: Academic Theory and Contribution, Immersive Technology, Data, Spatial Presentation, and Visual Presentation. Academic Theory and Contribution assess the motivations behind the works and their theoretical frameworks. Immersive Technology examines the display and input modalities, while Data dimension focuses on dataset types and generation. Spatial Presentation discusses the environment, space, embodiment, and collaboration aspects in IA, and Visual Presentation explores the visual elements, facet and position, and manipulation of views. By examining each dimension individually and cross-referencing them, this review uncovers trends and relationships that help inform the design of immersive systems visualizations. This analysis provides valuable insights for researchers and practitioners, offering guidance in designing future immersive analytics systems and shaping the trajectory of this rapidly evolving field.