Reviewerly/exHarmony huggingface dataset:
Deep Learning Method for Heliostat Instance Segmentation

Heliostat instance segmentation (HST-IS) is a crucial component of the heliostat tracking system at Heliogen’s Lancaster test facility. The system estimates the mirror normal of each heliostat by performing a nonlinear optimization-based fitting strategy using approximations of the non-shaded, non-blocked sunlit pixels on each heliostat, and the tracking system uses these estimates to improve performance.&#x0D; HST-IS is fundamentally challenging due to variability in lighting conditions and heliostat size relative to the capturing camera. Deep learning-based convolutional neural networks (CNN) have emerged in recent years by demonstrating noteworthy precision in tasks such as object recognition, detection, and segmentation. CNN-based methods offer a robust augmentation to HST-IS methods as they capture a context-less hierarchy of image features.&#x0D; In this study, we developed deep learning models to automatically segment heliostat instances from elevated images taken from the field. We study various image parameters and architectural customizations to optimize for scalability, robustness, and accuracy in our predictions. We perform robust evaluations of our best model to quantify gaps between model development and real-world deployment and provide evidence for utility in the field.
The Role of Artificial Intelligence Autonomy in Higher Education: A Uses and Gratification Perspective

With the rapid development of artificial intelligence (AI) technology, AI educators have become a reality. The advancement and increasing applications of AI technology in higher education not only provide more efficient tools for teachers in long-term and focused teaching, but also provide new active and independent spaces for sustainable self-motivated learning for college students. It is of great importance that the effects of AI educator design are understood to ensure the sustainable development and deployment of AI-driven courses at universities. This paper investigates the influences of AI educators’ autonomy design on students’ usage intentions by delving into how the artificial autonomy of AI educators satisfies students’ needs. Drawing on the uses and gratification (U&amp;G) framework, we theoretically elaborate on how AI educator autonomy (i.e., sensing autonomy, thought autonomy, and action autonomy) influences students’ intentions to use an AI educator through the mediating effects of U&amp;G benefits (i.e., information-seeking gratification, social interaction gratification, and entertainment gratification). By conducting an online survey (N = 673) on college students, we found that the sensing autonomy of AI educators is positively associated with usage intention due to the mediating effects of social interaction and entertainment gratifications; the thought autonomy of AI educators is positively related to usage intention, mediated by information-seeking and social interaction gratifications, and the action autonomy of AI educators is positively linked with usage intention through the paths of information-seeking and entertainment gratifications. Our findings provide both theoretical contributions and practical implications.
Improved Nuclear Reaction Heuristic Intelligence Algorithm for Online Learning in Self-Monitoring Strategy Convergence

INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategy-integrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning. OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning self-monitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments. RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05. CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning.
Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm

INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION: Addresses the lack of comprehensiveness of current approaches to early warning of new media events.
An efficient quantum algorithm for ensemble classification using bagging

Abstract Ensemble methods aggregate predictions from multiple models, typically demonstrating improved accuracy and reduced variance compared to individual classifiers. However, they often come with significant memory usage and computational time requirements. A novel quantum algorithm that leverages quantum superposition, entanglement, and interference to construct an ensemble of classification models using bagging as an aggregation strategy is introduced. Through the generation of numerous quantum trajectories in superposition, the authors achieve B transformations of the training set with only operations, allowing an exponential enlargement of the ensemble size while linearly increasing the depth of the corresponding circuit. Moreover, when assessing the algorithm's overall cost, the authors demonstrate that the training of a single weak classifier contributes additively to the overall time complexity, as opposed to the multiplicative impact commonly observed in classical ensemble methods. To illustrate the efficacy of the authors’ approach, experiments on reduced real‐world datasets utilising the IBM qiskit environment to demonstrate the functionality and performance of the proposed algorithm are introduced.
Conjecturing-Based Discovery of Patterns in Data

We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and Boolean expressions for categorical features. The proposed Conjecturing framework recovers known nonlinear and Boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the method to a previously proposed framework for symbolic regression on the ability to recover equations that are satisfied among features in a data set. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in the medical literature. Discovering patterns in data is a first step toward establishing causal relationships, which can be the basis for effective decision making. Data Ethics &amp; Reproducibility Note: Code and data to reproduce results are available here: https://github.com/jpbrooks/conjecturing . COVID-19 synthetic patient data were obtained as part of the Veterans Health Administration (VHA) Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge and are used here with permission from the Food and Drug Administration (FDA). The e-companion is available at https://doi.org/10.1287/ijds.2021.0043 . History: Olivia Sheng served as the senior editor for this article.
Federated Offline Reinforcement Learning

Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this paper, we propose a multi-site Markov decision process model which allows for both homogeneous and heterogeneous effects across sites. The proposed model makes the analysis of the site-level features possible. We design the first federated policy optimization algorithm for offline RL with sample complexity. The proposed algorithm is communication-efficient, which requires only a single round of communication interaction by exchanging summary statistics. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed. Extensive simulations demonstrate the effectiveness of the proposed algorithm. The method is applied to a sepsis dataset in multiple sites to illustrate its use in clinical settings.
The Mediating Role of Parasocial Relationship in Customer Services Chatbots Among Millennials and Gen Z Population

In the fast-evolving landscape of human–computer interaction (HCI), customer service chatbots have emerged as a new way for engaging with users. However, a critical challenge lies in ensuring continued usage of these chatbot's services. This study argues that by tapping into the emotional dimension of chatbot–user interactions, Parasocial Relationships (PSRs) can influence customers' continuance intention (CI) to use chatbot services. The findings from PLS-SEM shed light on the significance of Information Quality and System Quality in fostering PSRs. The study further identifies PSR as a significant mediator between Service Quality and Continuous intention to use. This research primarily examines Millennials and Gen Z. Future studies may explore consumers of different age groups. The implications of this research are crucial for advancing the field of chatbot–human interaction. Organizations can benefit from these insights by prioritizing personalized and empathetic service experiences to nurture stronger PSRs and promote user satisfaction, trust, and intention to continue using chatbot services.
Automatic Construction of Interval-Valued Fuzzy Hindi WordNet using Lexico-Syntactic Patterns and Word Embeddings

A computational lexicon is the backbone of any language processing system. It helps computers to understand the language complexity as a human does by inculcating words and their semantic associations. Manually constructed famous Hindi WordNet (HWN) consists of various classical semantic relations (crisp relations). To handle uncertainty and represent Hindi WordNet more semantically, Type- 1 fuzzy graphs are applied to relations of Hindi WordNet. But uncertainty in the crisp membership degree is not considered in Type 1 fuzzy set (T1FS). Also collecting billions (5,55,69,51,753 relations in HWN) of membership values from experts (humans) is not feasible. This paper applied the concept of Interval-Valued Fuzzy graphs and proposed Interval- Valued Fuzzy Hindi WordNet (IVFHWN). IVFHWN automatically identifies Interval- Valued Fuzzy relations between words and their degree of membership using word embeddings and lexico-syntactic patterns. The experimental results for the word sense disambiguation problem show better outcomes when IVFHWN is being used in place of Type 1 Fuzzy Hindi WordNet and classical Hindi WordNet.
The effects of NMT as a de facto dictionary on vocabulary learning: a comparison of three look-up conditions

Many language learners have reported using Neural machine translation (NMT) such as Google Translate as a reference tool for writing production or as a dictionary substitute to facilitate reading comprehension. Although increasing evidence has suggested that editing with NMT can help learners make improvement on the vocabulary level, no study has explored how consulting NMT for unknown vocabulary may impact the retention of word meaning compared to more conventional look-up conditions. Using a 3 × 3 quasi-experimental design, this study investigated the extent to which the use of NMT to learn unknown target words during reading comprehension activities yielded vocabulary retention compared to that of an online dictionary and how the findings varied by learners' proficiency levels. The research questions focused on the comparison of the outcomes of three different look-up conditions: (1) NMT word search, (2) NMT sentence search, and (3) online dictionary search. ANOVA results showed that learners who performed online dictionary search and NMT sentence search demonstrated significantly higher levels of word retention than those who carried out NMT single-word search, and the same pattern was observed regardless of their proficiency levels.
Flexible learning spaces as an arena for developing professional digital competence through small group collaboration

Flexible technology-rich learning spaces such as Future Classroom Labs (FCL) have a large potential for developing teacher education (TE) students’ professional digital competence through engaging in authentic collaborative learning experiences with digital technology. However, there are limited reports on how students perceive and enact the affordances existing in such learning spaces. This paper reports on how one such FCL can facilitate collaborative learning among TE students. The paper poses two research questions: 1) How does the FCL as a learning space accommodate for collaborative learning? and 2) How do the artefacts promote or limit collaboration among students at the FCL? The data were collected through observations of four sessions of group work at the FCL, each having a different group of 15–16 first-year TE students. The results suggest that the FCL promoted collaborative work in terms of finding and building groups, monitoring, and resource-sharing across the small groups. However, students depended on external guidance from a teacher or student assistant to understand the technical sides of the artefacts, and their educational potential. Therefore, the implication is that in their first visits to FCLs, students need guidance to get started with the distinct tasks, and to master the various devices.
Dunhuang murals image restoration method based on generative adversarial network

Abstract Murals are an important part of China’s cultural heritage. After more than a 1000 years of exposure to the sun and wind, most of these ancient murals have become mottled, with damage such as cracking, mold, and even large-scale detachment. It is an urgent work to restore these damaged murals. The technique of digital restoration of mural images refers to the reconstruction of structures and textures to virtually fill in the damaged areas of the image. Existing digital restoration methods have the problems of incomplete restoration and distortion of local details. In this paper, we propose a generative adversarial network model combining a parallel dual convolutional feature extraction depth generator and a ternary heterogeneous joint discriminator. The generator network is designed with the mechanism of parallel extraction of image features by vanilla convolution and dilated convolution, capturing multi-scale features simultaneously, and reasonable parameter settings reduce the loss of image information. A pixel-level discriminator is proposed to identify the pixel-level defects of the captured image, and its joint global discriminator and local discriminator discriminate the generated image at different levels and granularities. In this paper, we create the Dunhuang murals dataset and validate our method on this dataset, and the experimental results show that the method of this paper has an overall improvement in the evaluation metrics of PSNR and SSIM compared with the comparative methods. The restored resultant image is more in line with the subjective vision of human beings, which achieves the effective restoration of mural images.
A multimodal virtual vision platform as a next-generation vision system for a surgical robot

Abstract Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot. Graphical Abstract
Towards an obstacle detection system for robot obstacle negotiation

Purpose To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles. Design/methodology/approach The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method. Findings The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage. Originality/value This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.
LCPCWSC: a Web service classification approach based on label confusion and priori correction

Purpose With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. Design/methodology/approach This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Findings Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. Originality/value This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.
Relationships among class climate, students' internal help‐seeking inclinations, participation in peer help and achievement in an online class

Abstract Help seeking has traditionally been studied in face‐to‐face settings using self‐report instruments measuring learners' internal inclinations for help seeking (eg, perceived benefits and threats). Much less is known about help seeking in online learning. Furthermore, external environmental factors such as a positive climate were found to encourage help seeking. Utilizing peer‐help analytics as well as self‐report measures, this study simultaneously modelled the structural relationships among students' perceived class climate, internal help‐seeking inclinations, help‐seeking engagement, participation in peer help and performance in an online class. Results from structural equation modelling indicated that students' perceived goal structure of an online class had a significant influence on their internal help‐seeking inclinations, which subsequently affected their help‐seeking engagement, participation in peer help and learning outcomes. Instructors of online classes should pay particular attention to fostering a mastery‐oriented climate in order to encourage help seeking and improve academic performance. Practitioner notes What is already known about this topic Help seeking is important for academic success. Due to a variety of individual and contextual factors, students often do not seek help despite the need. Existing research on help seeking focuses on the face‐to‐face setting and relies on self‐report measures. Students' help‐seeking inclinations and patterns may differ between in‐person classes and the online environment. What this paper adds This study examined help seeking in an online class through self‐report measures and objective analytics data. This study modelled the structural relationships among the external climate of an online class, students' internal help‐seeking inclinations, engagement in help seeking, participation in peer help and course performance. Structural equation modelling found that the goal structure of an online class influenced students' internal help‐seeking inclinations. Online students' internal help‐seeking inclinations affected their grades through their participation in help seeking. Implications for practice and/or policy A mastery‐oriented class climate elicits online learners' positive help‐seeking inclinations while discouraging negative ones. The design and teaching of online classes should aim to foster a mastery‐oriented climate. Online classes should offer peer‐help opportunities and encourage student participation, which is conducive to academic performance.