Reviewerly/exHarmony huggingface dataset:
Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

Authors:
raw_author_name: Romina Soledad Molina
raw_affiliation_string: Dipartimento di Ingegneria e Architettura (DIA), Università degli Studi di Trieste, 34127 Trieste, Italy; Multidisciplinary Laboratory (MLab), Science, Technology and Innovation Unit (STI), The Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy
raw_author_name: Giovanni Ramponi
raw_affiliation_string: Dipartimento di Ingegneria e Architettura (DIA), Università degli Studi di Trieste, 34127 Trieste, Italy
raw_author_name: Ricardo Petrino
raw_affiliation_string: Departamento de Electrónica, Facultad de Ciencias Físico Matemáticas y Naturales, Universidad Nacional de San Luis, San Luis 5700, Argentina

Abstract: This study focused on seismic event detection in a volcano using machine learning by leveraging the advantages of software/hardware co-design for a system on a chip (SoC) based on field-programmable gate array (FPGA) devices. A case study was conducted on the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcanic seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA into the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for the development of an early alert system.
STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning

Authors:
raw_author_name: Shutan Xu
raw_affiliation_string: Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Hucheng Ring Road 999, Shanghai 201306, China
raw_author_name: Ming Chen
raw_affiliation_string: Key Laboratory of Fisheries Information, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Hucheng Ring Road 999, Shanghai 201306, China

Abstract: Medaka (Oryzias latipes), as a crucial model organism in biomedical research, holds significant importance in fields such as cardiovascular diseases. Currently, the analysis of the medaka ventricle relies primarily on visual observation under a microscope, involving labor-intensive manual operations and visual assessments that are cumbersome and inefficient for biologists. Despite attempts by some scholars to employ machine learning methods, limited datasets and challenges posed by the blurred edges of the medaka ventricle have constrained research to relatively simple tasks such as ventricle localization and heart rate statistics, lacking precise segmentation of the medaka ventricle edges. To address these issues, we initially constructed a video object segmentation dataset comprising over 7000 microscopic images of medaka ventricles. Subsequently, we proposed a semi-supervised video object segmentation model named STAVOS, incorporating a spatial-temporal attention mechanism. Additionally, we developed an automated system capable of calculating various parameters and visualizing results for a medaka ventricle using the provided video. The experimental results demonstrate that STAVOS has successfully achieved precise segmentation of medaka ventricle contours. In comparison to the conventional U-Net model, where a mean accuracy improvement of 0.392 was achieved, our model demonstrates significant progress. Furthermore, when compared to the state-of-the-art Tackling Background Distraction (TBD) model, there is an additional enhancement of 0.038.
Positivity-preserving non-Markovian master equation for open quantum system dynamics: Stochastic Schrödinger equation approach

Authors:
raw_author_name: Wufu Shi
raw_affiliation_string: Physics Department, Stevens Institute of Technology, Hoboken, New Jersey 07030, USA; State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
raw_author_name: Yusui Chen
raw_affiliation_string: Physics Department, New York Institute of Technology, Old Westbury, New York 11568, USA
raw_author_name: Quanzhen Ding
raw_affiliation_string: Physics Department, Stevens Institute of Technology, Hoboken, New Jersey 07030, USA
raw_author_name: Jin Wang
raw_affiliation_string: Department of Chemistry and Department of Physics and Astronomy, State University of New York at Stony Brook, New York 11794-3400, USA
raw_author_name: Ting Yu
raw_affiliation_string: Physics Department, Stevens Institute of Technology, Hoboken, New Jersey 07030, USA

Abstract: Positivity preservation is naturally guaranteed in exact non-Markovian master equations for open quantum system dynamics. However, in many approximated non-Markovian master equations, the positivity of the reduced density matrix is not guaranteed. In this paper we provide a general class of time-local, perturbative, and positivity-preserving non-Markovian master equations generated from stochastic Schr\"odinger equations, particularly quantum-state-diffusion equations. Our method has an expanded range of applicability for accommodating a variety of non-Markovian environments. We show the positivity-preserving master equation for a three-level system coupled to a dissipative bosonic environment as a particular example to exemplify our general approach. We illustrate the numerical simulations with an analysis explaining why the previous approximated non-Markovian master equations cannot guarantee positivity. Our work provides a consistent master equation for studying the non-Markovian dynamics in ultrafast quantum processes and strong-coupling systems.
Power of noisy quantum states and the advantage of resource dilution

Authors:
raw_author_name: Marek Miller
raw_affiliation_string: Centre for Quantum Optical Technologies, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
raw_author_name: Manfredi Scalici
raw_affiliation_string: Centre for Quantum Optical Technologies, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
raw_author_name: Marco Fellous-Asiani
raw_affiliation_string: Centre for Quantum Optical Technologies, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
raw_author_name: Alexander Streltsov
raw_affiliation_string: Centre for Quantum Optical Technologies, Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland

Abstract: Entanglement distillation allows to convert noisy quantum states into singlets, which can, in turn, be used for various quantum technological tasks, such as quantum teleportation and quantum key distribution. Entanglement dilution is the inverse process: singlets are converted into quantum states with less entanglement. While the usefulness of distillation is apparent, practical applications of entanglement dilution are less obvious. Here, we show that entanglement dilution can increase the resilience of shared quantum states to local noise. The increased resilience is observed even if diluting singlets into states with arbitrarily little entanglement. We extend our analysis to other quantum resource theories, such as quantum coherence, quantum thermodynamics, and purity. For these resource theories, we demonstrate that diluting pure quantum states into noisy ones can be advantageous for protecting the system from noise. Our results demonstrate the usefulness of quantum resource dilution, and provide a rare example for an advantage of noisy quantum states over pure states in quantum information processing.
Deep Learning Method for Heliostat Instance Segmentation

Authors:
raw_author_name: Benjamin Liu
raw_affiliation_string: Heliogen
raw_author_name: Brian Brewington
raw_affiliation_string: Heliogen

Abstract: 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

Authors:
raw_author_name: Wuke Zhang
raw_affiliation_string: School of Business, Ningbo University, Ningbo 315211, China
raw_author_name: Xiaofeng Chen
raw_affiliation_string: College of Economics and Management, Zhejiang Agricultural and Forestry University, Hangzhou 310007, China

Abstract: 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

Authors:
raw_author_name: Yang Lu
raw_affiliation_string: Qing Gong College North University of Science and Technology, Tangshan 063000, Hebei, China
raw_author_name: Bin Xie
raw_affiliation_string: Qing Gong College North University of Science and Technology, Tangshan 063000, Hebei, China
raw_author_name: Lili Ma
raw_affiliation_string: Tangshan Preschool Teachers College Yutian Branch, Tangshan 063000, Hebei, China

Abstract: 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

Authors:
raw_author_name: Zhenghan Gao
raw_affiliation_string: Qingdao University of Technology

Abstract: 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

Authors:
raw_author_name: Antonio Macaluso
raw_affiliation_string: Agents and Simulated Reality Department, German Research Center for Artificial Intelligence (DFKI), Saarbruecken, Germany
raw_author_name: Luca Clissa
raw_affiliation_string: Department of Physics and Astronomy, University of Bologna, Bologna, Italy; Istituto Nazionale di Fisica Nucleare (INFN), Bologna, Italy
raw_author_name: Stefano Lodi
raw_affiliation_string: Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
raw_author_name: Claudio Sartori
raw_affiliation_string: Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

Abstract: 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

Authors:
raw_author_name: J. Paul Brooks
raw_affiliation_string: Department of Information Systems, Virginia Commonwealth University, Richmond, Virginia 23284;
raw_author_name: David J. Edwards
raw_affiliation_string: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia 23284;
raw_author_name: Craig E. Larson
raw_affiliation_string: Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, Virginia 23284;
raw_author_name: Nico Van Cleemput
raw_affiliation_string: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium

Abstract: 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

Authors:
raw_author_name: Doudou Zhou
raw_affiliation_string: Department of Biostatistics, Harvard T.H. Chan School of Public Health
raw_author_name: Yufeng Zhang
raw_affiliation_string: Departments of Industrial Engineering and Management Sciences, Northwestern University
raw_author_name: Aaron Sonabend-W
raw_affiliation_string: Department of Biostatistics, Harvard T.H. Chan School of Public Health
raw_author_name: Zhaoran Wang
raw_affiliation_string: Departments of Industrial Engineering and Management Sciences, Northwestern University
raw_author_name: Junwei Lu
raw_affiliation_string: Department of Biostatistics, Harvard T.H. Chan School of Public Health
raw_author_name: Tianxi Cai
raw_affiliation_string: Department of Biostatistics, Harvard T.H. Chan School of Public Health

Abstract: 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

Authors:
raw_author_name: J. B. Ramya
raw_affiliation_string: VIT Business School, Vellore Institute of Technology (VIT), Vellore, India
raw_author_name: Sivakumar Alur
raw_affiliation_string: VIT Business School, Vellore Institute of Technology (VIT), Vellore, India

Abstract: 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

Authors:
raw_author_name: Minni Jain
raw_affiliation_string: Computer Science &amp; Engineering Department, Delhi Technological University (DTU), Delhi, India
raw_author_name: Rajni Jindal
raw_affiliation_string: Computer Science &amp; Engineering Department, Delhi Technological University (DTU), Delhi, India
raw_author_name: Amita Jain
raw_affiliation_string: Netaji Subhas University of Technology (NSUT), East Campus, Delhi, India

Abstract: 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.