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Collaboration with Conversational AI Assistants for UX Evaluation: Questions and How to Ask them (Voice vs. Text)

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arXiv 2023年

作者： Kuang, Emily Soure, Ehsan Jahangirzadeh Fan, Mingming Zhao, Jian Shinohara, Kristen Golisano College of Computing and Information Sciences Rochester Institute of Technology United States School of Computer Science University of Waterloo Canada Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou China Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology China School of Information Rochester Institute of Technology United States

AI is promising in assisting UX evaluators with analyzing usability tests, but its judgments are typically presented as non-interactive visualizations. Evaluators may have questions about test recordings, but have no way of asking them. Interactive conversational assistants provide a Q&A dynamic that may improve analysis efficiency and evaluator autonomy. To understand the full range of analysis-related questions, we conducted a Wizard-of-Oz design probe study with 20 participants who interacted with simulated AI assistants via text or voice. We found that participants asked for five categories of information: user actions, user mental model, help from the AI assistant, product and task information, and user demographics. Those who used the text assistant asked more questions, but the question lengths were similar. The text assistant was perceived as significantly more efficient, but both were rated equally in satisfaction and trust. We also provide design considerations for future conversational AI assistants for UX evaluation. © 2023, CC BY-NC-ND.

关键词： Visualization

Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

POINTWISE ESTIMATES FOR THE FUNDAMENTAL SOLUTIONS OF HIGHER ORDER SCHRÖDINGER EQUATIONS IN ODD DIMENSIONS I: LOW DIMENSIONAL CASE

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arXiv 2024年

In this paper, for any odd n and any integer m ≥ 1 with n tu(x, t) = ((−∆)m + V(x))u(x, t), t ∈ , x ∈ n, where V is a real-valued potential with certain decay. Let Pac(H) denote the projection onto the absolutely continuous spectrum space of H = (−∆)m + V, and assume that H has no positive embedded eigenvalue. Our main result says that e−itHPac(H) has integral kernel K(t, x, y) satisfying (Equation presented), where the constants C, h > 0, and h can be specified by m, n and the zero energy resonances of H. A similar result for smoothing operators like (Equation presented) is also given. One of the main novelties lies in a new and unified approach to obtain the asymptotic expansions of perturbed resolvents under various types of zero energy resonances. © 2024, CC0.

关键词： Schrodinger equation

Biomechanics-informed Non-rigid Medical Image Registration and its Inverse Material Property Estimation with Linear and Nonlinear Elasticity

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arXiv 2024年

作者： Min, Zhe Baum, Zachary M.C. Saeed, Shaheer U. Emberton, Mark Barratt, Dean C. Taylor, Zeike A. Hu, Yipeng School of Control Science and Engineering Shandong University Jinan China Centre for Medical Image Computing and Wellcome EPSRC Centre for Interventional & Surgical Sciences University College London London United Kingdom Division of Surgery & Interventional Science University College London London United Kingdom CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine Institute of Medical and Biological Engineering University of Leeds Leeds United Kingdom

This paper investigates both biomechanical-constrained nonrigid medical image registrations and accurate identifications of material properties for soft tissues, using physics-informed neural networks (PINNs). The nonlinear elasticity theory is leveraged to formally establish the partial differential equations (PDEs) representing physics laws of biomechanical constraints to be satisfied, with which registration and identification tasks are treated as forward (i.e., data-driven solutions of PDEs) and inverse (i.e., parameter estimation) problems under PINNs respectively. While the forward problem has direct clinical applications in guiding targeted biopsy and treatment, the solution to the inverse problem may open new research directions in quantifying disease-indicative mechanical properties of in vivo tissues. Two net configurations (i.e., Cfg1 and Cfg2) have also been compared for both linear and nonlinear physics models, according to whether backbones are shared between branches or not. Two sets of experiments have been conducted, using pairs of undeformed and deformed MR images from clinical cases of prostate cancer biopsy. In the first experiment, against the finite-element-computed ground-truth, the root mean squared error (rmse) of registration for surface points was reduced from 1.83±0.51 mm without PINNs to 1.43±0.70 mm (Cfg1, p = 0.024) and 1.23 ± 0.69 mm (Cfg2, p © 2024, CC BY-NC-ND.

关键词： Biomechanics

Trust-aware Routing Protocol using Modified Sand Cat Swarm Optimization in Iot-Based WSN

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Trust-aware Routing Protocol using Modified Sand Cat Swarm O...

Evolutionary Algorithms and Soft computing Techniques (EASCT), International Conference on

作者： Nagendar Yamsani Anitha D Y. M. Mahaboob John M. Jamuna Rani Sowmya M School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computational Intelligence School of computing SRM Institute of Science and Technology Kattankulathur India Ece Department Mahendra College Of Engineering Salem India Department of ECE Sona College of Technology Salem India Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India

The trust-aware secure route selection leverages trust-based metrics for evaluating the credibility of nodes and routes which enables the selection of the most reliable and secure paths for data transmission. Secure routing is of major significance for preventing wireless devices from other threats and effective characteristics in WSN attain an efficient secure routing selection. This research proposed the trust secure route selection utilizing Modified Sand Cat Swarm Optimization (TSRS-MSCSO) for efficient route selection. Initially, the secure nodes are identified by using energy and delay of the fitness function. After identifying secure nodes, the route selection is performed through the Modified Sand Cat Swarm Optimization algorithm. The performance of the proposed model is estimated by metrics like packet delivery ratio, throughput, delay and energy consumption with various nodes of 50, 100, 150, and 200. The proposed model attained a high packet delivery ratio of 98.8%, less delay of 3s, high throughput of 96.27%, and less energy consumption of 2.14J for 50 nodes which ensures better performance than other existing methods like Dynamic Deep Neural Network-based Particle Swarm Optimization (DDNN-PSO), Chaotic Bumble Bees Mating Optimization- Trust Sensing Model (CBBMOR-TSM) and fuzzy TOPSIS based hybrid Jarratt Butterfly Optimization (FTOPSIS-HJBO) models.

关键词：

New semidefinite relaxations for a class of complex quadratic programming problems

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arXiv 2023年

作者： Xu, Yingzhe Lu, Cheng Deng, Zhibin Liu, Ya-Feng School of Economics and Management North China Electric Power University Beijing102206 China School of Economics and Management University of Chinese Academy of Sciences MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing100190 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

In this paper, we propose some new semidefinite relaxations for a class of nonconvex complex quadratic programming problems, which widely appear in the areas of signal processing and power system. By deriving new valid constraints to the matrix variables in the lifted space, we derive some enhanced semidefinite relaxations of the complex quadratic programming problems. Then, we compare the proposed semidefinite relaxations with existing ones, and show that the newly proposed semidefinite relaxations could be strictly tighter than the previous ones. Moreover, the proposed semidefinite relaxations can be applied to more general cases of complex quadratic programming problems, whereas the previous ones are only designed for special cases. Numerical results indicate that the proposed semidefinite relaxations © 2023, CC BY.

关键词： Approximation algorithms

A RIEMANNIAN EXPONENTIAL AUGMENTED LAGRANGIAN METHOD FOR computing THE PROJECTION ROBUST WASSERSTEIN DISTANCE

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arXiv 2022年

作者： Jiang, Bo Liu, Ya-Feng Key Laboratory for NSLSCS of Jiangsu Province School of Mathematical Sciences Nanjing Normal University Nanjing China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

Projecting the distance measures onto a low-dimensional space is an efficient way of mitigating the curse of dimensionality in the classical Wasserstein distance using optimal transport. The obtained maximized distance is referred to as projection robust Wasserstein (PRW) distance. In this paper, we equivalently reformulate the computation of the PRW distance as an optimization problem over the Cartesian product of the Stiefel manifold and the Euclidean space with additional nonlinear inequality constraints. We propose a Riemannian exponential augmented Lagrangian method (ReALM) with a global convergence guarantee to solve this problem. Compared with the existing approaches, ReALM can potentially avoid too small penalty parameters. Moreover, we propose a framework of inexact Riemannian gradient descent methods to solve the subproblems in ReALM efficiently. In particular, by using the special structure of the subproblem, we give a practical algorithm named as the inexact Riemannian Barzilai-Borwein method with Sinkhorn iteration (iRBBS). The remarkable features of iRBBS lie in that it performs a flexible number of Sinkhorn iterations to compute an inexact gradient with respect to the projection matrix of the problem and adopts the Barzilai-Borwein stepsize based on the inexact gradient information to improve the performance. We show that iRBBS can return an ǫ-stationary point of the original PRW distance problem within O(ǫ−3) iterations. Extensive numerical results on synthetic and real datasets demonstrate that our proposed ReALM as well as iRBBS outperform the state-of-the-art solvers for computing the PRW *** Codes 65K10, 90C26, 90C47 Copyright © 2022, The Authors. All rights reserved.

关键词： Constrained optimization

A predictor-corrector deep learning algorithm for high dimensional stochastic partial differential equations

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arXiv 2022年

作者： Zhang, He Zhang, Ran Zhou, Tao School of Mathematics Jilin University Jilin Changchun130012 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

In this paper, we present a deep learning-based numerical method for approximating high dimensional stochastic partial differential equations (SPDEs). At each time step, our method relies on a predictor-corrector procedure. More precisely, we decompose the original SPDE into a degenerate SPDE and a deterministic PDE. Then in the prediction step, we solve the degenerate SPDE with the Euler scheme, while in the correction step we solve the second-order deterministic PDE by deep neural networks via its equivalent backward stochastic differential equation (BSDE). Under standard assumptions, error estimates and the rate of convergence of the proposed algorithm are presented. The efficiency and accuracy of the proposed algorithm are illustrated by numerical examples. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

Benchmarking Machine Learning Models to Predict Corporate Bankruptcy

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arXiv 2022年

作者： Alanis, Emmanuel Chava, Sudheer Shah, Agam McCoy College of Business Texas State University United States Scheller College of Business Georgia Institute of Technology United States School of Computational Science & Engineering College of Computing Georgia Institute of Technology United States

Using a comprehensive sample of 2, 585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U.S. firms. We find that gradient boosted trees outperform other models in one-year-ahead forecasts. Variable permutation tests show that excess stock returns, idiosyncratic risk, and relative size are the more important variables for predictions. Textual features derived from corporate filings do not improve performance materially. In a credit competition model that accounts for the asymmetric cost of default misclassification, the survival random forest is able to capture large dollar profits. © 2022, CC BY.

关键词： Benchmarking