https://arxiv.org/api/tWvwlD55AAilkTTKwGT37wdBaIg 2026-09-26T16:14:49Z 40 19 0 http://arxiv.org/abs/2603.26031v1 2026-03-27T03:00:17Z

Prolonged mid-air interaction in virtual reality (VR) causes arm fatigue and discomfort, negatively affecting user experience. Incorporating ergonomic considerations into VR user interface (UI) design typically requires extensive human-in-the-loop evaluation. Although biomechanical models have been used to simulate human behavior in HCI tasks, their application as surrogate users for ergonomic VR UI design remains underexplored. We propose a hierarchical reinforcement learning framework that leverages biomechanical user models to evaluate and optimize VR interfaces for mid-air interaction. A motion agent is trained to perform button-press tasks in VR under sequential conditions, using realistic movement strategies and estimating muscle-level effort via a validated three-compartment control with recovery (3CC-r) fatigue model. The simulated fatigue output serves as feedback for a UI agent that optimizes UI element layout via reinforcement learning (RL) to minimize fatigue. We compare the RL-optimized layout against a manually-designed centered baseline and a Bayesian optimized baseline. Results show that fatigue trends from the biomechanical model align with human user data. Moreover, the RL-optimized layout using simulated fatigue feedback produced significantly lower perceived fatigue in a follow-up human study. We further demonstrate the framework's extensibility via a simulated case study on longer sequential tasks with non-uniform interaction frequencies. To our knowledge, this is the first work using simulated biomechanical muscle fatigue as a direct optimization signal for VR UI layout design. Our findings highlight the potential of biomechanical user models as effective surrogate tools for ergonomic VR interface design, enabling efficient early-stage iteration with less reliance on extensive human participation.

2026-03-27T03:00:17Z Harshitha Voleti Charalambos Poullis http://arxiv.org/abs/2602.09772v1 2026-02-10T13:34:00Z

The possibility to create reactive robot programs faster without the need for extensively trained programmers is becoming increasingly important. So far, it has not been explored how various techniques for creating Behavior Tree (BT) program representations could be combined with complete graphical user interfaces (GUIs) to allow a human user to validate and edit trees suggested by automated methods. In this paper, we introduce BEhavior TRee GUI (BETR-GUI) for creating BTs with the help of an AI assistant that combines methods using large language models, planning, genetic programming, and Bayesian optimization with a drag-and-drop editor. A user study with 60 participants shows that by combining different assistive methods, BETR-GUI enables users to perform better at solving the robot programming tasks. The results also show that humans using the full variant of BETR-GUI perform better than the AI assistant running on its own.

2026-02-10T13:34:00Z Jonathan Styrud Matteo Iovino Rebecca Stower Mart Kartašev Mikael Norrlöf Mårten Björkman Christian Smith http://arxiv.org/abs/2512.14733v1 2025-12-11T04:03:44Z

Exploration is essential to improve long-term recommendation quality, but it often degrades short-term business performance, especially in remote-first TV environments where users engage passively, expect instant relevance, and offer few chances for correction. This paper introduces an approach for delivering content-level exploration safely and efficiently by optimizing its placement based on reach and opportunity cost. Deployed on a large-scale streaming platform with over 100 million monthly active users, our approach identifies scroll-depth regions with lower engagement and strategically introduces a dedicated container, the "Something Completely Different" row containing randomized content. Rather than enforcing exploration uniformly across the user interface (UI), we condition its appearance on empirically low-cost, high-reach positions to ensure minimal tradeoff against platform-level watch time goals. Extensive A/B testing shows that this strategy preserves business metrics while collecting unbiased interaction data. Our method complements existing intra-row diversification and bandit-based exploration techniques by introducing a deployable, behaviorally informed mechanism for surfacing exploratory content at scale. Moreover, we demonstrate that the collected unbiased data, integrated into downstream candidate generation, significantly improves user engagement, validating its value for recommender systems.

2025-12-11T04:03:44Z IEEE Conference on Cognitive Machine Intelligence Nov. 11-14, 2025, Pittsburgh, PA, USA Qiang Chen Venkatesh Ganapati Hegde http://arxiv.org/abs/2510.05800v1 2025-10-07T11:18:35Z

Simulations are valuable tools for empirically evaluating the properties of statistical methods and are primarily employed in methodological research to draw general conclusions about methods. In addition, they can often be useful to applied statisticians, who may rely on published simulation results to select an appropriate statistical method for their application. However, on the one hand, applying published simulation results directly to practical settings is frequently challenging, as the scenarios considered in methodological studies rarely align closely enough with the characteristics of specific real-world applications to be truly informative. Applied statisticians, on the other hand, may struggle to construct their own simulations or to adapt methodological research to better reflect their specific data due to time constraints and limited programming expertise. We propose bridging this gap between methodological research and statistical practice through a translational approach by developing dedicated software along with simulation studies, which should abstract away the coding-intensive aspects of running simulations while still offering sufficient flexibility in parameter selection to meet the needs of applied statisticians. We demonstrate this approach using two practical examples, illustrating that the concept of translational simulation can be implemented in practice in different ways. In the first example - simulation-based evaluation of power in two-arm randomized clinical trials with an ordinal endpoint - the solution we discuss is a Shiny web application providing a graphical user interface for running informative simulations in this context. For the second example - assessing the impact of measurement error in multivariable regression - a less labor-intensive approach is suggested, involving the provision of user-friendly, well-structured, and modular analysis code.

2025-10-07T11:18:35Z Anne-Laure Boulesteix Patrick Callahan Luzia Hanssum Vincent Gaertner Eva Hoster http://arxiv.org/abs/2508.16077v1 2025-08-22T04:12:03Z

Designing successful interactions requires identifying optimal design parameters. To do so, designers often conduct iterative user testing and exploratory trial-and-error. This involves balancing multiple objectives in a high-dimensional space, making the process time-consuming and cognitively demanding. System-led optimization methods, such as those based on Bayesian optimization, can determine for designers which parameters to test next. However, they offer limited opportunities for designers to intervene in the optimization process, negatively impacting the designer's experience. We propose a design optimization framework that enables natural language interactions between designers and the optimization system, facilitating cooperative design optimization. This is achieved by integrating system-led optimization methods with Large Language Models (LLMs), allowing designers to intervene in the optimization process and better understand the system's reasoning. Experimental results show that our method provides higher user agency than a system-led method and shows promising optimization performance compared to manual design. It also matches the performance of an existing cooperative method with lower cognitive load.

2025-08-22T04:12:03Z 25 pages, 20 figures, to appear in Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST '25), September 28-October 1, 2025, Busan, Republic of Korea Ryogo Niwa Shigeo Yoshida Yuki Koyama Yoshitaka Ushiku 10.1145/3746059.3747789 http://arxiv.org/abs/2501.11829v2 2025-01-24T08:27:38Z

Automated Urban Air Mobility (UAM) can improve passenger transportation and reduce congestion, but its success depends on passenger trust. While initial research addresses passengers' information needs, questions remain about how to simulate air taxi flights and how these simulations impact users and interface requirements. We conducted a between-subjects study (N=40), examining the influence of motion fidelity in Virtual-Reality-simulated air taxi flights on user effects and interface design. Our study compared simulations with and without motion cues using a 3-Degrees-of-Freedom motion chair. Optimizing the interface design across six objectives, such as trust and mental demand, we used multi-objective Bayesian optimization to determine the most effective design trade-offs. Our results indicate that motion fidelity decreases users' trust, understanding, and acceptance, highlighting the need to consider motion fidelity in future UAM studies to approach realism. However, minimal evidence was found for differences or equality in the optimized interface designs, suggesting personalized interface designs.

2025-01-21T02:15:17Z Luca-Maxim Meinhardt Clara Schramm Pascal Jansen Mark Colley Enrico Rukzio 10.1145/3706598.3713288 http://arxiv.org/abs/2203.05620v2 2022-03-14T17:26:02Z

Monte Carlo radiation transport simulations have clearly contributed to improve the design of nuclear systems. When performing in-beam or shielding simulations a complexity arises due to the fact that particles must be tracked to regions far from the original source or behind the shielding, often lacking sufficient statistics. Different possibilities to overcome this problem such as using particle lists or generating synthetic sources have already been reported. In this work we present a new approach by using the adaptive multivariate kernel density estimator (KDE) method. This concept was implemented in KDSource, a general tool for modelling, optimizing and sampling KDE sources, which provides a convenient user interface. The basic properties of the method were studied in an analytical problem with a known density distribution. Furthermore, the tool was used in two Monte Carlo simulations that modelled neutron beams, which showed good agreement with experimental results.

2022-03-10T20:13:16Z N. S. Schmidt O. I. Abbate Z. M. Prieto J. I. Robledo J. I. Márquez Damián A. A. Márquez J. Dawidowski http://arxiv.org/abs/2202.11571v3 2022-06-30T11:34:20Z

SporTran is a Python utility designed to estimate generic transport coefficients in extended systems, based on the Green-Kubo theory of linear response and the recently introduced cepstral analysis of the current time series generated by molecular dynamics simulations. SporTran can be applied to univariate as well as multivariate time series. Cepstral analysis requires minimum discretion from the user, in that it weakly depends on two parameters, one of which is automatically estimated by a statistical model-selection criterion that univocally determine the resulting accuracy. In order to facilitate the optimal refinement of these parameters, SporTran features an easy-to-use graphical user interface. A command-line interface and a Python API, easy to embed in complex data-analysis workflows, are also provided.

2022-02-23T15:40:53Z 11 pages, 3 figures Comput. Phys. Commun. 280, 108470 (2022) Loris Ercole Riccardo Bertossa Sebastiano Bisacchi Stefano Baroni 10.1016/j.cpc.2022.108470 http://arxiv.org/abs/2109.10020v2 2021-09-22T04:41:55Z

Predicting metrics associated with entities' transnational behavior within payment processing networks is essential for system monitoring. Multivariate time series, aggregated from the past transaction history, can provide valuable insights for such prediction. The general multivariate time series prediction problem has been well studied and applied across several domains, including manufacturing, medical, and entomology. However, new domain-related challenges associated with the data such as concept drift and multi-modality have surfaced in addition to the real-time requirements of handling the payment transaction data at scale. In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database. We propose a model with five unique components to estimate the transaction metrics from multi-modality data. Four of these components capture interaction, temporal, scale, and shape perspectives, and the fifth component fuses these perspectives together. We also propose a hybrid offline/online training scheme to address concept drift in the data and fulfill the real-time requirements. Combining the estimation model with a graphical user interface, the prototype transaction metric estimation system has demonstrated its potential benefit as a tool for improving a payment processing company's system monitoring capability.

2021-09-21T08:23:27Z 10 pages Chin-Chia Michael Yeh Zhongfang Zhuang Junpeng Wang Yan Zheng Javid Ebrahimi Ryan Mercer Liang Wang Wei Zhang 10.1145/3459637.3481942 http://arxiv.org/abs/2106.15519v1 2021-06-21T16:54:04Z

We present MultivariatePowerSeries, a Maple library introduced in Maple 2021, providing a variety of methods to study formal multivariate power series and univariate polynomials over such series. This library offers a simple and easy-to-use user interface. Its implementation relies on lazy evaluation techniques and takes advantage of Maple's features for object-oriented programming. The exposed methods include Weierstrass Preparation Theorem and factorization via Hensel's lemma. The computational performance is demonstrated by means of an experimental comparison with software counterparts.

2021-06-21T16:54:04Z Mohammadali Asadi Alexander Brandt Mahsa Kazemi Marc Moreno Maza Erik Postma http://arxiv.org/abs/2106.07914v3 2021-11-02T21:00:59Z

We study the problem of off-policy evaluation from batched contextual bandit data with multidimensional actions, often termed slates. The problem is common to recommender systems and user-interface optimization, and it is particularly challenging because of the combinatorially-sized action space. Swaminathan et al. (2017) have proposed the pseudoinverse (PI) estimator under the assumption that the conditional mean rewards are additive in actions. Using control variates, we consider a large class of unbiased estimators that includes as specific cases the PI estimator and (asymptotically) its self-normalized variant. By optimizing over this class, we obtain new estimators with risk improvement guarantees over both the PI and the self-normalized PI estimators. Experiments with real-world recommender data as well as synthetic data validate these improvements in practice.

2021-06-15T06:59:53Z NeurIPS 2021 Nikos Vlassis Ashok Chandrashekar Fernando Amat Gil Nathan Kallus http://arxiv.org/abs/2104.05959v1 2021-04-13T06:20:54Z

We present AutoOED, an Optimal Experiment Design platform powered with automated machine learning to accelerate the discovery of optimal solutions. The platform solves multi-objective optimization problems in time- and data-efficient manner by automatically guiding the design of experiments to be evaluated. To automate the optimization process, we implement several multi-objective Bayesian optimization algorithms with state-of-the-art performance. AutoOED is open-source and written in Python. The codebase is modular, facilitating extensions and tailoring the code, serving as a testbed for machine learning researchers to easily develop and evaluate their own multi-objective Bayesian optimization algorithms. An intuitive graphical user interface (GUI) is provided to visualize and guide the experiments for users with little or no experience with coding, machine learning, or optimization. Furthermore, a distributed system is integrated to enable parallelized experimental evaluations by independent workers in remote locations. The platform is available at https://autooed.org.

2021-04-13T06:20:54Z Yunsheng Tian Mina Konaković Luković Timothy Erps Michael Foshey Wojciech Matusik http://arxiv.org/abs/2010.08859v1 2020-10-17T20:30:03Z

We, as a society, need artists to help us interpret and explain science, but what does an artist's studio look like when today's science is built upon the language of large, increasingly complex data? This paper presents a data visualization design interface that lifts the barriers for artists to engage with actively studied, 3D multivariate datasets. To accomplish this, the interface must weave together the need for creative artistic processes and the challenging constraints of real-time, data-driven 3D computer graphics. The result is an interface for a technical process, but technical in the way artistic printmaking is technical, not in the sense of computer scripting and programming. Using metaphor, computer graphics algorithms and shader program parameters are reimagined as tools in an artist's printmaking studio. These artistic metaphors and language are merged with a puzzle-piece approach to visual programming and matching iconography. Finally, artists access the interface using a web browser, making it possible to design immersive multivariate data visualizations that can be displayed in VR and AR environments using familiar drawing tablets and touch screens. We report on insights from the interdisciplinary design of the interface and early feedback from artists.

2020-10-17T20:30:03Z Bridger Herman Francesca Samsel Annie Bares Seth Johnson Greg Abram Daniel F. Keefe http://arxiv.org/abs/2006.11873v1 2020-06-21T18:43:06Z

Facing growing competition from online rivals, the retail industry is increasingly investing in their online shopping platforms to win the high-stake battle of customer' loyalty. User experience is playing an essential role in this competition, and retailers are continuously experimenting and optimizing their user interface for better user experience. The cost of experimentation is dominated by the opportunity cost of providing a suboptimal service to the customers. Through this paper, we demonstrate the effectiveness of opportunistic bandits to make the experiments as inexpensive as possible using real online retail data. In fact, we model user interface experimentation as an opportunistic bandit problem, in which the cost of exploration varies under a factor extracted from customer features. We achieve significant regret reduction by mitigating costly exploration and providing extra contextual information that helps to guide the testing process. Moreover, we analyze the advantages and challenges of using opportunistic bandits for online retail experimentation.

2020-06-21T18:43:06Z SDM Data Science for Retail and E-Commerce Workshop (2020) Nader Bouacida Amit Pande Xin Liu http://arxiv.org/abs/2003.04208v1 2020-03-09T15:48:29Z

Principal Moment Analysis is a method designed for dimension reduction, analysis and visualization of high dimensional multivariate data. It generalizes Principal Component Analysis and allows for significant statistical modeling flexibility, when approximating an unknown underlying probability distribution, by enabling direct analysis of general approximate measures. Through https://principalmomentanalysis.github.io/ we provide an implementation, together with a graphical user interface, of a simplex based version of Principal Moment Analysis.

2020-03-09T15:48:29Z 10 pages, 0 figures Magnus Fontes Rasmus Henningsson http://arxiv.org/abs/1801.08002v1 2018-01-24T14:37:53Z

The numerical availability of statistical inference methods for a modern and robust analysis of longitudinal- and multivariate data in factorial experiments is an essential element in research and education. While existing approaches that rely on specific distributional assumptions of the data (multivariate normality and/or characteristic covariance matrices) are implemented in statistical software packages, there is a need for user-friendly software that can be used for the analysis of data that do not fulfill the aforementioned assumptions and provide accurate p-value and confidence interval estimates. Therefore, newly developed statistical methods for the analysis of repeated measures designs and multivariate data that neither assume multivariate normality nor specific covariance matrices have been implemented in the freely available R-package MANOVA.RM. The package is equipped with a graphical user interface for plausible applications in academia and other educational purpose. Several motivating examples illustrate the application of the methods.

2018-01-24T14:37:53Z Sarah Friedrich Frank Konietschke Markus Pauly http://arxiv.org/abs/1606.03966v2 2017-05-09T14:41:15Z

Applications and systems are constantly faced with decisions that require picking from a set of actions based on contextual information. Reinforcement-based learning algorithms such as contextual bandits can be very effective in these settings, but applying them in practice is fraught with technical debt, and no general system exists that supports them completely. We address this and create the first general system for contextual learning, called the Decision Service. Existing systems often suffer from technical debt that arises from issues like incorrect data collection and weak debuggability, issues we systematically address through our ML methodology and system abstractions. The Decision Service enables all aspects of contextual bandit learning using four system abstractions which connect together in a loop: explore (the decision space), log, learn, and deploy. Notably, our new explore and log abstractions ensure the system produces correct, unbiased data, which our learner uses for online learning and to enable real-time safeguards, all in a fully reproducible manner. The Decision Service has a simple user interface and works with a variety of applications: we present two live production deployments for content recommendation that achieved click-through improvements of 25-30%, another with 18% revenue lift in the landing page, and ongoing applications in tech support and machine failure handling. The service makes real-time decisions and learns continuously and scalably, while significantly lowering technical debt.

2016-06-13T14:17:00Z Alekh Agarwal Sarah Bird Markus Cozowicz Luong Hoang John Langford Stephen Lee Jiaji Li Dan Melamed Gal Oshri Oswaldo Ribas Siddhartha Sen Alex Slivkins http://arxiv.org/abs/1301.1942v2 2016-01-10T16:01:22Z

Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high-dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple, has important invariance properties, and applies to domains with both categorical and continuous variables. We present a thorough theoretical analysis of REMBO. Empirical results confirm that REMBO can effectively solve problems with billions of dimensions, provided the intrinsic dimensionality is low. They also show that REMBO achieves state-of-the-art performance in optimizing the 47 discrete parameters of a popular mixed integer linear programming solver.

2013-01-09T18:26:56Z 33 pages Ziyu Wang Frank Hutter Masrour Zoghi David Matheson Nando de Freitas http://arxiv.org/abs/physics/0703039v5 2009-07-07T20:15:27Z

In high-energy physics, with the search for ever smaller signals in ever larger data sets, it has become essential to extract a maximum of the available information from the data. Multivariate classification methods based on machine learning techniques have become a fundamental ingredient to most analyses. Also the multivariate classifiers themselves have significantly evolved in recent years. Statisticians have found new ways to tune and to combine classifiers to further gain in performance. Integrated into the analysis framework ROOT, TMVA is a toolkit which hosts a large variety of multivariate classification algorithms. Training, testing, performance evaluation and application of all available classifiers is carried out simultaneously via user-friendly interfaces. With version 4, TMVA has been extended to multivariate regression of a real-valued target vector. Regression is invoked through the same user interfaces as classification. TMVA 4 also features more flexible data handling allowing one to arbitrarily form combined MVA methods. A generalised boosting method is the first realisation benefiting from the new framework.

2007-03-04T19:45:41Z TMVA-v4 Users Guide: 135 pages, 19 figures, numerous code examples and references A. Hoecker P. Speckmayer J. Stelzer J. Therhaag E. von Toerne H. Voss M. Backes T. Carli O. Cohen A. Christov D. Dannheim K. Danielowski S. Henrot-Versille M. Jachowski K. Kraszewski A. Krasznahorkay M. Kruk Y. Mahalalel R. Ospanov X. Prudent A. Robert D. Schouten F. Tegenfeldt A. Voigt K. Voss M. Wolter A. Zemla