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Beyond Pass or Fail: Multi-Dimensional Benchmarking of Foundation Models for Goal-based Mobile UI Navigation

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arXiv 2025年

作者： Ran, Dezhi Wu, Mengzhou Yu, Hao Li, Yuetong Ren, Jun Cao, Yuan Zeng, Xia Lu, Haochuan Xu, Zexin Xu, Mengqian Su, Ting Yao, Liangchao Xiong, Ting Yang, Wei Deng, Yuetang Marron, Assaf Harel, David Xie, Tao MOE SCS Peking University Beijing China School of EECS Peking University Beijing China School of Software and Microelectronics Peking University Beijing China The University of Chicago Chicago United States University of Texas at Dallas Dallas United States Tencent Inc Shenzhen China East China Normal University Shanghai China Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

Recent advances of foundation models (FMs) have made navigating mobile applications (apps) based on high-level goal instructions within reach, with significant industrial applications such as UI testing. While existing benchmarks evaluate FM-based UI navigation using the binary pass/fail metric, they have two major limitations: they cannot reflect the complex nature of mobile UI navigation where FMs may fail for various reasons (e.g., misunderstanding instructions and failed planning), and they lack industrial relevance due to oversimplified tasks that poorly represent real-world scenarios. To address the preceding limitations, we propose Sphinx, a comprehensive benchmark for multi-dimensional evaluation of FMs in industrial settings of UI navigation. Sphinx introduces a specialized toolkit that evaluates five essential FM capabilities, providing detailed insights into failure modes such as insufficient app knowledge or planning issues. Using both popular Google Play applications and WeChat’s internal UI test cases, we evaluate 8 FMs with 20 different configurations. Our results show that existing FMs universally struggle with goal-based testing tasks, primarily due to insufficient UI-specific capabilities. We summarize seven lessons learned from benchmarking FMs with Sphinx, providing clear directions for improving FM-based mobile UI navigation. Copyright © 2025, The Authors. All rights reserved.

关键词： Model checking

Optimizing Airline Networks: A Comparative Analysis of Graph-Based Techniques

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Optimizing Airline Networks: A Comparative Analysis of Graph...

IT and Industrial Technologies (ICIT), International Conference on

作者： Mahnoor Iftikhar Raja Hashim Ali Memoona Saleem Nazia Shahzadi Usama Arshad Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Faculty of Computer Science & Engg GIK Institute of Engg. Sciences & Tech Khyber Pakhtunkhwa Pakistan Faculty of Computer Science & Engg AI Research Group GIK Institute of Engg. Sciences & Tech Khyber Pakhtunkhwa Pakistan Dept. of Tech & Software Engg Univ. of Europe of Applied Sciences Berlin Germany Faculty of Electronic Systems Engg University of Regina Regina Canada

In the vast expanse of global travel, the intricate web of interconnected airline networks serves as the lifeline of modern aviation. Amidst the bustling realm of global aviation, where time and resources are of the essence, the optimization of airline networks emerges as a paramount pursuit, unleashing the power to streamline operations, enhance connectivity, and elevate the travel experience for passengers worldwide. In this paper, four prominent graph-based algorithms, namely A*, Bellman-Ford, bidirectional search, and Dijkstra’s algorithm, were used and compared for optimization of airline networks. These algorithms leverage the inherent structure of the airways network, represented as a graph, to determine the most efficient routes. By evaluating the execution times, path lengths, and complexities of these algorithms, this research equips the airline industry with valuable insights for optimizing airways path-finding. With optimized path-finding, the skies become more navigable, fostering a seamless travel experience for passengers and bolstering the economic growth driven by the aviation sector.

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Park-Space: A Parking Space Indicator

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Park-Space: A Parking Space Indicator

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Dhairya Khanduja Janvi Mittal Tanupriya Choudhury Ketan Kotecha Manhar Singh Suryansh Sinha School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India CSE Dept. Graphic Era (Deemed to be University) Dehradun Uttarakhand India CSE Dept. Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

The Parking Spot Indicator Application is a software program that aims to simplify the process of finding parking spaces in busy lots for drivers. Utilizing sensors installed in each spot, it monitors their occupancy status in real-time and presents this information through an easy-to-use interface. This application offers drivers, lot operators, and the environment several benefits. Drivers can save time and reduce stress by avoiding the hassle of searching for a spot, thanks to the real-time updates provided by the application. Operators can optimize resources, reduce congestion, and increase revenue by effectively managing their lots. Additionally, this application helps minimize traffic congestion and emissions by reducing circling time spent looking for parking spots. It is designed to be scalable and adaptable to various types of lots, including outdoor areas, multi-level structures, and underground facilities. The user-friendly interface provides a simple experience with a real-time map displaying available spots highlighted.

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Post-Quantum Security of the Even-Mansour Cipher

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arXiv 2021年

作者： Alagic, Gorjan Bai, Chen Katz, Jonathan Majenz, Christian QuICS University of Maryland NIST United States Dept. of Electrical and Computer Engineering University of Maryland United States Dept. of Computer Science University of Maryland United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

The Even-Mansour cipher is a simple method for constructing a (keyed) pseudorandom permutation E from a public random permutation P : {0, 1}n → {0, 1}n. It is secure against classical attacks, with optimal attacks requiring qE queries to E and qP queries to P such that qE · qP ≈ 2n. If the attacker is given quantum access to both E and P, however, the cipher is completely insecure, with attacks using qE, qP = O(n) queries known. In any plausible real-world setting, however, a quantum attacker would have only classical access to the keyed permutation E implemented by honest parties, even while retaining quantum access to P. Attacks in this setting with qE · qP2 ≈ 2n are known, showing that security degrades as compared to the purely classical case, but leaving open the question as to whether the Even-Mansour cipher can still be proven secure in this natural, "post-quantum" setting. We resolve this question, showing that any attack in that setting requires qE · qP2 + qP · qE2 ≈ 2n. Our results apply to both the two-key and single-key variants of Even-Mansour. Along the way, we establish several generalizations of results from prior work on quantum-query lower bounds that may be of independent interest. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum cryptography

Gap Edit Distance via Non-Adaptive Queries: Simple and Optimal

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Gap Edit Distance via Non-Adaptive Queries: Simple and Optim...

Annual IEEE Symposium on Foundations of computer science

作者： Elazar Goldenberg Tomasz Kociumaka Robert Krauthgamer Barna Saha School of Computer Science The Academic College of Tel Aviv-Yaffo Yaffo Israel Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Dept. of Computer Science & Applied Mathematics Weizmann Institute of Science Rehovot Israel University of California San Diego San Diego CA USA

ISBN: (数字)9781665455190

ISBN: (纸本)9781665455206

We study the problem of approximating edit distance in sublinear time. This is formalized as the $(k,\ k^{\mathrm{c}})$-GAP EDIT DISTANCE problem, where the input is a pair of strings $X, \mathrm{Y}$ and parameters $k, c\gt 1$, and the goal is to return YES if ED(X, Y) $\leq k$, NO if ED(X, Y) $\gt k^{\mathrm{c}}$, and an arbitrary answer when $k\lt $ ED(X, Y) $\leq k^{\mathrm{c}}$. Recent years have witnessed significant interest in designing sublinear-time algorithms for GAP EDIT *** this work, we resolve the non-adaptive query complexity of GAP EDIT DISTANCE for the entire range of parameters, improving over a sequence of previous results. Specifically, we design a non-adaptive algorithm with query complexity $\tilde{O}(n/k^{\mathrm{c}-\mathrm{O}.5})$, and we further prove that this bound is optimal up to polylogarithmic *** algorithm also achieves optimal time complexity $\tilde{O}(n/k^{\mathrm{c}-\mathrm{O}.5})$ whenever $ c\geq$ 1.5. For $1 \lt c\lt $ 1.5, the running time of our algorithm is $\tilde{O}(n/k^{2\mathrm{c}-2})$. In the restricted case of $k^{\mathrm{c}}=\Omega(n)$, this matches a known result [Batu, Ergün, Kilian, Magen, Raskhodnikova, Rubinfeld, and Sami; STOC 2003], and in all other (nontrivial) cases, our running time is strictly better than all previous algorithms, including the adaptive ones. However, independent work of Bringmann, Cassis, Fischer, and Nakos [STOC 2022] provides an adaptive algorithm that bypasses the non-adaptive lower bound, but only for small enough k and c.

关键词： computer science Adaptive algorithms Complexity theory Time complexity

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

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arXiv 2022年

作者： Wang, Xiang Yang, Yimin Guo, Zhichang Zhou, Zhili Liu, Yu Pang, Qixiang Du, Shan The Dept. of Computer Science Lakehead University Thunder BayON Canada The Dept. of electrical and computer engineering Western University LondonON Canada The School of Mathematics Harbin Institute of Technology Hei Long Jiang China The School of Computer and Software Nanjing University of Information Science and Technology Jiangsu China The School of Microelectronics Tianjin University Tianjin China The Okanagan College KelownaBC Canada The Dept. of Comp. Sci. Math Physics & Statistics The University of British Columbia OkanaganBC Canada

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods. © 2022, CC BY.

关键词： Convolutional neural networks

Contextual Decision-Making with Knapsacks Beyond the Worst Case

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arXiv 2022年

作者： Chen, Zhaohua Yang, Mingwei Wang, Chang Ai, Rui Pan, Yuqi Deng, Xiaotie School of Computer Science Peking University Haidian Beijing China Dept. of Management Science and Engineering Stanford University StanfordCA94305 United States Dept. of Computer Science Northwestern University EvanstonIL60208 United States IDSS & LIDS Massachusetts Institute of Technology CambridgeMA02139 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States School of Computer Science Institute for Artificial Intelligence Peking University Haidian Beijing China

We study the framework of a dynamic decision-making scenario with resource constraints. In this framework, an agent, whose target is to maximize the total reward under the initial inventory, selects an action in each round upon observing a random request, leading to a reward and resource consumptions that are further associated with an unknown random external factor. While previous research has already established an Õ√T) worst-case regret for this problem, this work offers two results that go beyond the worst-case perspective: one for the worst-case gap between benchmarks and another for logarithmic regret rates. We first show that an Ω(√T) distance between the commonly used fluid benchmark and the online optimum is unavoidable when the former has a degenerate optimal solution. On the algorithmic side, we merge the re-solving heuristic with distribution estimation skills and propose an algorithm that achieves an Õ(1) regret as long as the fluid LP has a unique and non-degenerate solution. Furthermore, we prove that our algorithm maintains a near-optimal Õ(√T) regret even in the worst cases and extend these results to the setting where the request and external factor are continuous. Regarding information structure, our regret results are obtained under two feedback models, respectively, where the algorithm accesses the external factor at the end of each round and at the end of a round only when a non-null action is executed. Copyright © 2022, The Authors. All rights reserved.

关键词： Heuristic algorithms

Managing the Fall Risk in Smart Homes Using Monocular Cameras, Ambient Sensors and an Interactive Social Robot

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Managing the Fall Risk in Smart Homes Using Monocular Camera...

Blockchain, Smart Healthcare and Emerging Technologies (SmartBlock4Health), IEEE International Conference on

作者： Killian Lachaux Julien Maître Kévin Bouchard Maxime Lussier Carolina Bottari Mélanie Couture Nathalie Bier Sylvain Giroux Sébastien Gaboury Dept. of Math. and Computer Science Université du Québec à Chicoutimi Chicoutimi Canada Dept. of Medicine Université de Montréal Montréal Canada School of Rehabilitation Université de Montréal Montréal Canada CIUSSS West-Central Montreal Center for Research and Expertise in Social Gerontology Côte Saint-Luc Canada Dept. of Computer Science Université de Sherbrooke Sherbrooke Canada

ISBN: (纸本)9781665460156

One of the biggest challenges faced by older adults is the risk of falling. Falls and the fear of falling cause serious physical and mental health issues. They are also a very impacting factor on the independence of the individual. This paper approaches the problem through the view of a population afflicted by the self-neglect syndrome. Therefore, we propose the NAOREA system, using monocular cameras, various ambient sensors and a humanoid social robot, to manage the risk of falling through a step-by-step process. This system achieved up to 100% detection rates of obstacles for proactive fall control in a real-home setting, as well as a 87% detection rates of falls after they happened.

关键词： Legged locomotion Navigation Social robots Robot vision systems Smart homes Cameras Fall detection

Across scales & across dimensions: Temporal super-resolution using deep internal learning

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arXiv 2020年

作者： Zuckerman, Liad Pollak Bagon, Shai Naor, Eyal Pisha, George Irani, Michal Dept. of Computer Science and Applied Math Weizmann Institute of Science Technion Israel Institute of Technology

When a very fast dynamic event is recorded with a low-framerate camera, the resulting video suffers from severe motion blur (due to exposure time) and motion aliasing (due to low sampling rate in time). True Temporal Super-Resolution (TSR) is more than just Temporal-Interpolation (increasing framerate). It can also recover new high temporal frequencies beyond the temporal Nyquist limit of the input video, thus resolving both motion-blur and motion-aliasing – effects that temporal frame interpolation (as sophisticated as it may be) cannot undo. In this paper we propose a "Deep Internal Learning" approach for true TSR. We train a video-specific CNN on examples extracted directly from the low-framerate input video. Our method exploits the strong recurrence of small space-time patches inside a single video sequence, both within and across different spatio-temporal scales of the video. We further observe (for the first time) that small space-time patches recur also across-dimensions of the video sequence – i.e., by swapping the spatial and temporal dimensions. In particular, the higher spatial resolution of video frames provides strong examples as to how to increase the temporal resolution of that video. Such internal video-specific examples give rise to strong self-supervision, requiring no data but the input video itself. This results in Zero-Shot Temporal-SR of complex videos, which removes both motion blur and motion aliasing, outperforming previous supervised methods trained on external video datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning