User feedback is crucial for the evolution of mobile apps. However, research suggests that users tend to submit uninformative, vague, or destructive feedback. Unlike recent AI4SE approaches that focus on generating code and other development artifacts, our work aims at empowering users to submit better and more constructive UI feedback with concrete suggestions on how to improve the app. We propose LikeThis!, a GenAI-based approach that takes a user comment with the corresponding screenshot to immediately generate multiple improvement alternatives, from which the user can easily choose their preferred option. To evaluate LikeThis!, we first conducted a model benchmarking study based on a public dataset of carefully critiqued UI designs. The results show that GPT-Image-1 significantly outperformed three other state-of-the-art image generation models in improving the designs to address UI issues while keeping the fidelity and without introducing new issues. An intermediate step in LikeThis! is to generate a solution specification before sketching the design as a key to achieving effective improvement. Second, we conducted a user study with 10 production apps, where 15 users used Like
User feedback is essential for the success of mobile apps, yet what users report and what developers need often diverge. Research shows that users often submit vague feedback and omit essential contextual details. This leads to incomplete reports and time-consuming clarification discussions. To overcome this challenge, we propose FeedAIde, a context-aware, interactive feedback approach that supports users during the reporting process by leveraging the reasoning capabilities of Multimodal Large Language Models. FeedAIde captures contextual information, such as the screenshot where the issue emerges, and uses it for adaptive follow-up questions to collaboratively refine with the user a rich feedback report that contains information relevant to developers. We implemented an iOS framework of FeedAIde and evaluated it on a gym's app with its users. Compared to the app's simple feedback form, participants rated FeedAIde as easier and more helpful for reporting feedback. An assessment by two industry experts of the resulting 54 reports showed that FeedAIde improved the quality of both bug reports and feature requests, particularly in terms of completeness. The findings of our study demons
The recently completed SubMIT platform is a small set of servers that provide interactive access to substantial data samples at high speeds, enabling sophisticated data analyses with very fast turnaround times. Additionally, it seamlessly integrates massive processing resources for large-scale tasks by connecting to a set of powerful batch processing systems. It serves as an ideal prototype for an Analysis Facility tailored to meet the demanding data and computational requirements anticipated during the High-Luminosity phase of the Large Hadron Collider. The key features that make this facility so powerful include highly optimized data access with a minimum of 100Gbps networking per server, a large managed NVMe storage system, and a substantial spinning-disk Ceph file system. The platform integrates a diverse set of high multicore CPU machines for tasks benefiting from the multithreading and GPU resources for example for neural network training. SubMIT also provides and supports a flexible environment for users to manage their own software needs for example by using containers. This article describes the facility, its users, and a few complementary, generic and real-life analyses t
With the dawn of AI factories ushering a new era of computing supremacy, development of strategies to effectively track and utilize the available computing resources is garnering utmost importance. These computing resources are often modeled as Markov sources, which oscillate between free and busy states, depending on their internal load and external utilization, and are commonly referred to as Markov machines (MMs). Most of the prior work solely focuses on the problem of tracking these MMs, while often assuming a rudimentary decision process that governs their utilization. Our key observation is that the ultimate goal of tracking a MM is to properly utilize it. In this work, we consider the problem of maximizing the utility of a MM, where the utility is defined as the average revenue generated by the MM. Assuming a Poisson job arrival process and a query-based sampling procedure to sample the state of the MM, we find the optimal times to submit the available jobs to the MM so as to maximize the average revenue generated per unit job. We show that, depending on the parameters of the MM, the optimal policy is in the form of either a \emph{threshold policy} or a \emph{switching polic
What is the optimal order in which a researcher should submit their papers to journals of differing quality? I analyze a sequential search model without recall where the researcher's expected value from journal submission depends on the history of past submissions. Acceptances immediately terminate the search process and deliver some payoff, while rejections carry information about the paper's quality, affecting the researcher's belief in acceptance probability over future journals. When journal feedback does not change the paper's quality, the researcher's optimal strategy is monotone in their acceptance payoff. Submission costs distort the researcher's effective acceptance payoff, but maintain monotone optimality. If journals give feedback which can affect the paper's quality, such as through \textit{referee reports}, the search order can change drastically depending on the agent's prior belief about their paper's quality. However, I identify a set of \textit{assortative matched} conditions on feedback such that monotone strategies remain optimal whenever the agent's prior is sufficiently optimistic.
Deep generative models have achieved promising results in image generation, and various generative model hubs, e.g., Hugging Face and Civitai, have been developed that enable model developers to upload models and users to download models. However, these model hubs lack advanced model management and identification mechanisms, resulting in users only searching for models through text matching, download sorting, etc., making it difficult to efficiently find the model that best meets user requirements. In this paper, we propose a novel setting called Generative Model Identification (GMI), which aims to enable the user to identify the most appropriate generative model(s) for the user's requirements from a large number of candidate models efficiently. To our best knowledge, it has not been studied yet. In this paper, we introduce a comprehensive solution consisting of three pivotal modules: a weighted Reduced Kernel Mean Embedding (RKME) framework for capturing the generated image distribution and the relationship between images and prompts, a pre-trained vision-language model aimed at addressing dimensionality challenges, and an image interrogator designed to tackle cross-modality issue
This paper presents a novel method for finding features in the analysis of variable distributions stemming from time series. We apply the methodology to the case of submitted and accepted papers in peer-reviewed journals. We provide a comparative study of editorial decisions for papers submitted to two peer-reviewed journals: the Journal of the Serbian Chemical Society (JSCS) and this MDPI Entropy journal. We cover three recent years for which the fate of submitted papers, about 600 papers to JSCS and 2500 to Entropy, is completely determined. Instead of comparing the number distributions of these papers as a function of time with respect to a uniform distribution, we analyze the relevant probabilities, from which we derive the information entropy. It is argued that such probabilities are indeed more relevant for authors than the actual number of submissions. We tie this entropy analysis to the so called diversity of the variable distributions. Furthermore, we emphasize the correspondence between the entropy and the diversity with inequality measures, like the Herfindahl-Hirschman index and the Theil index, itself being in the class of entropy measures; the Gini coefficient which a
A Charge Density Wave (CDW) submitted to an electric field displays a strong shear deformation because of pinning at the lateral surfaces of the sample. This CDW transverse pinning was recently observed but has received little attention from a theoretical point of view until now despite important consequences on electrical conductivity properties. Here, we provide a description of this phenomenon by considering a CDW submitted to an external dc electric field and constrained by boundary conditions including both longitudinal pinning due to electrical contacts and transverse surface pinning. A simple formula for the CDW phase is obtained in 3D by using the Green function and image charges method. In addition, an analytical expression of the threshold field dependence on both length and sample cross section is obtained by considering the phase slip process. We show that the experimental data are well reproduced with this model and that bulk pinning can be neglected. This study shows that the dynamical properties of CDW systems could be mainly driven by boundary effects, despite the comparatively huge sample volumes.
The Astrophysics Source Code Library (ASCL) contains 3000 metadata records about astrophysics research software and serves primarily as a registry of software, though it also can and does accept code deposit. Though the ASCL was started in 1999, many astronomers, especially those new to the field, are not very familiar with it. This hands-on virtual tutorial was geared to new users of the resource to teach them how to use the ASCL, with a focus on finding software and information about software not only in this resource, but also by using Google and NASA's Astrophysics Data System (ADS). With computational methods so important to research, finding these methods is useful for examining (for transparency) and possibly reusing the software (for reproducibility or to enable new research). Metadata about software is useful for, for example, knowing how to cite software when it is used for research and studying trends in the computational landscape. Though the tutorial was primarily aimed at new users, advanced users were also likely to learn something new.
Many species of ants forage by building up two files: an outbound one moving from the nest to the foraging area, and a nestbound one, returning from it to the nest. Those files are eventually submitted to different threats. If the danger is concentrated at one point of the file, one might expect that ants returning to the nest will pass danger information to their nestmates moving in the opposite direction towards the danger area. In this paper, we construct simple cellular automata models for foraging ants submitted to localized abduction, were danger information is transmitted using different protocols, including the possibility of no transmission. The parameters we have used in the simulations have been estimated from actual experiments under natural conditions. So, it would be easy to test our information-transmission hypothese in real experiments. Preliminary experimental results published elsewhere suggest that the behavior of foraging ants of the species Atta insularis is best described using the hypothesis of no transmission of danger information.
The effect of a standing acoustic wave on the diffusion of an ad-atom on a crystalline surface is theoretically studied. We used an unidimensional space model to study the ad-atom+substrate system. The dynamic equation of the ad-atom, a Generalized Langevin equation, is analytically derived from the full Hamiltonian of the ad-atom+substrate system submitted to the acoustic wave. A detailed analysis of each term of this equation, as well as of their properties, is presented. Special attention is devoted to the expression of the effective force induced by the wave on the ad-atom. It has essentially the same spatial and time dependences as its parent standing acoustic wave.
An analytical result for the renormalization of the jump of the heat capacity Delta C/C_N by the anisotropy of the order parameter is derived within the framework of the very recent model proposed by Posazhennikova, Dahm and Maki [cond-mat/0204272, submitted to Europhys. Lett.], for both oblate and prolate anisotropy. The graph of Delta C/C_N versus the ratio of the gaps on the equator and the pole of the Fermi surface, Delta_e/Δ_p, allows a direct determination of the gap anisotropy parameter Delta_e/Δ_p by fitting data from specific heat measurements Delta C/C_N. Using the experimental value Delta C/C_N=0.82 +/- 10% by Wang, Plackowski, and Junod [Physica C 355 (2001) 179] we find Delta_e/Δ_p = 4.0.
We establish the abstract and semi-abstract equations that translate the conservation law for a totally-filled, or totally-empty basin of arbitrary shape submitted to a SH plane seismic body wave. This law states that the input flux is equal to the sum of the scattered and absorbed fluxes, the latter being equal to zero when the filler medium is non-lossy. We show that the well-known, exact, solutions for the case of a semi-circular basin indeed satisfy this law. The latter can, and should, be employed to test the validity of any theoretical or numerical solution one might propose for a basin of arbitrary shape.
Elasticity theory calculations predict the number N of depressions that appear at the surface of a spherical thin shell submitted to an external isotropic pressure. In a model that mainly considers curvature deformations, we show that N only depends on the relative volume variation. Equilibrium configurations show single depression (N=1) for small volume variations, then N increases up to 6, before decreasing more abruptly due to steric constraints, down to N=1 again for maximal volume variations. These predictions are consistent with previously published experimental observations.
We report experimental observations of traveling waves in a pure fluid with a free surface situated in a long container submitted to a horizontal temperature gradient perpendicular to its large extension. Above a critical value of the gradient and depending on the height of liquid $ h, $ a source of waves is created in the container for small value of $ h, $ while the system exhibits stationary patterns for larger values of $ h. $ The spatio-temporal properties of the waves are studied and compared to theoretical predictions. Email contact: daviaud@amoco.saclay.cea.fr
As quantum computing platforms increasingly adopt cloud-based execution, users submit quantum circuits to remote compilers and backends, trusting that what they submit is exactly what will be run. This shift introduces new trust assumptions in the submission pipeline, which remain largely unexamined. In this paper, we present QSpy, the first proof-of-concept Quantum Remote Access Trojan capable of intercepting quantum circuits in transit. Once deployed on a user's machine, QSpy silently installs a rogue certificate authority and proxies outgoing API traffic, enabling a man-in-the-middle (MITM) attack on submitted quantum circuits. We show that the intercepted quantum circuits may be forwarded to a remote server, which is capable of categorizing, storing, and analyzing them, without disrupting execution or triggering authentication failures. Our prototype targets IBM Qiskit APIs on a Windows system, but the attack model generalizes to other delegated quantum computing workflows. This work highlights the urgent need for submission-layer protections and demonstrates how even classical attack primitives can pose critical threats to quantum workloads.
To address the 'novelty-vicious cycle' and the 'replicability crisis' of the field (both discussed in the survey) we propose abolishing the "ICSE paper" as we know it and replacing it with a two-tier system that also evolves the existing notion of 'Registered Report'. Authors proposing a new idea, experiment, or analysis would submit a "Registered Proposal" of their idea and the proposed experimental methodology to undergo peer review. The following year, anyone can submit (shorter) "Results Reports" on the realization of the empirical work based on the registered proposals of the previous ICSE (or FSE or ISSTA or ASE etc.). Both works should be first class citizens of the mainstream events. We argue that such a disruptive (heretical?) idea is supported and based on the responses of the community of the Future of Software Engineering pre-survey
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states. The organizers provide development and evaluation datasets and evaluation scripts, as well as baseline anonymization systems and a list of training resources formed on the basis of the participants' requests. Participants apply their developed anonymization systems, run evaluation scripts and submit evaluation results and anonymized speech data to the organizers. Results will be presented at a workshop held in conjunction with Interspeech 2024 to which all participants are invited to present their challenge systems and to submit additional workshop papers.
The miniaturization of thermal sensors for mobile platforms inherently limits their spatial resolution and textural fidelity, leading to blurry and less informative images. Existing thermal super-resolution (SR) methods can be grouped into single-image and RGB-guided approaches: the former struggles to recover fine structures from limited information, while the latter relies on accurate and laborious cross-camera calibration, which hinders practical deployment and robustness. Here, we propose 3M-TI, a calibration-free Multi-camera cross-Modality diffusion framework for Mobile Thermal Imaging. At its core, 3M-TI integrates a cross-modal self-attention module (CSM) into the diffusion UNet, replacing the original self-attention layers to adaptively align thermal and RGB features throughout the denoising process, without requiring explicit camera calibration. This design enables the diffusion network to leverage its generative prior to enhance spatial resolution, structural fidelity, and texture detail in the super-resolved thermal images. Extensive evaluations on real-world mobile thermal cameras and public benchmarks validate our superior performance, achieving state-of-the-art resul
The First VoicePrivacy Attacker Challenge is a new kind of challenge organized as part of the VoicePrivacy initiative and supported by ICASSP 2025 as the SP Grand Challenge It focuses on developing attacker systems against voice anonymization, which will be evaluated against a set of anonymization systems submitted to the VoicePrivacy 2024 Challenge. Training, development, and evaluation datasets are provided along with a baseline attacker system. Participants shall develop their attacker systems in the form of automatic speaker verification systems and submit their scores on the development and evaluation data to the organizers. To do so, they can use any additional training data and models, provided that they are openly available and declared before the specified deadline. The metric for evaluation is equal error rate (EER). Results will be presented at the ICASSP 2025 special session to which 5 selected top-ranked participants will be invited to submit and present their challenge systems.