Tootfinder

Universal Deep Research: Bring Your Own Model and Strategy
Peter Belcak, Pavlo Molchanov
https://arxiv.org/abs/2509.00244 https://arxiv.org/pdf/2509.00244

Universal Deep Research: Bring Your Own Model and Strategy
Deep research tools are among the most impactful and most commonly encountered agentic systems today. We observe, however, that each deep research agent introduced so far is hard-coded to carry out a particular research strategy using a fixed choice of tools. We introduce Universal Deep Research (UDR), a generalist agentic system that wraps around any language model and enables the user to create, edit, and refine their own entirely custom deep research strategies without any need for additiona…

Day 8 (a bit late): Timnit Gebru
Academic authors are authors too, and there are a bunch of people I deeply respect both in my fields and adjacent.
Gebru is someone I have huge respect for because she stood up for her (mild, completely reasonable) principles to the point of losing her job on Google's AI ethics team (since disbanded entirely), and then went ahead and founded an independent research institute to continue doing AI ethics research.
Why was she fired? Because she insisted on publishing her "Stochastic Parrots" paper after it passed Google internal review only to have extra nonstandard scrutiny applied at the last minute. Why did Google want to suppress her paper (which included an academic co-author)? Because it expressed valid criticisms of the large language models fad, and Google was planning to make money off that fad. Personally, I don't think I'd hire an "AI ethics" team only to then try to suppress their publications, and Google seems to now agree, having scrapped the team (during the initial furor, Timnit's boss also effectively quit to support her).
That "Stochastic Parrots" paper? Indeed, it predicts the core underlying problems with large language models that lead to so many of their user-side harms today. You can read it here: #20AuthorsNoMen

🌍🛰️ How NRENs & RRENs are powering Earth Observation (EO) research
In May, GÉANT and the Group on Earth Observations (GEO) hosted the second infoshare on the critical role that NRENs & RRENs play in enabling EO research.
This session focused on the GEO Infrastructure strategy, highlighted inspiring user cases from partners such as EUMETSAT, and showcased the role of NRENs in supporting EO research, including stories from CSC and ASNET-AM.
👉 Read more:

MARS: Modality-Aligned Retrieval for Sequence Augmented CTR Prediction
Yutian Xiao, Shukuan Wang, Binhao Wang, Zhao Zhang, Yanze Zhang, Shanqi Liu, Chao Feng, Xiang Li, Fuzhen Zhuang
https://arxiv.org/abs/2509.01184

MARS: Modality-Aligned Retrieval for Sequence Augmented CTR Prediction
Click-through rate (CTR) prediction serves as a cornerstone of recommender systems. Despite the strong performance of current CTR models based on user behavior modeling, they are still severely limited by interaction sparsity, especially in low-active user scenarios. To address this issue, data augmentation of user behavior is a promising research direction. However, existing data augmentation methods heavily rely on collaborative signals while overlooking the rich multimodal features of items,…

Contrastive Diffusion Guidance for Spatial Inverse Problems
Sattwik Basu, Chaitanya Amballa, Zhongweiyang Xu, Jorge Van\v{c}o Sampedro, Srihari Nelakuditi, Romit Roy Choudhury
https://arxiv.org/abs/2509.26489

Contrastive Diffusion Guidance for Spatial Inverse Problems
We consider the inverse problem of reconstructing the spatial layout of a place, a home floorplan for example, from a user`s movements inside that layout. Direct inversion is ill-posed since many floorplans can explain the same movement trajectories. We adopt a diffusion-based posterior sampler to generate layouts consistent with the measurements. While active research is in progress on generative inverse solvers, we find that the forward operator in our problem poses new challenges. The path-p…

Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study
Hasan Mahmuda, Najmul Islam, Satish Krishnan
https://arxiv.org/abs/2510.02153

Human-Robo-advisor collaboration in decision-making: Evidence from a multiphase mixed methods experimental study
Robo-advisors (RAs) are cost-effective, bias-resistant alternatives to human financial advisors, yet adoption remains limited. While prior research has examined user interactions with RAs, less is known about how individuals interpret RA roles and integrate their advice into decision-making. To address this gap, this study employs a multiphase mixed methods design integrating a behavioral experiment (N = 334), thematic analysis, and follow-up quantitative testing. Findings suggest that people t…

Folgende Features sind enthalten:
* alles an einem Ort
* zum ersten Mal
* für multimodale Daten
* filterbasiert und
* über eine grafische User Interface
* einschließlich einer speziellen Incipit-Suche für notierte Musik 🎼 🎹
* einschließlich einer KI-basierten Bildähnlichkeitssuche 🖼️
📬 Da es sich um eine erste Version handelt, freuen wir uns über er Feedback unter contact@nfdi4culture.de oder beantworten gerne alle weiteren Fragen unter

EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis
Mohammad Hossein Samaei, Faryad Darabi Sahneh, Lee W. Cohnstaedt, Caterina Scoglio
https://arxiv.org/abs/2510.00024

EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis
Large Language Models (LLMs) offer new opportunities to automate complex interdisciplinary research domains. Epidemic modeling, characterized by its complexity and reliance on network science, dynamical systems, epidemiology, and stochastic simulations, represents a prime candidate for leveraging LLM-driven automation. We introduce \textbf{EpidemIQs}, a novel multi-agent LLM framework that integrates user inputs and autonomously conducts literature review, analytical derivation, network modelin…

Multi-Objective Task-Aware Predictor for Image-Text Alignment
Eunki Kim, Na Min An, James Thorne, Hyunjung Shim
https://arxiv.org/abs/2510.00766 https://ar…

Multi-Objective Task-Aware Predictor for Image-Text Alignment
Evaluating image-text alignment while reflecting human preferences across multiple aspects is a significant issue for the development of reliable vision-language applications. It becomes especially crucial in real-world scenarios where multiple valid descriptions exist depending on contexts or user needs. However, research progress is hindered by the lack of comprehensive benchmarks and existing evaluation predictors lacking at least one of these key properties: (1) Alignment with human judgmen…

Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns
Hanqi Xiao, Vaidehi Patil, Hyunji Lee, Elias Stengel-Eskin, Mohit Bansal
https://arxiv.org/abs/2509.24988

Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns
Generating accurate and calibrated confidence estimates is critical for deploying LLMs in high-stakes or user-facing applications, and remains an open challenge. Prior research has often framed confidence as a problem of eliciting a model's "self-knowledge", i.e., the ability of an LLM to judge whether its own answers are correct; this approach implicitly assumes that there is some privileged information about the answer's correctness that is accessible to the model itself. However, our experim…

Anthropic releases Claude for Chrome, which lets Claude take actions on the user's behalf within the browser, as a research preview for 1,000 Max subscribers (Maxwell Zeff/TechCrunch)
https://techcrunch.com/2025/08/26/anthropic-launches-a-clau…

Anthropic launches a Claude AI agent that lives in Chrome | TechCrunch
Anthropic is the latest AI lab to offer an AI agent with the ability to view and take action in a user's Chrome browser.

Bridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations
Bo Ma, LuYao Liu, Simon Lau, Chandler Yuan, and XueY Cui, Rosie Zhang
https://arxiv.org/abs/2510.01606

Bridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations
Recent research has explored using Large Language Models for recommendation tasks by transforming user interaction histories and item metadata into text prompts, then having the LLM produce rankings or recommendations. A promising approach involves connecting collaborative filtering knowledge to LLM representations through compact adapter networks, which avoids expensive fine-tuning while preserving the strengths of both components. Yet several challenges persist in practice: collaborative filt…

just discovered this glorious mix of thumping techno and local government service design user research
https://charlieoctogirl.bandcamp.com/track/whens-bins-happy-birthday-gilly

academia_edu: Academica.edu (2011)
Snapshot of the follower relationships among users of academia.edu, a platform for academics to share research papers, scraped in 2011. Nodes are users and a directed edge (i,j) denotes that user i follows j.
This network has 200169 nodes and 1398063 edges.
Tags: Social, Online, Unweighted
https://

Atlas of Human-AI Interaction (v1): An Interactive Meta-Science Platform for Large-Scale Research Literature Sensemaking
Chayapatr Archiwaranguprok, Awu Chen, Sheer Karny, Hiroshi Ishii, Pattie Maes, Pat Pataranutaporn
https://arxiv.org/abs/2509.25499

Atlas of Human-AI Interaction (v1): An Interactive Meta-Science Platform for Large-Scale Research Literature Sensemaking
Human-AI interaction researchers face an overwhelming challenge: synthesizing insights from thousands of empirical studies to understand how AI impacts people and inform effective design. Existing approach for literature reviews cluster papers by similarities, keywords or citations, missing the crucial cause-and-effect relationships that reveal how design decisions impact user outcomes. We introduce the Atlas of Human-AI Interaction, an interactive web interface that provides the first systemat…

The NIAID Discovery Portal: A Unified Search Engine for Infectious and Immune-Mediated Disease Datasets
Ginger Tsueng (The Scripps Research Institute, La Jolla, CA, USA), Emily Bullen (The Scripps Research Institute, La Jolla, CA, USA), Candice Czech (The Scripps Research Institute, La Jolla, CA, USA), Dylan Welzel (The Scripps Research Institute, La Jolla, CA, USA), Leandro Collares (The Scripps Research Institute, La Jolla, CA, USA), Jason Lin (The Scripps Research Institute, La Joll…

The NIAID Discovery Portal: A Unified Search Engine for Infectious and Immune-Mediated Disease Datasets
The NIAID Data Ecosystem Discovery Portal (https://data.niaid.nih.gov) provides a unified search interface for over 4 million datasets relevant to infectious and immune-mediated disease (IID) research. Integrating metadata from domain-specific and generalist repositories, the Portal enables researchers to identify and access datasets using user-friendly filters or advanced queries, without requiring technical expertise. The Portal supports discovery of a wide range of resources, including epide…

Anthropic enables Claude Opus 4 and 4.1 to end conversations in "cases of persistently harmful or abusive user interactions"; users can still start new chats (Anthropic)
https://www.anthropic.com/research/end-subset-conversations

The ROI of #UX is the value of your entire company, because user research is the work that determines what problem is most valuable to solve in the first place.
Trouble is, people won't believe you if you tell them this.
So the second job on top of that job is to convince people to trust us.

The ROI of UX is so massive that no one will believe you
User research makes the most difference when findings challenge closely-held assumptions. But the truth is often unpopular.

Designing AI-Infused Interactive Systems for Online Communities: A Systematic Literature Review
Yuanhao Zhang, Xiaoyu Wang, Jiaxiong Hu, Ziqi Pan, Zhenhui Peng, Xiaojuan Ma
https://arxiv.org/abs/2509.23309

Designing AI-Infused Interactive Systems for Online Communities: A Systematic Literature Review
AI-infused systems have demonstrated remarkable capabilities in addressing diverse human needs within online communities. Their widespread adoption has shaped user experiences and community dynamics at scale. However, designing such systems requires a clear understanding of user needs, careful design decisions, and robust evaluation. While research on AI-infused systems for online communities has flourished in recent years, a comprehensive synthesis of this space remains absent. In this work, w…

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
Lingyu Zhang, Guobin Wu, Yan Wang, Pengfei Xu, Jian Liang, Xuan Song, Yunhai Wang
https://arxiv.org/abs/2509.22661

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
The next Point-of-interest (POI) recommendation is mainly based on sequential traffic information to predict the user's next boarding point location. This is a highly regarded and widely applied research task in the field of intelligent transportation, and there have been many research results to date. Traditional POI prediction models primarily rely on short-term traffic sequence information, often neglecting both long-term and short-term preference data, as well as crucial spatiotemporal cont…

Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Kalyani Khona
https://arxiv.org/abs/2509.13337 https://arxi…

Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Understanding how users authentically interact with Large Language Models (LLMs) remains a significant challenge in human-computer interaction research. Most existing studies rely on self-reported usage patterns or controlled experimental conditions, potentially missing genuine behavioral adaptations. This study presents a behavioral analysis of the use of English-speaking urban professional ChatGPT in India based on 238 authentic, unedited user prompts from 40 participants in 15+ Indian cities…

Enjoyed this interview with UX researcher Ben Taels. He talks a lot about what games ux research is as well as what it's not. Some high level goals and some specific examples.
https://aftermath.site/video-games-user-experience-ux-r…

'Dopamine Simply Doesn't Work Like That': An Interview With A User Experience Researcher - Aftermath
Part of the reason I have worked in games UXR and not, say, microwave UX, is that games can be designed to be hard

Le Conseil fédéral a induit Š plusieurs reprises la population en erreur avec de fausses indications sur le choix des F-35 dont le prix explose. https://www.letemps.ch/suisse/comment-une-presentation-biaisee-des-couts-a-pousse-au-choix-du-f-35?shouldPreventPurchase=true&inAppView=3.18.1&didomiConfig.user.externalConsent.value={"purposes":{"consent":{"enabled":["audiencem-hJxaeGrR","cookies","create_ads_profile","create_content_profile","device_characteristics","geolocation_data","improve_products","market_research","measure_ad_performance","measure_content_performance","parselyne-Ya2EC4dG","select_basic_ads","select_personalized_ads","select_personalized_content","use_limited_data_to_select_content"],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"vendors":{"consent":{"enabled":["amazon","c:aws-cloudfront","c:bannerwise-EeHwBjLT","c:chartbeat","c:cloudflare","c:echobox-GqD96L9K","c:forecast-ZRJPAUT7","c:googleana-4TXnJigR","c:hotjar","c:jsdelivr-xdD77X6w","c:jw-player","c:keycdn","c:linkedin","c:magnitein-ePEpbCQ7","c:meta-43HDmFRa","c:microsoft","c:nonli-jnWf8kqD","c:pianohybr-R3VKC2r4","c:pinterest","c:spyri-XAtBPx3i","c:tapadinc-VDTUUcKw","c:tiktok-KZAUQLZ9","c:unrulygro-nKyLqdKi","c:youtube","facebook","google","twitter",1,10,1020,1021,1028,1046,1069,108,109,11,1126,12,120,128,13,130,131,1310,132,136,137,138,142,143,154,156,16,164,202,209,21,211,23,238,24,241,243,25,253,264,28,284,290,30,301,32,36,39,4,412,422,45,469,475,493,50,519,52,559,58,61,612,617,68,684,69,73,76,77,783,790,793,80,81,811,84,85,855,898,91,92,95,965,98],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"user_id":"36B03261-8BC8-4FB4-A026-0543FF748710","created":"2025-05-28T08:36:56.058Z","updated":"2025-08-30T09:35:31.055Z","source":{"type":"app","domain":"ch.letemps.LT"},"action":"webview"}

from my link log —
Flat design vs rich (“skeuomorphic”) design: a bibliography.
https://www.flatisbad.com/
saved 2025-07-30 https://dot…

A Holistic Approach to E-Commerce Innovation: Redefining Security and User Experience
Mohammad Olid Ali Akash (North South University), Priyangana Saha (North South University)
https://arxiv.org/abs/2509.11712

A Holistic Approach to E-Commerce Innovation: Redefining Security and User Experience
In the modern, fast-moving world of e-commerce, many Android apps face challenges in providing a simple and secure shopping experience. Many of these apps, often enough, have complicated designs that prevent users from finding what they want quickly, thus frustrating them and wasting their precious time. Another major issue is that of security; with the limitation of payment options and weak authentication mechanisms, users' sensitive information can be compromised. This research presents a new…

Improving and Evaluating Open Deep Research Agents
Doaa Allabadi, Kyle Bradbury, Jordan M. Malof
https://arxiv.org/abs/2508.10152 https://arxiv.org/pdf/250…

Improving and Evaluating Open Deep Research Agents
We focus here on Deep Research Agents (DRAs), which are systems that can take a natural language prompt from a user, and then autonomously search for, and utilize, internet-based content to address the prompt. Recent DRAs have demonstrated impressive capabilities on public benchmarks however, recent research largely involves proprietary closed-source systems. At the time of this work, we only found one open-source DRA, termed Open Deep Research (ODR). In this work we adapt the challenging recen…

Jš vi IA mentindo e omitindo... Só o que me surpreende é usarmos isso para qualquer coisa, quer dizer, sermos usados por isso para qualquer coisa.
@… https://mas.to/@carnage4life/115228191

Feature Request Analysis and Processing: Tasks, Techniques, and Trends
Feifei Niu, Chuanyi Li, Haosheng Zuo, Jionghan Wu, Xin Xia
https://arxiv.org/abs/2508.12436 https://

Feature Request Analysis and Processing: Tasks, Techniques, and Trends
Feature requests are proposed by users to request new features or enhancements of existing features of software products, which represent users' wishes and demands. Satisfying users' demands can benefit the product from both competitiveness and user satisfaction. Feature requests have seen a rise in interest in the past few years and the amount of research has been growing. However, the diversity in the research topics suggests the need for their collective analysis to identify the challenges a…

OpenAI fixes zero-click ShadowLeak vulnerability affecting ChatGPT Deep Research agent https://therecord.media/openai-fixes-zero-click-shadowleak-vulnerability

From Canada to Japan: How 10,000 km Affect User Perception in Robot Teleoperation
Sim\'eon Capy, Thomas M. Kwok, Kevin Joseph, Yuichiro Kawasumi, Koichi Nagashima, Tomoya Sasaki, Yue Hu, Eiichi Yoshida
https://arxiv.org/abs/2508.05143

From Canada to Japan: How 10,000 km Affect User Perception in Robot Teleoperation
Robot teleoperation (RTo) has emerged as a viable alternative to local control, particularly when human intervention is still necessary. This research aims to study the distance effect on user perception in RTo, exploring the potential of teleoperated robots for older adult care. We propose an evaluation of non-expert users' perception of long-distance RTo, examining how their perception changes before and after interaction, as well as comparing it to that of locally operated robots. We have de…

Videogames convinced me that the power-cells on large devices would be user-removable, shaped like giant versions of device batteries, and be interchangeable between different types of device.
Move the power-cell from the winch to the security door controls of the abandoned research station.

academia_edu: Academica.edu (2011)
Snapshot of the follower relationships among users of academia.edu, a platform for academics to share research papers, scraped in 2011. Nodes are users and a directed edge (i,j) denotes that user i follows j.
This network has 200169 nodes and 1398063 edges.
Tags: Social, Online, Unweighted
https://

TOM: An Open-Source Tongue Segmentation Method with Multi-Teacher Distillation and Task-Specific Data Augmentation
Jiacheng Xie, Ziyang Zhang, Biplab Poudel, Congyu Guo, Yang Yu, Guanghui An, Xiaoting Tang, Lening Zhao, Chunhui Xu, Dong Xu
https://arxiv.org/abs/2508.14932

TOM: An Open-Source Tongue Segmentation Method with Multi-Teacher Distillation and Task-Specific Data Augmentation
Tongue imaging serves as a valuable diagnostic tool, particularly in Traditional Chinese Medicine (TCM). The quality of tongue surface segmentation significantly affects the accuracy of tongue image classification and subsequent diagnosis in intelligent tongue diagnosis systems. However, existing research on tongue image segmentation faces notable limitations, and there is a lack of robust and user-friendly segmentation tools. This paper proposes a tongue image segmentation model (TOM) based on…

Enhancing LLM-Based Social Bot via an Adversarial Learning Framework
Fanqi Kong, Xiaoyuan Zhang, Xinyu Chen, Yaodong Yang, Song-Chun Zhu, Xue Feng
https://arxiv.org/abs/2508.17711

Enhancing LLM-Based Social Bot via an Adversarial Learning Framework
Developing Large Language Model (LLM) agents that exhibit human-like behavior, encompassing not only individual heterogeneity rooted in unique user profiles but also adaptive response to socially connected neighbors, is a significant research challenge. Social media platforms, with their diverse user data and explicit social structures, provide an ideal testbed for such investigations. This paper introduces EvoBot, an \textbf{Evo}lving LLM-based social \textbf{Bot} that significantly enhances h…

Addressing Personalized Bias for Unbiased Learning to Rank
Zechun Niu, Lang Mei, Liu Yang, Ziyuan Zhao, Qiang Yan, Jiaxin Mao, Ji-Rong Wen
https://arxiv.org/abs/2508.20798 https…

Addressing Personalized Bias for Unbiased Learning to Rank
Unbiased learning to rank (ULTR), which aims to learn unbiased ranking models from biased user behavior logs, plays an important role in Web search. Previous research on ULTR has studied a variety of biases in users' clicks, such as position bias, presentation bias, and outlier bias. However, existing work often assumes that the behavior logs are collected from an ``average'' user, neglecting the differences between different users in their search and browsing behaviors. In this paper, we intro…

Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy
Lihua Du, Xing Lyu, Lezi Xie, Bo Feng
https://arxiv.org/abs/2509.21665

Alignment Without Understanding: A Message- and Conversation-Centered Approach to Understanding AI Sycophancy
AI sycophancy is increasingly recognized as a harmful alignment, but research remains fragmented and underdeveloped at the conceptual level. This article redefines AI sycophancy as the tendency of large language models (LLMs) and other interactive AI systems to excessively and/or uncritically validate, amplify, or align with a user's assertions-whether these concern factual information, cognitive evaluations, or affective states. Within this framework, we distinguish three types of sycophancy: …

Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild"
Lingyao Li, Renkai Ma, Zhaoqian Xue, Junjie Xiong
https://arxiv.org/abs/2509.08912

Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild"
While Large Language Models (LLMs) are rapidly integrating into daily life, research on their risks often remains lab-based and disconnected from the problems users encounter "in the wild." While recent HCI research has begun to explore these user-facing risks, it typically concentrates on a singular LLM chatbot like ChatGPT or an isolated risk like privacy. To gain a holistic understanding of multi-risk across LLM chatbots, we analyze online discussions on Reddit around seven major LLM chatbot…

Context Matters: Incorporating Target Awareness in Conversational Abusive Language Detection
Raneem Alharthi, Rajwa Alharthi, Aiqi Jiang, Arkaitz Zubiaga
https://arxiv.org/abs/2508.12828

Context Matters: Incorporating Target Awareness in Conversational Abusive Language Detection
Abusive language detection has become an increasingly important task as a means to tackle this type of harmful content in social media. There has been a substantial body of research developing models for determining if a social media post is abusive or not; however, this research has primarily focused on exploiting social media posts individually, overlooking additional context that can be derived from surrounding posts. In this study, we look at conversational exchanges, where a user replies t…

An Analytical and Experimental Study of Distributed Uplink Beamforming in the Presence of Carrier Frequency Offsets
Mehdi Zafari, Divyanshu Pandey, Rahman Doost-Mohammady
https://arxiv.org/abs/2508.08506

An Analytical and Experimental Study of Distributed Uplink Beamforming in the Presence of Carrier Frequency Offsets
Realizing distributed multi-user beamforming (D-MUBF) in time division duplex (TDD)-based multi-user MIMO (MU-MIMO) systems faces significant challenges. One of the most fundamental challenges is achieving accurate over-the-air (OTA) timing and frequency synchronization among distributed access points (APs), particularly due to residual frequency offsets caused by local oscillator (LO) drifts. Despite decades of research on synchronization for MU-MIMO, there are only a few experimental studies …

Two Web Toolkits for Multimodal Piano Performance Dataset Acquisition and Fingering Annotation
Junhyung Park, Yonghyun Kim, Joonhyung Bae, Kirak Kim, Taegyun Kwon, Alexander Lerch, Juhan Nam
https://arxiv.org/abs/2509.15222

Two Web Toolkits for Multimodal Piano Performance Dataset Acquisition and Fingering Annotation
Piano performance is a multimodal activity that intrinsically combines physical actions with the acoustic rendition. Despite growing research interest in analyzing the multimodal nature of piano performance, the laborious process of acquiring large-scale multimodal data remains a significant bottleneck, hindering further progress in this field. To overcome this barrier, we present an integrated web toolkit comprising two graphical user interfaces (GUIs): (i) PiaRec, which supports the synchroni…

Discovering Top-k Periodic and High-Utility Patterns
Qingfeng Zhou, Wensheng Gan, Guoting Chen
https://arxiv.org/abs/2509.15732 https://arxiv.org/pdf/2509.…

Discovering Top-k Periodic and High-Utility Patterns
With a user-specified minimum utility threshold (minutil), periodic high-utility pattern mining (PHUPM) aims to identify high-utility patterns that occur periodically in a transaction database. A pattern is deemed periodic if its period aligns with the periodicity constraint set by the user. However, users may not be interested in all periodic high-utility patterns (PHUPs). Moreover, setting minutil in advance is also a challenging issue. To address these issues, our research introduces an algo…

«Le Conseil fédéral s’est privé, dès le départ et sans en débattre, d’une possibilité de choisir finalement un avion de combat qui Š la fois remplirait les exigences de la défense aérienne et aurait permis d’obtenir une plus-value dans l’intérêt du pays», écrivent les rapporteurs.
https://www.letemps.ch/suisse/comment-une-presentation-biaisee-des-couts-a-pousse-au-choix-du-f-35?shouldPreventPurchase=true&inAppView=3.18.1&didomiConfig.user.externalConsent.value={"purposes":{"consent":{"enabled":["audiencem-hJxaeGrR","cookies","create_ads_profile","create_content_profile","device_characteristics","geolocation_data","improve_products","market_research","measure_ad_performance","measure_content_performance","parselyne-Ya2EC4dG","select_basic_ads","select_personalized_ads","select_personalized_content","use_limited_data_to_select_content"],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"vendors":{"consent":{"enabled":["amazon","c:aws-cloudfront","c:bannerwise-EeHwBjLT","c:chartbeat","c:cloudflare","c:echobox-GqD96L9K","c:forecast-ZRJPAUT7","c:googleana-4TXnJigR","c:hotjar","c:jsdelivr-xdD77X6w","c:jw-player","c:keycdn","c:linkedin","c:magnitein-ePEpbCQ7","c:meta-43HDmFRa","c:microsoft","c:nonli-jnWf8kqD","c:pianohybr-R3VKC2r4","c:pinterest","c:spyri-XAtBPx3i","c:tapadinc-VDTUUcKw","c:tiktok-KZAUQLZ9","c:unrulygro-nKyLqdKi","c:youtube","facebook","google","twitter",1,10,1020,1021,1028,1046,1069,108,109,11,1126,12,120,128,13,130,131,1310,132,136,137,138,142,143,154,156,16,164,202,209,21,211,23,238,24,241,243,25,253,264,28,284,290,30,301,32,36,39,4,412,422,45,469,475,493,50,519,52,559,58,61,612,617,68,684,69,73,76,77,783,790,793,80,81,811,84,85,855,898,91,92,95,965,98],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"user_id":"36B03261-8BC8-4FB4-A026-0543FF748710","created":"2025-05-28T08:36:56.058Z","updated":"2025-08-30T09:35:31.055Z","source":{"type":"app","domain":"ch.letemps.LT"},"action":"webview"}

AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn, Chetan Arora, Gunel Gulmammadova, Joey Chua
https://arxiv.org/abs/2509.16861

AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software
Guardrails are critical for the safe deployment of Large Language Models (LLMs)-powered software. Unlike traditional rule-based systems with limited, predefined input-output spaces that inherently constrain unsafe behavior, LLMs enable open-ended, intelligent interactions--opening the door to jailbreak attacks through user inputs. Guardrails serve as a protective layer, filtering unsafe prompts before they reach the LLM. However, prior research shows that jailbreak attacks can still succeed ove…

Feasibility of In-Ear Single-Channel ExG for Wearable Sleep~Monitoring in Real-World Settings
Philipp Lepold, Jonas Leichtle, Tobias R\"oddiger, Michael Beigl
https://arxiv.org/abs/2509.07896

Feasibility of In-Ear Single-Channel ExG for Wearable Sleep~Monitoring in Real-World Settings
Automatic sleep staging typically relies on gold-standard EEG setups, which are accurate but obtrusive and impractical for everyday use outside sleep laboratories. This limits applicability in real-world settings, such as home environments, where continuous, long-term monitoring is needed. Detecting sleep onset is particularly relevant, enabling consumer applications (e.g. automatically pausing media playback when the user falls asleep). Recent research has shown correlations between in-ear EEG…

Bootstrapping Conditional Retrieval for User-to-Item Recommendations
Hongtao Lin, Haoyu Chen, Jaewon Jang, Jiajing Xu
https://arxiv.org/abs/2508.16793 https://

Bootstrapping Conditional Retrieval for User-to-Item Recommendations
User-to-item retrieval has been an active research area in recommendation system, and two tower models are widely adopted due to model simplicity and serving efficiency. In this work, we focus on a variant called \textit{conditional retrieval}, where we expect retrieved items to be relevant to a condition (e.g. topic). We propose a method that uses the same training data as standard two tower models but incorporates item-side information as conditions in query. This allows us to bootstrap new c…

Optimal Boost Design for Auto-bidding Mechanism with Publisher Quality Constraints
Huanyu Yan, Yu Huo, Min Lu, Weitong Ou, Xingyan Shi, Ruihe Shi, Xiaoying Tang
https://arxiv.org/abs/2508.08772

Optimal Boost Design for Auto-bidding Mechanism with Publisher Quality Constraints
Online bidding is crucial in mobile ecosystems, enabling real-time ad allocation across billions of devices to optimize performance and user experience. Improving ad allocation efficiency is a long-standing research problem, as it directly enhances the economic outcomes for all participants in advertising platforms. This paper investigates the design of optimal boost factors in online bidding while incorporating quality value (the impact of displayed ads on publishers' long-term benefits). To a…

academia_edu: Academica.edu (2011)
Snapshot of the follower relationships among users of academia.edu, a platform for academics to share research papers, scraped in 2011. Nodes are users and a directed edge (i,j) denotes that user i follows j.
This network has 200169 nodes and 1398063 edges.
Tags: Social, Online, Unweighted
https://

Floor sensors are cheap and easy to use! A Nihon Buyo Case Study
Miho Imai
https://arxiv.org/abs/2508.19261 https://arxiv.org/pdf/2508.19261

Floor sensors are cheap and easy to use! A Nihon Buyo Case Study
As floor-sensing technologies gain traction in movement research, questions remain about their usability and effectiveness for non-expert users. This study presents a case study evaluating Flexel, a modular, low-cost, high-resolution pressure-sensing floor interface, in the context of Nihon Buyo, a traditional Japanese dance. The system was installed, calibrated, and used by a first-time, non-technical user to track weight distribution patterns of a teacher and learner over nine weeks. Live pre…

"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants
Yunbo Lyu, Zhou Yang, Jieke Shi, Jianming Chang, Yue Liu, David Lo
https://arxiv.org/abs/2508.12285

"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants
This paper aims to explore fundamental questions in the era when AI coding assistants like GitHub Copilot are widely adopted: what do developers truly value and criticize in AI coding assistants, and what does this reveal about their needs and expectations in real-world software development? Unlike previous studies that conduct observational research in controlled and simulated environments, we analyze extensive, first-hand user reviews of AI coding assistants, which capture developers' authent…

ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations
Ben Kabongo, Vincent Guigue, Pirmin Lemberger
https://arxiv.org/abs/2508.20312 https://

ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations
Collaborative filtering drives many successful recommender systems but struggles with fine-grained user-item interactions and explainability. As users increasingly seek transparent recommendations, generating textual explanations through language models has become a critical research area. Existing methods employ either RNNs or Transformers. However, RNN-based approaches fail to leverage the capabilities of pre-trained Transformer models, whereas Transformer-based methods often suffer from subo…

Age of Information Aided Intelligent Grant-Free Massive Access for Heterogeneous mMTC Traffic
Zhongwen Sun, Wei Chen, Yuxuan Sun, Bo Ai
https://arxiv.org/abs/2509.14503 https://…

Age of Information Aided Intelligent Grant-Free Massive Access for Heterogeneous mMTC Traffic
With the arrival of 6G, the Internet of Things (IoT) traffic is becoming more and more complex and diverse. To meet the diverse service requirements of IoT devices, massive machine-type communications (mMTC) becomes a typical scenario, and more recently, grant-free random access (GF-RA) presents a promising direction due to its low signaling overhead. However, existing GF-RA research primarily focuses on improving the accuracy of user detection and data recovery, without considering the heterog…

« Cette résolution de ne pas construire du nouveau s’est imposée récemment, et j’ai vraiment honte qu’elle ne remonte qu’Š cinq ou six ans, quand j’ai réalisé que l’architecture était presque aussi néfaste que l’industrie pétrolière en matière de complicité dans la crise climatique, puisque environ 40% du carbone consommé provient du secteur du bâtiment. »
https://www.letemps.ch/culture/arts/adam-caruso-architecte-la-seule-position-valable-c-est-la-renovation?shouldPreventPurchase=true&inAppView=3.18.1&didomiConfig.user.externalConsent.value={"purposes":{"consent":{"enabled":["audiencem-hJxaeGrR","cookies","create_ads_profile","create_content_profile","device_characteristics","geolocation_data","improve_products","market_research","measure_ad_performance","measure_content_performance","parselyne-Ya2EC4dG","select_basic_ads","select_personalized_ads","select_personalized_content","use_limited_data_to_select_content"],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"vendors":{"consent":{"enabled":["amazon","c:aws-cloudfront","c:bannerwise-EeHwBjLT","c:chartbeat","c:cloudflare","c:echobox-GqD96L9K","c:forecast-ZRJPAUT7","c:googleana-4TXnJigR","c:hotjar","c:jsdelivr-xdD77X6w","c:jw-player","c:keycdn","c:linkedin","c:magnitein-ePEpbCQ7","c:meta-43HDmFRa","c:microsoft","c:nonli-jnWf8kqD","c:pianohybr-R3VKC2r4","c:pinterest","c:spyri-XAtBPx3i","c:tapadinc-VDTUUcKw","c:tiktok-KZAUQLZ9","c:unrulygro-nKyLqdKi","c:youtube","facebook","google","twitter",1,10,1020,1021,1028,1046,1069,108,109,11,1126,12,120,128,13,130,131,1310,132,136,137,138,142,143,154,156,16,164,202,209,21,211,23,238,24,241,243,25,253,264,28,284,290,30,301,32,36,39,4,412,422,45,469,475,493,50,519,52,559,58,61,612,617,68,684,69,73,76,77,783,790,793,80,81,811,84,85,855,898,91,92,95,965,98],"disabled":[]},"legitimate_interest":{"enabled":[],"disabled":[]}},"user_id":"36B03261-8BC8-4FB4-A026-0543FF748710","created":"2025-05-28T08:36:56.058Z","updated":"2025-08-30T09:35:31.055Z","source":{"type":"app","domain":"ch.letemps.LT"},"action":"webview"}

Social bias is prevalent in user reports of hate and abuse online
Florence E. Enock, Helen Z. Margetts, Jonathan Bright
https://arxiv.org/abs/2510.04748 https://

Social bias is prevalent in user reports of hate and abuse online
The prevalence of online hate and abuse is a pressing global concern. While tackling such societal harms is a priority for research across the social sciences, it is a difficult task, in part because of the magnitude of the problem. User engagement with reporting mechanisms (flagging) online is an increasingly important part of monitoring and addressing harmful content at scale. However, users may not flag content routinely enough, and when they do engage, they may be biased by group identity a…

SoK: Preconfirmations
Aikaterini-Panagiota Stouka, Conor McMenamin, Demetris Kyriacou, Lin Oshitani, Quentin Botha
https://arxiv.org/abs/2510.02947 https://

SoK: Preconfirmations
In recent years, significant research efforts have focused on improving blockchain throughput and confirmation speeds without compromising security. While decreasing the time it takes for a transaction to be included in the blockchain ledger enhances user experience, a fundamental delay still remains between when a transaction is issued by a user and when its inclusion is confirmed in the blockchain ledger. This delay limits user experience gains through the confirmation uncertainty it brings f…

MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces
Reuben A. Luera, Ryan Rossi, Franck Dernoncourt, Samyadeep Basu, Sungchul Kim, Subhojyoti Mukherjee, Puneet Mathur, Ruiyi Zhang, Jihyung Kil, Nedim Lipka, Seunghyun Yoon, Jiuxiang Gu, Zichao Wang, Cindy Xiong Bearfield, Branislav Kveton
https://

MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces
In an ideal design pipeline, user interface (UI) design is intertwined with user research to validate decisions, yet studies are often resource-constrained during early exploration. Recent advances in multimodal large language models (MLLMs) offer a promising opportunity to act as early evaluators, helping designers narrow options before formal testing. Unlike prior work that emphasizes user behavior in narrow domains such as e-commerce with metrics like clicks or conversions, we focus on subje…

Rethinking Purity and Diversity in Multi-Behavior Sequential Recommendation from the Frequency Perspective
Yongqiang Han, Kai Cheng, Kefan Wang, Enhong Chen
https://arxiv.org/abs/2508.20427

Rethinking Purity and Diversity in Multi-Behavior Sequential Recommendation from the Frequency Perspective
In recommendation systems, users often exhibit multiple behaviors, such as browsing, clicking, and purchasing. Multi-behavior sequential recommendation (MBSR) aims to consider these different behaviors in an integrated manner to improve the recommendation performance of the target behavior. However, some behavior data will also bring inevitable noise to the modeling of user interests. Some research efforts focus on data denoising from the frequency domain perspective to improve the accuracy of …

academia_edu: Academica.edu (2011)
Snapshot of the follower relationships among users of academia.edu, a platform for academics to share research papers, scraped in 2011. Nodes are users and a directed edge (i,j) denotes that user i follows j.
This network has 200169 nodes and 1398063 edges.
Tags: Social, Online, Unweighted
https://

Using AI for User Representation: An Analysis of 83 Persona Prompts
Joni Salminen, Danial Amin, Bernard Jansen
https://arxiv.org/abs/2508.13047 https://arx…

Using AI for User Representation: An Analysis of 83 Persona Prompts
We analyzed 83 persona prompts from 27 research articles that used large language models (LLMs) to generate user personas. Findings show that the prompts predominantly generate single personas. Several prompts express a desire for short or concise persona descriptions, which deviates from the tradition of creating rich, informative, and rounded persona profiles. Text is the most common format for generated persona attributes, followed by numbers. Text and numbers are often generated together, a…

The EmpathiSEr: Development and Validation of Software Engineering Oriented Empathy Scales
Hashini Gunatilake, John Grundy, Rashina Hoda, Ingo Mueller
https://arxiv.org/abs/2510.12546

The EmpathiSEr: Development and Validation of Software Engineering Oriented Empathy Scales
Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scales, while well-established in psychology and healthcare, often rely on language, scenarios, and assumptions that are not meaningful or inte…

Dual Actor DDPG for Airborne STAR-RIS Assisted Communications
Danish Rizvi, David Boyle
https://arxiv.org/abs/2509.13328 https://arxiv.org/pdf/2509.13328…

Dual Actor DDPG for Airborne STAR-RIS Assisted Communications
This study departs from the prevailing assumption of independent Transmission and Reflection Coefficients (TRC) in Airborne Simultaneous Transmit and Reflect Reconfigurable Intelligent Surface (STAR-RIS) research. Instead, we explore a novel multi-user downlink communication system that leverages a UAV-mounted STAR-RIS (Aerial-STAR) incorporating a coupled TRC phase shift model. Our key contributions include the joint optimization of UAV trajectory, active beamforming vectors at the base statio…

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
He Zhang, Yueyan Liu, Xin Guan, Jie Cai, John M. Carroll
https://arxiv.org/abs/2509.12709

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
Semi-structured interviews highly rely on the quality of follow-up questions, yet interviewers' knowledge and skills may limit their depth and potentially affect outcomes. While many studies have shown the usefulness of large language models (LLMs) for qualitative analysis, their possibility in the data collection process remains underexplored. We adopt an AI-driven "Wizard-of-Oz" setup to investigate how real-time LLM support in generating follow-up questions shapes semi-structured interviews.…

VortexPIA: Indirect Prompt Injection Attack against LLMs for Efficient Extraction of User Privacy
Yu Cui, Sicheng Pan, Yifei Liu, Haibin Zhang, Cong Zuo
https://arxiv.org/abs/2510.04261

VortexPIA: Indirect Prompt Injection Attack against LLMs for Efficient Extraction of User Privacy
Large language models (LLMs) have been widely deployed in Conversational AIs (CAIs), while exposing privacy and security threats. Recent research shows that LLM-based CAIs can be manipulated to extract private information from human users, posing serious security threats. However, the methods proposed in that study rely on a white-box setting that adversaries can directly modify the system prompt. This condition is unlikely to hold in real-world deployments. The limitation raises a critical que…

Stack Overflow Is Not Dead Yet: Crowd Answers Still Matter
Denis Helic, Tiago Santos
https://arxiv.org/abs/2509.05879 https://arxiv.org/pdf/2509.05879

Stack Overflow Is Not Dead Yet: Crowd Answers Still Matter
Millions of users visit Stack Overflow regularly to ask community for answers to their programming questions. However, like many other platforms, Stack Overflow consistently struggles with low user retention and declining levels of user contributions to the platform. With the introduction of ChatGPT in November 2022, these ongoing difficulties on Stack Overflow were further magnified, as many users moved toward ChatGPT for programming help. In this paper, we build upon recent research on this p…

VoxRole: A Comprehensive Benchmark for Evaluating Speech-Based Role-Playing Agents
Weihao Wu, Liang Cao, Xinyu Wu, Zhiwei Lin, Rui Niu, Jingbei Li, Zhiyong Wu
https://arxiv.org/abs/2509.03940

VoxRole: A Comprehensive Benchmark for Evaluating Speech-Based Role-Playing Agents
Recent significant advancements in Large Language Models (LLMs) have greatly propelled the development of Role-Playing Conversational Agents (RPCAs). These systems aim to create immersive user experiences through consistent persona adoption. However, current RPCA research faces dual limitations. First, existing work predominantly focuses on the textual modality, entirely overlooking critical paralinguistic features including intonation, prosody, and rhythm in speech, which are essential for con…

An LLM-enabled semantic-centric framework to consume privacy policies
Rui Zhao, Vladyslav Melnychuk, Jun Zhao, Jesse Wright, Nigel Shadbolt
https://arxiv.org/abs/2509.01716 http…

An LLM-enabled semantic-centric framework to consume privacy policies
In modern times, people have numerous online accounts, but they rarely read the Terms of Service or Privacy Policy of those sites, despite claiming otherwise, due to the practical difficulty in comprehending them. The mist of data privacy practices forms a major barrier for user-centred Web approaches, and for data sharing and reusing in an agentic world. Existing research proposed methods for using formal languages and reasoning for verifying the compliance of a specified policy, as a potentia…

CrisisNews: A Dataset Mapping Two Decades of News Articles on Online Problematic Behavior at Scale
Jeanne Choi, DongJae Kang, Yubin Choi, Juhoon Lee, Joseph Seering
https://arxiv.org/abs/2510.12243

CrisisNews: A Dataset Mapping Two Decades of News Articles on Online Problematic Behavior at Scale
As social media adoption grows globally, online problematic behaviors increasingly escalate into large-scale crises, requiring an evolving set of mitigation strategies. While HCI research often analyzes problematic behaviors with pieces of user-generated content as the unit of analysis, less attention has been given to event-focused perspectives that track how discrete events evolve. In this paper, we examine 'social media crises': discrete patterns of problematic behaviors originating and evol…

Research on Conversational Recommender System Considering Consumer Types
Yaying Luo, Hui Fang, Zhu Sun
https://arxiv.org/abs/2508.13209 https://arxiv.org/p…

Research on Conversational Recommender System Considering Consumer Types
Conversational Recommender Systems (CRS) provide personalized services through multi-turn interactions, yet most existing methods overlook users' heterogeneous decision-making styles and knowledge levels, which constrains both accuracy and efficiency. To address this gap, we propose CT-CRS (Consumer Type-Enhanced Conversational Recommender System), a framework that integrates consumer type modeling into dialogue recommendation. Based on consumer type theory, we define four user categories--depe…

Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agent Software
Pradyumna Shome, Sashreek Krishnan, Sauvik Das
https://arxiv.org/abs/2509.14528 h…

Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agent Software
There is growing imprecision about what "AI agents" are, what they can do, and how effectively they can be used by their intended users. We pose two key research questions: (i) How does the tech industry conceive of and market "AI agents"? (ii) What challenges do end-users face when attempting to use commercial AI agents for their advertised uses? We first performed a systematic review of marketed use cases for 102 commercial AI agents, finding that they fall into three umbrella categories: orc…

Relational Algebras for Subset Selection and Optimisation
David Robert Pratten, Luke Mathieson, Fahimeh Ramezani
https://arxiv.org/abs/2509.06439 https://a…

Relational Algebras for Subset Selection and Optimisation
The database community lacks a unified relational query language for subset selection and optimisation queries, limiting both user expression and query optimiser reasoning about such problems. Decades of research (latterly under the rubric of prescriptive analytics) have produced powerful evaluation algorithms with incompatible, ad-hoc SQL extensions that specify and filter through distinct mechanisms. We present the first unified algebraic foundation for these queries, introducing relational e…

Can I Trust This Chatbot? Assessing User Privacy in AI-Healthcare Chatbot Applications
Ramazan Yener, Guan-Hung Chen, Ece Gumusel, Masooda Bashir
https://arxiv.org/abs/2509.14581

Can I Trust This Chatbot? Assessing User Privacy in AI-Healthcare Chatbot Applications
As Conversational Artificial Intelligence (AI) becomes more integrated into everyday life, AI-powered chatbot mobile applications are increasingly adopted across industries, particularly in the healthcare domain. These chatbots offer accessible and 24/7 support, yet their collection and processing of sensitive health data present critical privacy concerns. While prior research has examined chatbot security, privacy issues specific to AI healthcare chatbots have received limited attention. Our s…

Reverse Browser: Vector-Image-to-Code Generator
Zoltan Toth-Czifra
https://arxiv.org/abs/2509.05394 https://arxiv.org/pdf/2509.05394

Reverse Browser: Vector-Image-to-Code Generator
Automating the conversion of user interface design into code (image-to-code or image-to-UI) is an active area of software engineering research. However, the state-of-the-art solutions do not achieve high fidelity to the original design, as evidenced by benchmarks. In this work, I approach the problem differently: I use vector images instead of bitmaps as model input. I create several large datasets for training machine learning models. I evaluate the available array of Image Quality Assessment …

System Password Security: Attack and Defense Mechanisms
Chaofang Shi, Zhongwen Li, Xiaoqi Li
https://arxiv.org/abs/2510.10246 https://arxiv.org/pdf/2510.10…

System Password Security: Attack and Defense Mechanisms
System passwords serve as critical credentials for user authentication and access control when logging into operating systems or applications. Upon entering a valid password, users pass verification to access system resources and execute corresponding operations. In recent years, frequent password cracking attacks targeting system passwords have posed a severe threat to information system security. To address this challenge, in-depth research into password cracking attack methods and defensive …

Soundtracks of Our Lives: How Age Influences Musical Preferences
Arsen Matej Golubovikj, Bruce Ferwerda, Alan Said, Marko Tal\v{c}i\v{c}
https://arxiv.org/abs/2509.08337 https:/…

Soundtracks of Our Lives: How Age Influences Musical Preferences
The majority of research in recommender systems, be it algorithmic improvements, context-awareness, explainability, or other areas, evaluates these systems on datasets that capture user interaction over a relatively limited time span. However, recommender systems can very well be used continuously for extended time. Similarly so, user behavior may evolve over that extended time. Although media studies and psychology offer a wealth of research on the evolution of user preferences and behavior as…

User Exploration and Exploitation Behavior Under the Influence of Real-time Interactions in Live Streaming Environments
Akira Matsui, Kazuki Fujikawa, Ryo Sasaki, Ryo Adachi
https://arxiv.org/abs/2509.09138

User Exploration and Exploitation Behavior Under the Influence of Real-time Interactions in Live Streaming Environments
Live streaming platforms offer a distinctive way for users and content creators to interact with each other through real-time communication. While research on user behavior in online platforms has explored how users discover their favorite content from creators and engage with them, the role of real-time features remains unclear. There are open questions as to what commonalities and differences exist in users' relationships with live streaming platforms compared to traditional on-demand style p…

Enhancing Systematic Interoperability: Convergences and Mismatches between Web 3.0 and the EU Data Act
Linyi Xu, Zihao Li
https://arxiv.org/abs/2508.07356 https://

Enhancing Systematic Interoperability: Convergences and Mismatches between Web 3.0 and the EU Data Act
Interoperability is increasingly recognised as a foundational principle for fostering innovation, competition, and user autonomy in the evolving digital ecosystem. Existing research on interoperability predominantly focuses either on technological interoperability itself or on the legal regulations concerning interoperability, with insufficient exploration of their interdisciplinary intersection. This paper compares the technological interoperability in Web 3.0 with the theoretical framework of…

ReVISit 2: A Full Experiment Life Cycle User Study Framework
Zach Cutler, Jack Wilburn, Hilson Shrestha, Yiren Ding, Brian Bollen, Khandaker Abrar Nadib, Tingying He, Andrew McNutt, Lane Harrison, Alexander Lex
https://arxiv.org/abs/2508.03876

ReVISit 2: A Full Experiment Life Cycle User Study Framework
Online user studies of visualizations, visual encodings, and interaction techniques are ubiquitous in visualization research. Yet, designing, conducting, and analyzing studies effectively is still a major burden. Although various packages support such user studies, most solutions address only facets of the experiment life cycle, make reproducibility difficult, or do not cater to nuanced study designs or interactions. We introduce reVISit 2, a software framework that supports visualization resea…

Understanding NFTs from EIP Standards
Minfeng Qi, Qin Wang, Guangsheng Yu, Ruiqiang Li, Victor Zhou, Shiping Chen
https://arxiv.org/abs/2508.07190 https://…

Understanding NFTs from EIP Standards
We argue that the technical foundations of non-fungible tokens (NFTs) remain inadequately understood. Prior research has focused on market dynamics, user behavior, and isolated security incidents, yet systematic analysis of the standards underpinning NFT functionality is largely absent. We present the first study of NFTs through the lens of Ethereum Improvement Proposals (EIPs). We conduct a large-scale empirical analysis of 191 NFT-related EIPs and 10K+ Ethereum Magicians discussions (as of …

LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
Ren Qin, Chai Zheng, Xiao Xijun, Zheng Yuchao, Wu Di
https://arxiv.org/abs/2508.15486 htt…

LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
Precisely modeling user ultra-long sequences is critical for industrial recommender systems. Current approaches predominantly focus on leveraging ultra-long sequences in the ranking stage, whereas research for the candidate retrieval stage remains under-explored. This paper presents LongRetriever, a practical framework for incorporating ultra-long sequences into the retrieval stage of recommenders. Specifically, we propose in-context training and multi-context retrieval, which enable candidate-…

Effective and Stealthy One-Shot Jailbreaks on Deployed Mobile Vision-Language Agents
Renhua Ding, Xiao Yang, Zhengwei Fang, Jun Luo, Kun He, Jun Zhu
https://arxiv.org/abs/2510.07809

Effective and Stealthy One-Shot Jailbreaks on Deployed Mobile Vision-Language Agents
Large vision-language models (LVLMs) enable autonomous mobile agents to operate smartphone user interfaces, yet vulnerabilities to UI-level attacks remain critically understudied. Existing research often depends on conspicuous UI overlays, elevated permissions, or impractical threat models, limiting stealth and real-world applicability. In this paper, we present a practical and stealthy one-shot jailbreak attack that leverages in-app prompt injections: malicious applications embed short prompts…

Limitations of Current Evaluation Practices for Conversational Recommender Systems and the Potential of User Simulation
Nolwenn Bernard, Krisztian Balog
https://arxiv.org/abs/2510.05624

Limitations of Current Evaluation Practices for Conversational Recommender Systems and the Potential of User Simulation
Research and development on conversational recommender systems (CRSs) critically depends on sound and reliable evaluation methodologies. However, the interactive nature of these systems poses significant challenges for automatic evaluation. This paper critically examines current evaluation practices and identifies two key limitations: the over-reliance on static test collections and the inadequacy of existing evaluation metrics. To substantiate this critique, we analyze real user interactions w…

Immersive Invaders: Privacy Threats from Deceptive Design in Virtual Reality Games and Applications
Hilda Hadan, Michaela Valiquette, Lennart E. Nacke, Leah Zhang-Kennedy
https://arxiv.org/abs/2509.09916

Immersive Invaders: Privacy Threats from Deceptive Design in Virtual Reality Games and Applications
Virtual Reality (VR) technologies offer immersive experiences but collect substantial user data. While deceptive design is well-studied in 2D platforms, little is known about its manifestation in VR environments and its impact on user privacy. This research investigates deceptive designs in privacy communication and interaction mechanisms of 12 top-rated VR games and applications through autoethnographic evaluation of the applications and thematic analysis of privacy policies. We found that whi…

DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
Moyu Zhang, Yun Chen, Yujun Jin, Jinxin Hu, Yu Zhang
https://arxiv.org/abs/2508.14500

DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
Recent advances in generative models have inspired the field of recommender systems to explore generative approaches, but most existing research focuses on sequence generation, a paradigm ill-suited for click-through rate (CTR) prediction. CTR models critically depend on a large number of cross-features between the target item and the user to estimate the probability of clicking on the item, and discarding these cross-features will significantly impair model performance. Therefore, to harness t…

Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction
Mansi Sharma, Shuang Chen, Philipp M\"uller, Maurice Rekrut, Antonio Kr\"uger
https://arxiv.org/abs/2508.01860

Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction
For machines to effectively assist humans in challenging visual search tasks, they must differentiate whether a human is simply glancing into a scene (navigational intent) or searching for a target object (informational intent). Previous research proposed combining electroencephalography (EEG) and eye-tracking measurements to recognize such search intents implicitly, i.e., without explicit user input. However, the applicability of these approaches to real-world scenarios suffers from two key li…

Protecting participants or population? Comparison of k-anonymous Origin-Destination matrices
Pietro Armenante, Kai Huang, Nikhil Jha, Luca Vassio
https://arxiv.org/abs/2509.12950

Protecting participants or population? Comparison of k-anonymous Origin-Destination matrices
Origin-Destination (OD) matrices are a core component of research on users' mobility and summarize how individuals move between geographical regions. These regions should be small enough to be representative of user mobility, without incurring substantial privacy risks. There are two added values of the NetMob2025 challenge dataset. Firstly, the data is extensive and contains a lot of socio-demographic information that can be used to create multiple OD matrices, based on the segments of the pop…

Beyond Gaze Overlap: Analyzing Joint Visual Attention Dynamics Using Egocentric Data
Kumushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna
https://arxiv.org/abs/2509.12419

Beyond Gaze Overlap: Analyzing Joint Visual Attention Dynamics Using Egocentric Data
Joint visual attention (JVA) provides informative cues on human behavior during social interactions. The ubiquity of egocentric eye-trackers and large-scale datasets on everyday interactions offer research opportunities in identifying JVA in multi-user environments. We propose a novel approach utilizing spatiotemporal tubes centered on attention rendered by individual gaze and detect JVA using deep-learning-based feature mapping. Our results reveal object-focused collaborative tasks to yield hi…

Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems
Xin Wang, Xiaowen Huang, Jitao Sang
https://arxiv.org/abs/2509.11139

Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems
Personalized news recommendation systems inadvertently create information cocoons--homogeneous information bubbles that reinforce user biases and amplify societal polarization. To address the lack of comprehensive assessment frameworks in prior research, we propose a multidimensional analysis that evaluates cocoons through dual perspectives: (1) Individual homogenization via topic diversity (including the number of topic categories and category information entropy) and click repetition; (2) Gro…

Privacy Perceptions in Robot-Assisted Well-Being Coaching: Examining the Roles of Information Transparency, User Control, and Proactivity
Atikkhan Faridkhan Nilgar, Manuel Dietrich, Kristof Van Laerhoven
https://arxiv.org/abs/2509.04358

Privacy Perceptions in Robot-Assisted Well-Being Coaching: Examining the Roles of Information Transparency, User Control, and Proactivity
Social robots are increasingly recognized as valuable supporters in the field of well-being coaching. They can function as independent coaches or provide support alongside human coaches, and healthcare professionals. In coaching interactions, these robots often handle sensitive information shared by users, making privacy a relevant issue. Despite this, little is known about the factors that shape users' privacy perceptions. This research aims to examine three key factors systematically: (1) the…

Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
Xiaoxi Cui, Weihai Lu, Yu Tong, Yiheng Li, Zhejun Zhao
https://arxiv.org/abs/2508.05352

Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
The sequential recommendation system utilizes historical user interactions to predict preferences. Effectively integrating diverse user behavior patterns with rich multimodal information of items to enhance the accuracy of sequential recommendations is an emerging and challenging research direction. This paper focuses on the problem of multi-modal multi-behavior sequential recommendation, aiming to address the following challenges: (1) the lack of effective characterization of modal preferences…

Model-agnostic post-hoc explainability for recommender systems
Irina Ar\'evalo, Jose L Salmeron
https://arxiv.org/abs/2509.10245 https://arxiv.org/pdf/…

Model-agnostic post-hoc explainability for recommender systems
Recommender systems often benefit from complex feature embeddings and deep learning algorithms, which deliver sophisticated recommendations that enhance user experience, engagement, and revenue. However, these methods frequently reduce the interpretability and transparency of the system. In this research, we develop a systematic application, adaptation, and evaluation of deletion diagnostics in the recommender setting. The method compares the performance of a model to that of a similar model tr…

Octo's Heartland: Supporting Children with Congenital Heart Disease through Digital Health Education
Irene Zeng, Neda Barbazi, Ji Youn Shin, Gurumurthy Hiremath, Carlye Anne Lauff
https://arxiv.org/abs/2509.02537

Octo's Heartland: Supporting Children with Congenital Heart Disease through Digital Health Education
Children with congenital heart disease (CHD) often face challenges that require them to understand complex medical information from an early age in order to support lifelong care and improve health outcomes. However, prior research has rarely included young children in designing and evaluating digital tools to support health education using developmentally appropriate strategies. This study is part of a multi-phase research involving participatory design (PD), user testing, and iterative develo…

Multi-Granularity Sequence Denoising with Weakly Supervised Signal for Sequential Recommendation
Liang Li, Zhou Yang, Xiaofei Zhu
https://arxiv.org/abs/2510.10564 https://

Multi-Granularity Sequence Denoising with Weakly Supervised Signal for Sequential Recommendation
Sequential recommendation aims to predict the next item based on user interests in historical interaction sequences. Historical interaction sequences often contain irrelevant noisy items, which significantly hinders the performance of recommendation systems. Existing research employs unsupervised methods that indirectly identify item-granularity irrelevant noise by predicting the ground truth item. Since these methods lack explicit noise labels, they are prone to misidentify users' interested i…

Adaptique: Multi-objective and Context-aware Online Adaptation of Selection Techniques in Virtual Reality
Chao-Jung Lai, Mauricio Sousa, Tianyu Zhang, Ludwig Sidenmark, Tovi Grossman
https://arxiv.org/abs/2508.08505

Adaptique: Multi-objective and Context-aware Online Adaptation of Selection Techniques in Virtual Reality
Selection is a fundamental task that is challenging in virtual reality due to issues such as distant and small targets, occlusion, and target-dense environments. Previous research has tackled these challenges through various selection techniques, but complicates selection and can be seen as tedious outside of their designed use case. We present Adaptique, an adaptive model that infers and switches to the most optimal selection technique based on user and environmental information. Adaptique con…

A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang
https://arxiv.org/abs/2508.05668

A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
The advent of Large Language Models (LLMs) has significantly revolutionized web search. The emergence of LLM-based Search Agents marks a pivotal shift towards deeper, dynamic, autonomous information seeking. These agents can comprehend user intentions and environmental context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web. Leading examples like OpenAI's Deep Research highlight their potential for deep information mining and real-world a…

Evaluating the Impact of LLM-guided Reflection on Learning Outcomes with Interactive AI-Generated Educational Podcasts
Vishnu Menon, Andy Cherney, Elizabeth B. Cloude, Li Zhang, Tiffany D. Do
https://arxiv.org/abs/2508.04787

Evaluating the Impact of LLM-guided Reflection on Learning Outcomes with Interactive AI-Generated Educational Podcasts
This study examined whether embedding LLM-guided reflection prompts in an interactive AI-generated podcast improved learning and user experience compared to a version without prompts. Thirty-six undergraduates participated, and while learning outcomes were similar across conditions, reflection prompts reduced perceived attractiveness, highlighting a call for more research on reflective interactivity design.

A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives
Kuan Zou, Aixin Sun
https://arxiv.org/abs/2509.06002 https://

A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives
Recommender systems have generated tremendous value for both users and businesses, drawing significant attention from academia and industry alike. However, due to practical constraints, academic research remains largely confined to offline dataset optimizations, lacking access to real user data and large-scale recommendation platforms. This limitation reduces practical relevance, slows technological progress, and hampers a full understanding of the key challenges in recommender systems. In this…

How Long Does It Take to Alleviate Discomfort? A Preliminary Study on Reducing Cybersickness in Novice Users
Zhengxin Zhang, Shufang Qian, Yi Wang, Xiao Liu, Thuong Hoang, Chetan Arora, Jingjing Zhang, Henry Been Lirn Duh
https://arxiv.org/abs/2508.01070

How Long Does It Take to Alleviate Discomfort? A Preliminary Study on Reducing Cybersickness in Novice Users
Cybersickness significantly impacts the user experience in VR applications. Locomotion tunneling is a widely adopted technique for mitigating cybersickness in susceptible users. However, there is a lack of research investigating the effects of prolonged use of locomotion tunneling among novice users. To fill this gap, we used VRChat as our experimental platform. We recruited 24 novice VR users, defined as participants with no prior experience using immersive virtual environments. We collected f…

KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation
Changle Qu, Sunhao Dai, Ke Guo, Liqin Zhao, Yanan Niu, Xiao Zhang, Jun Xu
https://arxiv.org/abs/2508.05633

KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation
Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interactions, and highly engaging user experiences. These unique characteristics introduce new challenges that differentiate live streaming recommendation from traditional recommendation settings and have garnered increasing attention from industry in recent years. However, research progress in academia has been hindered by the lack of publicly available datasets …

Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
Matus Krajcovic, Peter Demcak, Eduard Kuric
https://arxiv.org/abs/2508.02376

Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
Embodied conversational agents (ECAs) are increasingly more realistic and capable of dynamic conversations. In online surveys, anthropomorphic agents could help address issues like careless responding and satisficing, which originate from the lack of personal engagement and perceived accountability. However, there is a lack of understanding of how ECAs in user experience research may affect participant engagement, satisfaction, and the quality of responses. As a proof of concept, we propose an …

Balancing Accuracy and Novelty with Sub-Item Popularity
Chiara Mallamaci, Aleksandr Vladimirovich Petrov, Alberto Carlo Maria Mancino, Vito Walter Anelli, Tommaso Di Noia, Craig Macdonald
https://arxiv.org/abs/2508.05198

Balancing Accuracy and Novelty with Sub-Item Popularity
In the realm of music recommendation, sequential recommenders have shown promise in capturing the dynamic nature of music consumption. A key characteristic of this domain is repetitive listening, where users frequently replay familiar tracks. To capture these repetition patterns, recent research has introduced Personalised Popularity Scores (PPS), which quantify user-specific preferences based on historical frequency. While PPS enhances relevance in recommendation, it often reinforces already-k…

Body Ownership Affects the Processing of Sensorimotor Contingencies in Virtual Reality
Evan G. Center, Matti Pouke, Alessandro Nardi, Lukas Gehrke, Klaus Gramann, Timo Ojala, Steven M. LaValle
https://arxiv.org/abs/2509.01420

Body Ownership Affects the Processing of Sensorimotor Contingencies in Virtual Reality
Presence in virtual reality (VR), the subjective sense of "being there" in a virtual environment, is notoriously difficult to measure. Electroencephalography (EEG) may offer a promising, unobtrusive means of assessing a user's momentary state of presence. Unlike traditional questionnaires, EEG does not interrupt the experience or rely on users' retrospective self-reports, thereby avoiding interference with the very state it aims to capture. Previous research has attempted to quantify presence i…