Tootfinder

Efficiently Verifiable Proofs of Data Attribution
Ari Karchmer, Seth Neel, Martin Pawelczyk
https://arxiv.org/abs/2508.10866 https://arxiv.org/pdf/2508.108…

Efficiently Verifiable Proofs of Data Attribution
Data attribution methods aim to answer useful counterfactual questions like "what would a ML model's prediction be if it were trained on a different dataset?" However, estimation of data attribution models through techniques like empirical influence or "datamodeling" remains very computationally expensive. This causes a critical trust issue: if only a few computationally rich parties can obtain data attributions, how can resource-constrained parties trust that the provided attributions are inde…

What would compel someone, with everything happening right now, to trust a tech company to host & protect your personal DNA or health data?
Ignorance? Sloth? Apathy? All 3?
From: @…
htt…

Trump is recklessly shredding our economic advantages in the global economy.
Companies that are looking to make investments shouldn’t have to worry if Trump has cooked the books on economic data--but that’s exactly what’s happening following his decision.
-- Katie Porter

Firing of labor statistics head undermines trust in key data, ex-Trump official warns
President Trump is expected to nominate a new head of the Bureau of Labor Statistics this week, days after firing Commissioner Erika McEntarfer following the release of a disappointing jobs report. The president dismissed the data as “rigged” and “manipulated for political purposes.” Geoff Bennett discussed more with William Beach, the commissioner of labor statistics during the first Trump term.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
Jane Carney, Kushal Upreti, Gaby G. Dagher, Tim Andersen
https://arxiv.org/abs/2508.10042 …

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present \Sys, a novel blockchain-enabled poison detection framewor…

A Curriculum Learning Approach to Reinforcement Learning: Leveraging RAG for Multimodal Question Answering
Chenliang Zhang, Lin Wang, Yuanyuan Lu, Yusheng Qi, Kexin Wang, Peixu Hou, Wenshi Chen
https://arxiv.org/abs/2508.10337

A Curriculum Learning Approach to Reinforcement Learning: Leveraging RAG for Multimodal Question Answering
This paper describes the solutions of the Dianping-Trust-Safety team for the META CRAG-MM challenge. The challenge requires building a comprehensive retrieval-augmented generation system capable for multi-modal multi-turn question answering. The competition consists of three tasks: (1) answering questions using structured data retrieved from an image-based mock knowledge graph, (2) synthesizing information from both knowledge graphs and web search results, and (3) handling multi-turn conversati…

FPF at PDP Week 2025: Generative AI, Digital Trust, and the Future of Cross-Border Data Transfers in APAC
https://fpf.org/blog/fpf-at-pdp-week-2025-generative-ai-digital-trust-and-the-future-of-cross-border-data-tran…

Auch der #owncloud Hosting Service https://owncloud.online wurde nun auch weggegeben. Gab wohl noch Kunden, die nicht auf die proprietäre

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design
Yuhao Sun, Yihua Zhang, Gaowen Liu, Hongtao Xie, Sijia Liu
https://arxiv.org/abs/2508.10065

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design
With the increasing demand for the right to be forgotten, machine unlearning (MU) has emerged as a vital tool for enhancing trust and regulatory compliance by enabling the removal of sensitive data influences from machine learning (ML) models. However, most MU algorithms primarily rely on in-training methods to adjust model weights, with limited exploration of the benefits that data-level adjustments could bring to the unlearning process. To address this gap, we propose a novel approach that le…

What Shapes User Trust in ChatGPT? A Mixed-Methods Study of User Attributes, Trust Dimensions, Task Context, and Societal Perceptions among University Students
Kadija Bouyzourn, Alexandra Birch
https://arxiv.org/abs/2507.05046

What Shapes User Trust in ChatGPT? A Mixed-Methods Study of User Attributes, Trust Dimensions, Task Context, and Societal Perceptions among University Students
This mixed-methods inquiry examined four domains that shape university students' trust in ChatGPT: user attributes, seven delineated trust dimensions, task context, and perceived societal impact. Data were collected through a survey of 115 UK undergraduate and postgraduate students and four complementary semi-structured interviews. Behavioural engagement outweighed demographics: frequent use increased trust, whereas self-reported understanding of large-language-model mechanics reduced it. Among…

Technical specification of a framework for the collection of clinical images and data
Alistair Mackenzie (Royal Surrey NHS Foundation Trust, Guildford, UK), Mark Halling-Brown (Royal Surrey NHS Foundation Trust, Guildford, UK), Ruben van Engen (Dutch Expert Centre for Screening), Carlijn Roozemond (Dutch Expert Centre for Screening), Lucy Warren (Royal Surrey NHS Foundation Trust, Guildford, UK), Dominic Ward (Royal Surrey NHS Foundation Trust, Guildford, UK), Nadia Smith (Royal Surrey…

Technical specification of a framework for the collection of clinical images and data
In this report a framework for the collection of clinical images and data for use when training and validating artificial intelligence (AI) tools is described. The report contains not only information about the collection of the images and clinical data, but the ethics and information governance processes to consider ensuring the data is collected safely, and the infrastructure and agreements required to allow for the sharing of data with other groups. A key characteristic of the main collect…

Check out today's Metacurity for the most critical infosec developments you might have missed over the weekend, including
--Chinese espionage campaign targeted House staffers ahead of trade talks,
--Ethical hackers uncover catastrophic flaws in restaurant chain's platforms,
--Salesloft Drift hack began last March,
--Customer data stolen in Wealthsimple breach,
--Trump to formally nominate Harman for NSA/Cybercom slot,
--Don't trust XChat's e…

Chinese espionage campaign targeted House staffers ahead of trade talks
Ethical hackers uncover catastrophic flaws in restaurant chain's platforms, Salesloft Drift hack began last March, Customer data stolen in Wealthsimple breach, Trump to formally nominate Harman for NSA/Cybercom slot, Don't trust XChat's encryption, Czech Republic warns of Chinese tech, much more

»Life360 Secretly Sells Users’ Geolocation Data to Third Parties, Class Action Claims:
A proposed class action alleges family tracking app Life360 secretly sells data about users’ locations and movements to third parties.«
When apps are (almost) free, they make unlimited money by selling you. Don't trust any, if they don't communicate their data protection.
🕵️

Life360 Secretly Sells Users’ Geolocation Data to Third Parties, Class Action Claims
A class action alleges family tracking app Life360 secretly sells data about users’ locations and movements to third parties.

Membrane: A Cryptographic Access Control System for Data Lakes
Sam Kumar, Samyukta Yagati, Conor Power, David E. Culler, Raluca Ada Popa
https://arxiv.org/abs/2509.08740 https:/…

Membrane: A Cryptographic Access Control System for Data Lakes
Organizations use data lakes to store and analyze sensitive data. But hackers may compromise data lake storage to bypass access controls and access sensitive data. To address this, we propose Membrane, a system that (1) cryptographically enforces data-dependent access control views over a data lake, (2) without restricting the analytical queries data scientists can run. We observe that data lakes, unlike DBMSes, disaggregate computation and storage into separate trust domains, making at-rest en…

Doesn't seem controversial. 'Palantir (inclusive of any associated companies) is an unacceptable choice of partner to create a Federated Data Platform for the NHS. It recognises that this partnership threatens to undermine public trust in NHS data systems'

ARM 2025: BMA passes resolution on Health Information Management and Information Technology - BMA media centre - BMA
BMA statement. 

AWS deleted my 10-year account and all data without warning
"On July 23, 2025, AWS deleted my 10-year-old account and every byte of data I had stored with them. No warning. No grace period. No recovery options. Just complete digital annihilation".
https://www.seuros.com/blog/aws…

Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI
Botao Zhu, Jeslyn Wang, Dusit Niyato, Xianbin Wang
https://arxiv.org/abs/2509.08151 https…

Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI
Accurate trustworthiness evaluation of potential collaborating devices is essential for the effective execution of complex computing tasks. This evaluation process involves collecting diverse trust-related data from potential collaborators, including historical performance and available resources, for collaborator selection. However, when each task owner independently assesses all collaborators' trustworthiness, frequent data exchange, complex reasoning, and dynamic situation changes can result…

AWS deleted my 10-year account and all data without warning
https://www.seuros.com/blog/aws-deleted-my-10-year-account-without-warning/
- The Architecture That Should Have Protected Me -
Small reminder that there is no 100% protection in the…

Stack Overflow survey: 84% of developers use or plan to use AI tools in their workflow, up from 76% in 2024, and 33% trust AI accuracy, down from 43% in 2024 (Sean Michael Kerner/VentureBeat)
https://venturebeat.com/ai/stack-overflo…

Stack Overflow data reveals the hidden productivity tax of ‘almost right’ AI code
Stack Overflow survey shows that as more enterprise developers actually use AI tools, their expectations aren't being met by reality.

"The #cloud isn’t your friend. It’s a business. And when their business needs conflict with your data’s existence, guess which one wins?"
"I wasn’t alone in being targeted by AWS—especially #MENA. Hundreds of Reddit threads, websites, forums, all telling similar stories."
"

AWS deleted my 10-year account and all data without warning
https://www.seuros.com/blog/aws-deleted-my-10-year-account-without-warning/

AWS deleted my 10-year account and all data without warning
After 10 years as an AWS customer and open-source contributor, they deleted my account and all data with zero warning. Here's how AWS's 'verification' process became a digital execution, and why you should never trust cloud providers with your only copy of anything.

The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data
Petteri Teikari, Mike Jarrell, Maryam Azh, Harri Pesola
https://arxiv.org/abs/2508.02765

The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data
The Uniform Appraisal Dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to structured, machine-readable formats. This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems. We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements.…

Innovating Augmented Reality Security: Recent E2E Encryption Approaches
Hamish Alsop, Leandros Maglaras, Helge Janicke, Iqbal H. Sarker, Mohamed Amine Ferrag
https://arxiv.org/abs/2509.10313

Innovating Augmented Reality Security: Recent E2E Encryption Approaches
End-to-end encryption (E2EE) has emerged as a fundamental element of modern digital communication, protecting data from unauthorized access during transmission. By design, E2EE ensures that only the intended recipient can decrypt the information, making it inaccessible even to service providers. Yet, this powerful safeguard of individual privacy and digital trust also introduces a paradox: it can simultaneously prevent law enforcement efforts by hiding potential malicious activities. This paper…

Today, using #GAFAM software is no longer a matter of needs or preferences. Today, it is a choice in the domain of ethics.
If someone gives me their personal data, be it their contact data, image, or anything else, it is my solemn duty to keep that data secure. If I give it away to an app that uses it for marketing purposes, to train models, to manipulate people, or simply sells it, I fail that trust.
So don't give apps unnecessary permissions, or simply don't use such apps. And if the whole system abuses your data — well, if you can't change it, there are always notebooks and pens, you know.
#FreeSoftware

When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference
Jack Heinzel, Salvatore Vitale
https://arxiv.org/abs/2509.07221 https://…

When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference
The coming years of gravitational wave astrophysics promises thousands of new detections, which can unlock fundamental scientific insights if the information in each observation can be properly synthesized into a coherent picture. State-of-the-art approaches often accomplish this with hierarchical Bayesian inference. However, this typically relies on Monte Carlo approximations that are already very expensive in current data, and may become prohibitively so in the future. In this paper we show h…

Just wondering when an european "Linkedin" will start or if there is already one available? We clearly can't trust a US company with all our personal data, relations, employers etc. ?
#linkedin #eu #privacy

Czech cyber agency warns against using services and products that send data to China https://therecord.media/czech-nukib-warns-against-products-sending-data-china

Climate Policy Radar is developing an open-source knowledge graph for climate policy. At Berlin Buzzwords, Harrison Pim and Fred O'Loughlin discussed how they combine in-house expertise with a scalable data infrastructure in order to identify key concepts within thousands of global climate policy documents.
Watch the full session: https://youtu.be/H6BhF6zSvp4?si=Jzioutp8n__2XY2c
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Very interesting looking paper: In tech we trust: A history of technophilia in the Intergovernmental Panel on Climate Change's (IPCC) climate mitigation expertise
https://www.sciencedirect.com/science/article/pii/S2214629625003615
Hopefully someone wil…

Aspect-Oriented Programming in Secure Software Development: A Case Study of Security Aspects in Web Applications
Mterorga Ukor
https://arxiv.org/abs/2509.07449 https://

Aspect-Oriented Programming in Secure Software Development: A Case Study of Security Aspects in Web Applications
Security remains a critical challenge in modern web applications, where threats such as unauthorized access, data breaches, and injection attacks continue to undermine trust and reliability. Traditional Object-Oriented Programming (OOP) often intertwines security logic with business functionality, leading to code tangling, scattering, and reduced maintainability. This study investigates the role of Aspect-Oriented Programming (AOP) in enhancing secure software development by modularizing cross-…

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework
N Harshit, K Mounvik
https://arxiv.org/abs/2508.07085 https://…

Improving Real-Time Concept Drift Detection using a Hybrid Transformer-Autoencoder Framework
In applied machine learning, concept drift, which is either gradual or abrupt changes in data distribution, can significantly reduce model performance. Typical detection methods,such as statistical tests or reconstruction-based models,are generally reactive and not very sensitive to early detection. Our study proposes a hybrid framework consisting of Transformers and Autoencoders to model complex temporal dynamics and provide online drift detection. We create a distinct Trust Score methodology,…

23andMe's Data Sold to Nonprofit Run by Its Co-Founder - 'And I Still Don't Trust It' - Slashdot
https://science.slashdot.org/story/25/07/19/0252236/23andmes-data-sold-to-nonprofit-run-by-its-co-founder---and-i-still-dont-trust-it

Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
Luisa Gall\'ee, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael G\"otz
https://arxiv.org/abs/2508.00639

Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
Classification models that provide human-interpretable explanations enhance clinicians' trust and usability in medical image diagnosis. One research focus is the integration and prediction of pathology-related visual attributes used by radiologists alongside the diagnosis, aligning AI decision-making with clinical reasoning. Radiologists use attributes like shape and texture as established diagnostic criteria and mirroring these in AI decision-making both enhances transparency and enables expli…

[Thread] an NYT standards editor responds to criticism of its recent Zohran Mamdani story, says the "ultimate source" was Columbia data that Mamdani confirmed (Patrick Healy/@patrickhealynyt)
https://x.com/patrickhealynyt/status/1941262786006483418

Patrick Healy (@patrickhealynyt) on X
As the @nytimes assistant managing editor for Standards and Trust, I’ve received reader feedback regarding our reporting on Zohran Mamdani’s 2009 application to Columbia University. To provide context on how the reporting came together, I wanted to share some information:

Trust and Reputation in Data Sharing: A Survey
Wenbo Wu, George Konstantinidis
https://arxiv.org/abs/2508.14028 https://arxiv.org/pdf/2508.14028

Trust and Reputation in Data Sharing: A Survey
Data sharing is the fuel of the galloping artificial intelligence economy, providing diverse datasets for training robust models. Trust between data providers and data consumers is widely considered one of the most important factors for enabling data sharing initiatives. Concerns about data sensitivity, privacy breaches, and misuse contribute to reluctance in sharing data across various domains. In recent years, there has been a rise in technological and algorithmic solutions to measure, captur…

from my link log —
Cracking the Vault: flaws in authentication, identity, and authorization in HashiCorp Vault.
https://cyata.ai/blog/cracking-the-vault-how-we-found-zero-day-flaws-in-authe…

Cracking the Vault: how we found zero-day flaws in authentication, identity, and authorization in HashiCorp Vault - Cyata | The Control Plane for Agentic Identity
Introduction: when the trust model can’t be trusted Secrets vaults are the backbone of digital infrastructure. They store the credentials, tokens, and certificates that govern access to systems, services, APIs, and data. They’re not just a part of the trust model, they are the trust model. In other words, if your vault is compromised, your […]

Effect of AI Performance, Risk Perception, and Trust on Human Dependence in Deepfake Detection AI system
Yingfan Zhou, Ester Chen, Manasa Pisipati, Aiping Xiong, Sarah Rajtmajer
https://arxiv.org/abs/2508.01906

Effect of AI Performance, Risk Perception, and Trust on Human Dependence in Deepfake Detection AI system
Synthetic images, audio, and video can now be generated and edited by Artificial Intelligence (AI). In particular, the malicious use of synthetic data has raised concerns about potential harms to cybersecurity, personal privacy, and public trust. Although AI-based detection tools exist to help identify synthetic content, their limitations often lead to user mistrust and confusion between real and fake content. This study examines the role of AI performance in influencing human trust and decisio…

Dorothea Baur reflecting on #AI tech bros going all in on even your most personal data. Claiming to help you solve a problem which they helped create in the beginning:
"A breach of trust enabled by AI now becomes the justification for surveillance-based trust systems. And the very people who helped break the system are offering to fix it – in exchange for your iris. That’s not a safety fe…

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Haoxiang Luo, Yinqiu Liu, Ruichen Zhang, Jiacheng Wang, Gang Sun, Dusit Niyato, Hongfang Yu, Zehui Xiong, Xianbin Wang, Xuemin Shen
https://arxiv.org/abs/2507.00672

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Edge computing enables real-time data processing closer to its source, thus improving the latency and performance of edge-enabled AI applications. However, traditional AI models often fall short when dealing with complex, dynamic tasks that require advanced reasoning and multimodal data processing. This survey explores the integration of multi-LLMs (Large Language Models) to address this in edge computing, where multiple specialized LLMs collaborate to enhance task performance and adaptability …

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Charlotte Jennings (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Andrew Broad (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Lucy Godson (National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Emily…

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images (WSIs) and high-throughput omics to predict overall survival. Searches of EMBASE, PubMed, and Cochrane CENTRAL (12/08/2024), plus citation screening, identified eligible studies. Data extraction used CHARMS; bias was assessed with PROBAST+AI; synthesis followed SWiM and PRISMA 2020. Protocol: PROSPERO (CRD42024594745)…

Scale AI emphasizes that it remains an independent company and says Meta will not have access to Scale's internal systems or customers' confidential information (Scale AI)
https://scale.com/blog/customer-trust-scale-meta-deal

Scale Remains Independent After Meta Investment | Scale
Customer trust and data security remain Scale’s top priorities.

Most viewed from what? People who rarely log in? People who rarely post? People named Adrian?
Sorry, LinkedIn, not buying into your bullshit to squeeze yet more personal data out of me.
Maybe do something to boost trust?

Even de naam vsn een van de personages van Paulus de boskabouter opzoeken.
personages Paulus de boskabouter – Qwant
https://www.qwant.com/
@…
/2

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2

At Berlin Buzzwords 2025, Viola Rädle & Raphael Franke presented gamma_flow, an open-source Python package for real-time spectral data analysis.
Watch the full session: https://youtu.be/cDCtdStMWuc?si=FyrOSJ8UIjmeQfLl
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Fully Decentralised Consensus for Extreme-scale Blockchain
Siamak Abdi, Giuseppe Di Fatta, Atta Badii, Giancarlo Fortino
https://arxiv.org/abs/2508.02595 https://

Fully Decentralised Consensus for Extreme-scale Blockchain
Blockchain is a decentralised, immutable ledger technology that has been widely adopted in many sectors for various applications such as cryptocurrencies, smart contracts and supply chain management. Distributed consensus is a fundamental component of blockchain, which is required to ensure trust, security, and integrity of the data stored and the transactions processed in the blockchain. Various consensus algorithms have been developed, each affected from certain issues such as node failures, …

Trusted Data Fusion, Multi-Agent Autonomy, Autonomous Vehicles
R. Spencer Hallyburton, Miroslav Pajic
https://arxiv.org/abs/2507.17875 https://arxiv.org/pd…

Trusted Data Fusion, Multi-Agent Autonomy, Autonomous Vehicles
Multi-agent collaboration enhances situational awareness in intelligence, surveillance, and reconnaissance (ISR) missions. Ad hoc networks of unmanned aerial vehicles (UAVs) allow for real-time data sharing, but they face security challenges due to their decentralized nature, making them vulnerable to cyber-physical attacks. This paper introduces a trust-based framework for assured sensor fusion in distributed multi-agent networks, utilizing a hidden Markov model (HMM)-based approach to estimat…

Oh no it happened - client for a research project I’m working on got upset that we’re doing manual data analysis of survey responses, and complained about why we are so slow when their internal team working on a different report got “everything done in a couple of days with #AI tools”
And then they told us that waiting for proper human analysis is a “waste of time” and that we need to just chuck our dataset into AI and “get it over with”
I really don’t know what to do right now 🥲
Trying to do this properly on their expected timeline will mean very little sleep for multiple days, but giving up on the project quality and dumping it into AI is will make this entire project a waste of time. (As I wouldn’t be able to trust the output of the analysis, or be proud of it to showcase the final report as an example of our work, and not to mention that I don’t want to support this expectation to rush everything at work with these AI models)

A Global South Strategy for Evaluating Research Value with ChatGPT
Robin Nunkoo, Mike Thelwall
https://arxiv.org/abs/2508.01882 https://arxiv.org/pdf/2508.…

A Global South Strategy for Evaluating Research Value with ChatGPT
Research evaluation is important for appointments, promotions, departmental assessments, and national science strategy monitoring. Whilst Global North universities often have sufficient senior researchers for effective peer review and enough trust in citation data to use it for supporting indicators, the same is less likely to be true in the Global South. Moreover, Global South research priorities may not align well with citation-based indicators. This article introduces a ChatGPT-based strateg…

#Microsoft outsourced administration of classified #DoD data to cheap workers in #China. 🇨🇳 🕵️
My latest update on

The Synthetic Mirror -- Synthetic Data at the Age of Agentic AI
Marcelle Momha
https://arxiv.org/abs/2506.13818 https://arxiv.org/pdf…

The Synthetic Mirror -- Synthetic Data at the Age of Agentic AI
Synthetic data, which is artificially generated and intelligently mimicking or supplementing the real-world data, is increasingly used. The proliferation of AI agents and the adoption of synthetic data create a synthetic mirror that conceptualizes a representation and potential distortion of reality, thus generating trust and accountability deficits. This paper explores the implications for privacy and policymaking stemming from synthetic data generation, and the urgent need for new policy inst…

Folks are getting a wee bit more concerned about their privacy now that Donald Trump is in charge of the US.
You may have noticed that he and his regime love getting their hands on other people's data.
Privacy isn't the only issue. -- Can you trust Microsoft to deliver on its service promises under American political pressure?
Ask the EU-based International Criminal Court (ICC) which after it issued arrest warrants for Israeli Prime Minister Benjamin Netanyahu fo…

The year of the European Union Linux desktop may finally arrive
Opinion: True digital sovereignty begins at the desktop

TRUCE-AV: A Multimodal dataset for Trust and Comfort Estimation in Autonomous Vehicles
Aditi Bhalla, Christian Hellert, Enkelejda Kasneci, Nastassja Becker
https://arxiv.org/abs/2508.17880

TRUCE-AV: A Multimodal dataset for Trust and Comfort Estimation in Autonomous Vehicles
Understanding and estimating driver trust and comfort are essential for the safety and widespread acceptance of autonomous vehicles. Existing works analyze user trust and comfort separately, with limited real-time assessment and insufficient multimodal data. This paper introduces a novel multimodal dataset called TRUCE-AV, focusing on trust and comfort estimation in autonomous vehicles. The dataset collects real-time trust votes and continuous comfort ratings of 31 participants during a simulat…

Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust
Amogh Mannekote, Adam Davies, Guohao Li, Kristy Elizabeth Boyer, ChengXiang Zhai, Bonnie J Dorr, Francesco Pinto
https://arxiv.org/abs/2507.02197

Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust
As LLMs are increasingly studied as role-playing agents to generate synthetic data for human behavioral research, ensuring that their outputs remain coherent with their assigned roles has become a critical concern. In this paper, we investigate how consistently LLM-based role-playing agents' stated beliefs about the behavior of the people they are asked to role-play ("what they say") correspond to their actual behavior during role-play ("how they act"). Specifically, we establish an evaluation …

News Sentiment Embeddings for Stock Price Forecasting
Ayaan Qayyum
https://arxiv.org/abs/2507.01970 https://arxiv.org/pdf/2507.01970

News Sentiment Embeddings for Stock Price Forecasting
This paper will discuss how headline data can be used to predict stock prices. The stock price in question is the SPDR S&P 500 ETF Trust, also known as SPY that tracks the performance of the largest 500 publicly traded corporations in the United States. A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict the movement of stock prices on a daily timescale with OpenAI-based text embedding models used to create vector encodings of each headline with principal componen…

Weighted Levenberg-Marquardt methods for fitting multichannel nuclear cross section data
M. Imbri\v{s}ak, A. E. Lovell, M. R. Mumpower
https://arxiv.org/abs/2508.19468 https://

Weighted Levenberg-Marquardt methods for fitting multichannel nuclear cross section data
We present an extension of the Levenberg-Marquardt algorithm for fitting multichannel nuclear cross section data. Our approach offers a practical and robust alternative to conventional trust-region methods for analyzing experimental data. The CoH$_3$ code, based on the Hauser-Feshbach statistical model, involves a large number of interdependent parameters, making optimization challenging due to the presence of "sloppy" directions in parameter space. To address the uneven distribution of experim…

Hey, I’m not a developer but I totally vibe-coded this game you have to install on your system and which I cannot confirm has no security holes and won’t exfiltrate your data because I’m still not a developer but trust me and install it ideally with root / admin privileges!

An Efficient Recommendation Filtering-based Trust Model for Securing Internet of Things
Muhammad Ibn Ziauddin, Rownak Rahad Rabbi, SM Mehrab, Fardin Faiyaz, Mosarrat Jahan
https://arxiv.org/abs/2508.17304

An Efficient Recommendation Filtering-based Trust Model for Securing Internet of Things
Trust computation is crucial for ensuring the security of the Internet of Things (IoT). However, current trust-based mechanisms for IoT have limitations that impact data security. Sliding window-based trust schemes cannot ensure reliable trust computation due to their inability to select appropriate window lengths. Besides, recent trust scores are emphasized when considering the effect of time on trust. This can cause a sudden change in overall trust score based on recent behavior, potentially …

At the CTIF workshop in Brussels on copyright infrastructure, a cooperative project of the Finnish and the Estonian governments. I will talk about licensing linked data for trust in media

Data warehouses, lakes, lakehouses, and more – the choices we make profoundly impact operational costs and development speed. At Berlin Buzzwords, Lars Albertsson dived into how common operational challenges like deployment, failure handling, and data quality are affected by different data processing paradigms.
Watch the full session: https://youtu.be/uev_27z3-1s?si=tXTIQ8aiZeCp_Xa0
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Unveiling the Variability and Chemical Composition of AL Col
Surath C. Ghosh, Santosh Joshi, Samrat Ghosh, Athul Dileep, Otto Trust, Mrinmoy Sarkar, Jaime Andr\'es Rosales Guzm\'an, Nicol\'as Esteban Castro-Toledo, Oleg Malkov, Harinder P. Singh, Kefeng Tan, Sarabjeet S. Bedi
https://arxiv.org/abs/2508.20681

Unveiling the Variability and Chemical Composition of AL Col
Using \tess\ short-cadence (120\,s) SAP flux, we identified a rotational frequency of 0.09655\,$\mathrm{d}^{-1}$ ($P_\mathrm{rot}=10.35733$\,d). Wavelet analysis reveals that while the amplitudes of the harmonic components vary over time, the strength of the primary rotational frequency remains stable. A SED analysis of multi-band photometric data yields an effective temperature ($T_\mathrm{eff}$) of {11,750\,K.} %MDPI: Comma added for five digits in the whole text, please check. High-resolut…

Measuring the Unmeasurable? Systematic Evidence on Scale Transformations in Subjective Survey Data
Caspar Kaiser, Anthony Lepinteur
https://arxiv.org/abs/2507.16440 https://

Measuring the Unmeasurable? Systematic Evidence on Scale Transformations in Subjective Survey Data
Economists routinely use survey measures of, for example, risk preferences, trust, political attitudes, or wellbeing. The literature generally treats numerical response categories as if they represent equal psychological intervals. We provide the first systematic test of this assumption, developing a general framework to quantify how easily results can be overturned when this linearity assumption is relaxed. Using original experimental data, we show that respondents interpret survey scales in w…

Had ik jullie al verteld dat Quant best regelmatig met weinig resultaten al gelijk uitstekende resultaten voorschotelt?
Qwant - De [Franse] zoekmachine met respect voor uw privacy
#Quant

NOSTRA: A noise-resilient and sparse data framework for trust region based multi objective Bayesian optimization
Maryam Ghasemzadeh, Anton van Beek
https://arxiv.org/abs/2508.16476

NOSTRA: A noise-resilient and sparse data framework for trust region based multi objective Bayesian optimization
Multi-objective Bayesian optimization (MOBO) struggles with sparse (non-space-filling), scarce (limited observations) datasets affected by experimental uncertainty, where identical inputs can yield varying outputs. These challenges are common in physical and simulation experiments (e.g., randomized medical trials and, molecular dynamics simulations) and are therefore incompatible with conventional MOBO methods. As a result, experimental resources are inefficiently allocated, leading to suboptim…

Robust Training with Data Augmentation for Medical Imaging Classification
Josu\'e Mart\'inez-Mart\'inez, Olivia Brown, Mostafa Karami, Sheida Nabavi
https://arxiv.org/abs/2506.17133

Robust Training with Data Augmentation for Medical Imaging Classification
Deep neural networks are increasingly being used to detect and diagnose medical conditions using medical imaging. Despite their utility, these models are highly vulnerable to adversarial attacks and distribution shifts, which can affect diagnostic reliability and undermine trust among healthcare professionals. In this study, we propose a robust training algorithm with data augmentation (RTDA) to mitigate these vulnerabilities in medical image classification. We benchmark classifier robustness a…

Rethinking Broken Object Level Authorization Attacks Under Zero Trust Principle
Anbin Wu (The College of Intelligence and Computing, Tianjin University), Zhiyong Feng (The College of Intelligence and Computing, Tianjin University), Ruitao Feng (The Southern Cross University)
https://arxiv.org/abs/2507.02309

Rethinking Broken Object Level Authorization Attacks Under Zero Trust Principle
RESTful APIs facilitate data exchange between applications, but they also expose sensitive resources to potential exploitation. Broken Object Level Authorization (BOLA) is the top vulnerability in the OWASP API Security Top 10, exemplifies a critical access control flaw where attackers manipulate API parameters to gain unauthorized access. To address this, we propose BOLAZ, a defense framework grounded in zero trust principles. BOLAZ analyzes the data flow of resource IDs, pinpointing BOLA atta…

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps
Timoteo Kelly, Abdulkadir Korkmaz, Samuel Mallet, Connor Souders, Sadra Aliakbarpour, Praveen Rao
https://arxiv.org/abs/2506.19268

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps
We present HARPT, a large-scale annotated corpus of mobile health app store reviews aimed at advancing research in user privacy and trust. The dataset comprises over 480,000 user reviews labeled into seven categories that capture critical aspects of trust in applications, trust in providers and privacy concerns. Creating HARPT required addressing multiple complexities, such as defining a nuanced label schema, isolating relevant content from large volumes of noisy data, and designing an annotati…

Biases in LLM-Generated Musical Taste Profiles for Recommendation
Bruno Sguerra, Elena V. Epure, Harin Lee, Manuel Moussallam
https://arxiv.org/abs/2507.16708

Biases in LLM-Generated Musical Taste Profiles for Recommendation
One particularly promising use case of Large Language Models (LLMs) for recommendation is the automatic generation of Natural Language (NL) user taste profiles from consumption data. These profiles offer interpretable and editable alternatives to opaque collaborative filtering representations, enabling greater transparency and user control. However, it remains unclear whether users consider these profiles to be an accurate representation of their taste, which is crucial for trust and usability.…

Breaking the Black Box: Inherently Interpretable Physics-Informed Machine Learning for Imbalanced Seismic Data
Vemula Sreenath, Filippo Gatti, Pierre Jehel
https://arxiv.org/abs/2508.19031

Breaking the Black Box: Inherently Interpretable Physics-Informed Machine Learning for Imbalanced Seismic Data
Ground motion models (GMMs) predict how strongly the ground will shake during an earthquake. They are essential for structural analysis, seismic design, and seismic risk assessment studies. Traditional machine learning (ML) approaches are popular to develop GMMs, due to large earthquake databases worldwide. However, they operate as "black boxes," which are hard to interpret and trust, limiting their use in high-stake decisions. Additionally, these databases suffer from significant data imbalanc…

Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Youze Wang, Zijun Chen, Ruoyu Chen, Shishen Gu, Yinpeng Dong, Hang Su, Jun Zhu, Meng Wang, Richang Hong, Wenbo Hu
https://arxiv.org/abs/2506.12336

Understanding and Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Recent advancements in multimodal large language models for video understanding (videoLLMs) have improved their ability to process dynamic multimodal data. However, trustworthiness challenges factual inaccuracies, harmful content, biases, hallucinations, and privacy risks, undermine reliability due to video data's spatiotemporal complexities. This study introduces Trust-videoLLMs, a comprehensive benchmark evaluating videoLLMs across five dimensions: truthfulness, safety, robustness, fairness, …

Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Yuanfang Ren, Esra Adiyeke, Ziyuan Guan, Zhenhong Hu, Mackenzie J Meni, Benjamin Shickel, Parisa Rashidi, Tezcan Ozrazgat-Baslanti, Azra Bihorac
https://arxiv.org/abs…

Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Despite advances in surgical techniques and care, postoperative complications are prevalent and effects up to 15% of the patients who underwent a major surgery. The objective of this study is to develop and validate models for predicting postoperative complications and death after major surgery on a large and multicenter dataset, following the previously validated MySurgeryRisk algorithm. This retrospective, longitudinal and multicenter cohort analysis included 508,097 encounters from 366,875 a…

LLMs are now part of our daily work, making coding easier. At Berlin Buzzwords 2025, Ivan Dolgov discussed how they built an in-house LLM for AI code completion in JetBrains products, covering design choices, data preparation, training and model evaluation.
Watch the full session: https://youtu.be/yLHCwi_mgvQ?si=CpW8bGd9jWHq43Ez
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Asteroseismology of HD 23734, HD 68703, and HD 73345 using K2-TESS Space-based Photometry and High-resolution Spectroscopy
Santosh Joshi, Athul Dileep, Eugene Semenko, Mrinmoy Sarkar, Otto Trust, Peter De Cat, Patricia Lampens, Marc-Antoine Dupret, Surath C. Ghosh, David Mkrtichian, Mathijs Vanrespaille, Sugyan Parida, Abhay Pratap Yadav, Pramod Kumar S., P. P. Goswami, Muhammed Riyas, Drisya Karinkuzhi

Asteroseismology of HD 23734, HD 68703, and HD 73345 using K2-TESS Space-based Photometry and High-resolution Spectroscopy
In this paper, we present a comprehensive study of three stars, HD 23734, HD 68703, and HD 73345, which were previously observed as chemically peculiar candidates within the Nainital-Cape survey and reported as null results for the pulsational variability. Frequency analyses of \ktwo\ and \tess\ time-series photometric data reveal the co-existence of rotational modulation and pulsation. We use the spectrum synthesis technique to determine fundamental parameters and chemical composition, which s…

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation
David Egea, Barproda Halder, Sanghamitra Dutta
https://arxiv.org/abs/2508.18933

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation
Automated detection of vulnerabilities in source code is an essential cybersecurity challenge, underpinning trust in digital systems and services. Graph Neural Networks (GNNs) have emerged as a promising approach as they can learn structural and logical code relationships in a data-driven manner. However, their performance is severely constrained by training data imbalances and label noise. GNNs often learn 'spurious' correlations from superficial code similarities, producing detectors that fai…

Implementing Zero Trust Architecture to Enhance Security and Resilience in the Pharmaceutical Supply Chain
Saeid Ghasemshirazi, Ghazaleh Shirvani, Marziye Ranjbar Tavakoli, Bahar Ghaedi, Mohammad Amin Langarizadeh
https://arxiv.org/abs/2508.15776

Implementing Zero Trust Architecture to Enhance Security and Resilience in the Pharmaceutical Supply Chain
The pharmaceutical supply chain faces escalating cybersecurity challenges threatening patient safety and operational continuity. This paper examines the transformative potential of zero trust architecture for enhancing security and resilience within this critical ecosystem. We explore the challenges posed by data breaches, counterfeiting, and disruptions and introduce the principles of continuous verification, least-privilege access, and data-centric security inherent in zero trust. Real-world …

Towards an Approach for Evaluating the Impact of AI Standards
Julia Lane
https://arxiv.org/abs/2506.13839 https://arxiv.org/pdf/2506.…

Towards an Approach for Evaluating the Impact of AI Standards
There have been multiple calls for investments in the development of AI standards that both preserve the transformative potential and minimize the risks of AI. The goals of AI standards, particularly with respect to AI data, performance, and governance, are to promote innovation and public trust in systems that use AI. However, there is a lack of a formal or shared method to measure the impact of these standardization activities on the goals of innovation and trust. This concept paper proposes …

Data-driven Trust Bootstrapping for Mobile Edge Computing-based Industrial IoT Services
Prabath Abeysekara, Hai Dong
https://arxiv.org/abs/2508.12560 https://

Data-driven Trust Bootstrapping for Mobile Edge Computing-based Industrial IoT Services
We propose a data-driven and context-aware approach to bootstrap trustworthiness of homogeneous Internet of Things (IoT) services in Mobile Edge Computing (MEC) based industrial IoT (IIoT) systems. The proposed approach addresses key limitations in adapting existing trust bootstrapping approaches into MEC-based IIoT systems. These key limitations include, the lack of opportunity for a service consumer to interact with a lesser-known service over a prolonged period of time to get a robust measur…

Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments
Christoph Schnabl, Daniel Hugenroth, Bill Marino, Alastair R. Beresford
https://arxiv.org/abs/2506.23706

Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments
Benchmarks are important measures to evaluate safety and compliance of AI models at scale. However, they typically do not offer verifiable results and lack confidentiality for model IP and benchmark datasets. We propose Attestable Audits, which run inside Trusted Execution Environments and enable users to verify interaction with a compliant AI model. Our work protects sensitive data even when model provider and auditor do not trust each other. This addresses verification challenges raised in re…

"Blockchain-Enabled Zero Trust Framework for Securing FinTech Ecosystems Against Insider Threats and Cyber Attacks"
Avinash Singh, Vikas Pareek, Asish Sharma
https://arxiv.org/abs/2507.19976 …

"Blockchain-Enabled Zero Trust Framework for Securing FinTech Ecosystems Against Insider Threats and Cyber Attacks"
Fintech provides technological services to increase operational efficiency in financial institutions, but traditional perimeter-based defense mechanisms are insufficient against evolving cyber threats like insider attacks, malware intrusions, and Advanced Persistent Threats (APTs). These vulnerabilities expose Fintech organizations to significant risks, including financial losses and data breaches. To address these challenges, this paper proposes a blockchain-integrated Zero Trust framework, ad…

Characterizing the Dynamics of Conspiracy Related German Telegram Conversations during COVID-19
Elisabeth H\"oldrich, Mathias Angermaier, Jana Lasser, Joao Pinheiro-Neto
https://arxiv.org/abs/2507.13398

Characterizing the Dynamics of Conspiracy Related German Telegram Conversations during COVID-19
Conspiracy theories have long drawn public attention, but their explosive growth on platforms like Telegram during the COVID-19 pandemic raises pressing questions about their impact on societal trust, democracy, and public health. We provide a geographical, temporal and network analysis of the structure of of conspiracy-related German-language Telegram chats in a novel large-scale data set. We examine how information flows between regional user groups and influential broadcasting channels, reve…

Trivial Trojans: How Minimal MCP Servers Enable Cross-Tool Exfiltration of Sensitive Data
Nicola Croce, Tobin South
https://arxiv.org/abs/2507.19880 https://

Trivial Trojans: How Minimal MCP Servers Enable Cross-Tool Exfiltration of Sensitive Data
The Model Context Protocol (MCP) represents a significant advancement in AI-tool integration, enabling seamless communication between AI agents and external services. However, this connectivity introduces novel attack vectors that remain largely unexplored. This paper demonstrates how unsophisticated threat actors, requiring only basic programming skills and free web tools, can exploit MCP's trust model to exfiltrate sensitive financial data. We present a proof-of-concept attack where a malicio…

Social Media Can Reduce Misinformation When Public Scrutiny is High
Gavin Wang, Haofei Qin, Xiao Tang, Lynn Wu
https://arxiv.org/abs/2506.16355 https://

Social Media Can Reduce Misinformation When Public Scrutiny is High
Misinformation poses a growing global threat to institutional trust, democratic stability, and public decision-making. While prior research has often portrayed social media as a channel for spreading falsehoods, less is known about the conditions under which it may instead constrain misinformation by enhancing transparency and accountability. Here we show this dual potential in the context of local governments' GDP reporting in China, where data falsifications are widespread. Analyzing official…

Patient-Centred Explainability in IVF Outcome Prediction
Adarsa Sivaprasad, Ehud Reiter, David McLernon, Nava Tintarev, Siladitya Bhattacharya, Nir Oren
https://arxiv.org/abs/2506.18760

Patient-Centred Explainability in IVF Outcome Prediction
This paper evaluates the user interface of an in vitro fertility (IVF) outcome prediction tool, focussing on its understandability for patients or potential patients. We analyse four years of anonymous patient feedback, followed by a user survey and interviews to quantify trust and understandability. Results highlight a lay user's need for prediction model \emph{explainability} beyond the model feature space. We identify user concerns about data shifts and model exclusions that impact trust. Th…

Public Perceptions of Autonomous Vehicles: A Survey of Pedestrians and Cyclists in Pittsburgh
Rudra Y. Bedekar
https://arxiv.org/abs/2506.17513 https://

Public Perceptions of Autonomous Vehicles: A Survey of Pedestrians and Cyclists in Pittsburgh
This study investigates how autonomous vehicle(AV) technology is perceived by pedestrians and bicyclists in Pittsburgh. Using survey data from over 1200 respondents, the research explores the interplay between demographics, AV interactions, infrastructural readiness, safety perceptions, and trust. Findings highlight demographic divides, infrastructure gaps, and the crucial role of communication and education in AV adoption.

NVIDIA GPU Confidential Computing Demystified
Zhongshu Gu, Enriquillo Valdez, Salman Ahmed, Julian James Stephen, Michael Le, Hani Jamjoom, Shixuan Zhao, Zhiqiang Lin
https://arxiv.org/abs/2507.02770

NVIDIA GPU Confidential Computing Demystified
GPU Confidential Computing (GPU-CC) was introduced as part of the NVIDIA Hopper Architecture, extending the trust boundary beyond traditional CPU-based confidential computing. This innovation enables GPUs to securely process AI workloads, providing a robust and efficient solution for handling sensitive data. For end users, transitioning to GPU-CC mode is seamless, requiring no modifications to existing AI applications. However, this ease of adoption contrasts sharply with the complexity of the …

Mining Voter Behaviour and Confidence: A Rule-Based Analysis of the 2022 U.S. Elections
Md Al Jubair, Mohammad Shamsul Arefin, Ahmed Wasif Reza
https://arxiv.org/abs/2507.14236

Mining Voter Behaviour and Confidence: A Rule-Based Analysis of the 2022 U.S. Elections
This study explores the relationship between voter trust and their experiences during elections by applying a rule-based data mining technique to the 2022 Survey of the Performance of American Elections (SPAE). Using the Apriori algorithm and setting parameters to capture meaningful associations (support >= 3%, confidence >= 60%, and lift > 1.5), the analysis revealed a strong connection between demographic attributes and voting-related challenges, such as registration hurdles, accessibility is…

The Agent Behavior: Model, Governance and Challenges in the AI Digital Age
Qiang Zhang, Pei Yan, Yijia Xu, Chuanpo Fu, Yong Fang, Yang Liu
https://arxiv.org/abs/2508.14415 https…

The Agent Behavior: Model, Governance and Challenges in the AI Digital Age
Advancements in AI have led to agents in networked environments increasingly mirroring human behavior, thereby blurring the boundary between artificial and human actors in specific contexts. This shift brings about significant challenges in trust, responsibility, ethics, security and etc. The difficulty in supervising of agent behaviors may lead to issues such as data contamination and unclear accountability. To address these challenges, this paper proposes the "Network Behavior Lifecycle" mode…

Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices
Rania Islmabouli, Marlene Brunner, Devender Kumar, Mahdi Sareban, Gunnar Treff, Michael Neudorfer, Josef Niebauer, Arne Bathke, Jan David Smeddinck
https://arxiv.org/abs/2508.19818

Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices
Wearable devices with photoplethysmography (PPG) sensors are widely used to monitor heart rate (HR), yet often suffer from accuracy issues. However, users typically do not receive an indication of potential measurement errors. We present a real-time warning system that detects and communicates inaccuracies in PPG-derived HR, aiming to enhance transparency and trust. Using data from Polar and Garmin devices, we trained a deep learning model to classify HR accuracy using only the derived HR signa…

Beneath the Mask: Can Contribution Data Unveil Malicious Personas in Open-Source Projects?
Ruby Nealon
https://arxiv.org/abs/2508.13453 https://arxiv.org/p…

Beneath the Mask: Can Contribution Data Unveil Malicious Personas in Open-Source Projects?
In February 2024, after building trust over two years with project maintainers by making a significant volume of legitimate contributions, GitHub user "JiaT75" self-merged a version of the XZ Utils project containing a highly sophisticated, well-disguised backdoor targeting sshd processes running on systems with the backdoored package installed. A month later, this package began to be distributed with popular Linux distributions until a Microsoft employee discovered the backdoor while investiga…

Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana
Hari Krishna Neupane, Bhupesh Kumar Mishra
https://arxiv.org/abs/2508.12389

Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana
The integration of Artificial Intelligence (AI) into healthcare systems in low-resource settings, such as Nepal and Ghana, presents transformative opportunities to improve personalized patient care, optimize resources, and address medical professional shortages. This paper presents a survey-based evaluation and insights from Nepal and Ghana, highlighting major obstacles such as data privacy, reliability, and trust issues. Quantitative and qualitative field studies reveal critical metrics, inclu…

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
Muhammad Sharshar, Ahmad Mohammad Saber, Davor Svetinovic, Amr M. Youssef, Deepa Kundur, Ehab F. El-Saadany
https://arxiv.org/abs/2507.22239

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
The increasing digitization of smart grids has improved operational efficiency but also introduced new cybersecurity vulnerabilities, such as False Data Injection Attacks (FDIAs) targeting Automatic Generation Control (AGC) systems. While machine learning (ML) and deep learning (DL) models have shown promise in detecting such attacks, their opaque decision-making limits operator trust and real-world applicability. This paper proposes a hybrid framework that integrates lightweight ML-based attac…

Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox
Malikussaid, Sutiyo
https://arxiv.org/abs/2506.20102 h…

Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox
The convergence of IT and OT has created hyper-connected ICS, exposing critical infrastructure to a new class of adaptive, intelligent adversaries that render static defenses obsolete. Existing security paradigms often fail to address a foundational "Trinity of Trust," comprising the fidelity of the system model, the integrity of synchronizing data, and the resilience of the analytical engine against sophisticated evasion. This paper introduces the ARC framework, a method for achieving analytic…