Tootfinder

Analyzing the Code From The Terminator’s HUD
https://poliverso.org/display/0477a01e-0272215f-cf433ff2eb8c1f11
Analyzing the Code From The Terminator’s HUD The T1000, also known as the Terminator, was like some kind of non-giving up robot guy. The ro…

Google announces a threat detection capability coming to Android that uses on-device AI to spot malware, analyzing apps' use of permissions and services (Sarah Perez/TechCrunch)
https://techcrunch.com/2024/05/15/goog

Google takes aim at Android malware with an AI-powered live threat detection service | TechCrunch
Google is preparing to launch a new system to help address the problem of malware on Android. Its new live threat detection service leverages Google Play

Honestly, this is a good read for both #LLM proponents and skeptics.
It’s a Readout about how to deal with AI Openness, but also describes the various bits that go into creating and running an LLM model.
It’s only 15 pages, so it’s a quick read.

"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Seth Bernstein, Paul Denny, Juho Leinonen, Lauren Kan, Arto Hellas, Matt Littlefield Sami Sarsa, Stephen MacNeil
https://arxiv.org/abs/2403.09409

"Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models
Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a good analogy can bridge the gap between unfamiliar concepts and familiar ones, providing an engaging way to aid understanding. However, creating effective educational analogies is difficult even for experienced instructors. We investigate to what extent large language models (LLMs), specifically ChatGPT, can provide acce…

Reproducing, Extending, and Analyzing Naming Experiments
Rachel Alpern, Ido Lazer, Issar Tzachor, Hanit Hakim, Sapir Weissbuch, Dror G. Feitelson
https://arxiv.org/abs/2402.10022 …

Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits
Hiba Dakdouk, Mohamed Sana, Mattia Merluzzi
https://arxiv.org/abs/2405.09430

Using Sequential Runtime Distributions for the Parallel Speedup Prediction of SAT Local Search
Alejandro Arbelaez, Charlotte Truchet, Philippe Codognet
https://arxiv.org/abs/2403.08790

Using Sequential Runtime Distributions for the Parallel Speedup Prediction of SAT Local Search
This paper presents a detailed analysis of the scalability and parallelization of local search algorithms for the Satisfiability problem. We propose a framework to estimate the parallel performance of a given algorithm by analyzing the runtime behavior of its sequential version. Indeed, by approximating the runtime distribution of the sequential process with statistical methods, the runtime behavior of the parallel process can be predicted by a model based on order statistics. We apply this app…

Analyzing Nursing Assistant Attitudes Towards Empathic Geriatric Caregiving Using Quantitative Ethnography
Behdokht Kiafar, Salam Daher, Shayla Sharmin, Asif Ahmmed, Ladda Thiamwong, Roghayeh Leila Barmaki
https://arxiv.org/abs/2405.08948

An Extensible Framework for Architecture-Based Data Flow Analysis for Information Security
Nicolas Boltz, Sebastian Hahner, Christopher Gerking, Robert Heinrich
https://arxiv.org/abs/2403.09402

An Extensible Framework for Architecture-Based Data Flow Analysis for Information Security
The growing interconnection between software systems increases the need for security already at design time. Security-related properties like confidentiality are often analyzed based on data flow diagrams (DFDs). However, manually analyzing DFDs of large software systems is bothersome and error-prone, and adjusting an already deployed software is costly. Additionally, closed analysis ecosystems limit the reuse of modeled information and impede comprehensive statements about a system's security.…