Tootfinder

This https://arxiv.org/abs/2306.01987 has been replaced.
link: https://scholar.google.com/scholar?q=a

Prompting Is All You Need: Automated Android Bug Replay with Large Language Models
Bug reports are vital for software maintenance that allow users to inform developers of the problems encountered while using the software. As such, researchers have committed considerable resources toward automating bug replay to expedite the process of software maintenance. Nonetheless, the success of current automated approaches is largely dictated by the characteristics and quality of bug reports, as they are constrained by the limitations of manually-crafted patterns and pre-defined vocabul…

Physics Sensor Based Deep Learning Fall Detection System
Zeyuan Qu, Tiange Huang, Yuxin Ji, Yongjun Li
https://arxiv.org/abs/2403.06994 https://

Physics Sensor Based Deep Learning Fall Detection System
Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshold based classification methods. In this paper, we build a complete system named TSFallDetect including data receiving device based on embed…

This https://arxiv.org/abs/2307.12615 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Finite-sum optimization: Adaptivity to smoothness and loopless variance reduction
For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past …

Explainability Guided Adversarial Evasion Attacks on Malware Detectors
Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Moustafa Saleh
https://arxiv.org/abs/2405.01728

Explainability Guided Adversarial Evasion Attacks on Malware Detectors
As the focus on security of Artificial Intelligence (AI) is becoming paramount, research on crafting and inserting optimal adversarial perturbations has become increasingly critical. In the malware domain, this adversarial sample generation relies heavily on the accuracy and placement of crafted perturbation with the goal of evading a trained classifier. This work focuses on applying explainability techniques to enhance the adversarial evasion attack on a machine-learning-based Windows PE malwa…

🦴 Scientists try out Stone Age tools to understand how they were used
#science

Scientists try out Stone Age tools to understand how they were used
Researchers from Tokyo Metropolitan University crafted replica Stone Age tools and used them for a range of tasks to see how different activities create traces on the edge. They found that a combination of macroscopic and microscopic traces can tell us how stone edges were used. Their criteria help separate tools used for wood-felling from other activities. In addition, dated stone edges may be used to identify when timber use began for early humans.

This https://arxiv.org/abs/2403.16032 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

FineWAVE: Fine-Grained Warning Verification of Bugs for Automated Static Analysis Tools
Automated Static Analysis Tools (ASATs) have evolved over time to assist in detecting bugs. However, the excessive false warnings can impede developers' productivity and confidence in the tools. Previous research efforts have explored learning-based methods to validate the reported warnings. Nevertheless, their coarse granularity, focusing on either long-term warnings or function-level alerts, which are insensitive to individual bugs. Also, they rely on manually crafted features or solely on so…

This https://arxiv.org/abs/2404.18405 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Understanding and Shaping Human-Technology Assemblages in the Age of Generative AI
Generative AI capabilities are rapidly transforming how we perceive, interact with, and relate to machines. This one-day workshop invites HCI researchers, designers, and practitioners to imaginatively inhabit and explore the possible futures that might emerge from humans combining generative AI capabilities into everyday technologies at massive scale. Workshop participants will craft stories, visualisations, and prototypes through scenario-based design to investigate these possible futures, res…

Entropy Engineered Middle-In Synthesis of Dual Single-Atom Compounds for Nitrate Reduction Reaction
Yao Hu, Haihui Lan, Junjun He, Wenjing Fang, Wen-Da Zhang, Shuanglong Lu, Fang Duan, Mingliang Du
https://arxiv.org/abs/2404.05075

Entropy Engineered Middle-In Synthesis of Dual Single-Atom Compounds for Nitrate Reduction Reaction
Despite the immense potential of Dual Single-Atom Compounds (DSACs), the challenges in their synthesis process, including complexity, stability, purity, and scalability, remain primary concerns in current research. Here, we present a general strategy, termed "Entropy-Engineered Middle-In Synthesis of Dual Single-Atom Compounds" (EEMIS-DSAC), which is meticulously crafted to produce a diverse range of DSACs, effectively addressing the aforementioned issues. Our strategy integrates the advantages…

From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations
Kapilya Gangadharan, K. Malathi, Anoop Purandaran, Barathi Subramanian, Rathinaraja Jeyaraj
https://arxiv.org/abs/2402.08109

From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations
This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algor…

Understanding and Shaping Human-Technology Assemblages in the Age of Generative AI
Josh Andres, Chris Danta, Andrea Bianchi, Sungyeon Hong, Zhuying Li, Eduardo B. Sandoval, Charles Martin, Ned Cooper
https://arxiv.org/abs/2404.18405

Understanding and Shaping Human-Technology Assemblages in the Age of Generative AI
Generative AI capabilities are rapidly transforming how we perceive, interact with, and relate to machines. This one-day workshop invites HCI researchers, designers, and practitioners to imaginatively inhabit and explore the possible futures that might emerge from humans combining generative AI capabilities into everyday technologies at massive scale. Workshop participants will craft stories, visualisations, and prototypes through scenario-based design to investigate these possible futures, res…