Tootfinder

XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
Raju Ningappa Mulawade, Christoph Garth, Alexander Wiebel
https://arxiv.org/abs/2507.22020 https://…

XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
We propose a novel segmentation-based explainable artificial intelligence (XAI) method for neural networks working on point cloud classification. As one building block of this method, we propose a novel point-shifting mechanism to introduce perturbations in point cloud data. Recently, AI has seen an exponential growth. Hence, it is important to understand the decision-making process of AI algorithms when they are applied in critical areas. Our work focuses on explaining AI algorithms that class…

This https://arxiv.org/abs/2406.12007 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor
Here we present the results of benchmarking a quantum processor based on trapped $^{171}$Yb$^{+}$ ions by performing basic quantum machine learning algorithms. Using a quantum-enhanced support vector machine algorithm with up to five qubits we perform a supervised binary classification on two types of datasets: small binary digit images and weighted graphs with a ring topology. For the first dataset, images are intentionally selected so that they could be classified with 100% accuracy. This all…

The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals
Margarita Loupa, Antonios Argyriou, Yanwei Liu
https://arxiv.org/abs/2507.20657 htt…

The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals
A subset of Human Activity Classification (HAC) systems are based on AI algorithms that use passively collected wireless signals. This paper presents the micro-Doppler attack targeting HAC from wireless orthogonal frequency division multiplexing (OFDM) signals. The attack is executed by inserting artificial variations in a transmitted OFDM waveform to alter its micro-Doppler signature when it reflects off a human target. We investigate two variants of our scheme that manipulate the waveform at …

Trustworthy Artificial Intelligence for Cyber Threat Analysis
Shuangbao Paul Wang, Paul Mullin
https://arxiv.org/abs/2506.19052 https://

Trustworthy Artificial Intelligence for Cyber Threat Analysis
Artificial Intelligence brings innovations into the society. However, bias and unethical exist in many algorithms that make the applications less trustworthy. Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. In …

Adversarial attacks to image classification systems using evolutionary algorithms
Sergio Nesmachnow, Jamal Toutouh
https://arxiv.org/abs/2507.13136 https:/…

Adversarial attacks to image classification systems using evolutionary algorithms
Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models based on artificial intelligence. This article explores an approach to generate adversarial attacks against image classifiers using a combination of evolutionary algorithms and generative adversarial networks. The proposed approach explores the latent space of a generative adversarial network with an evolutionary algo…

This https://arxiv.org/abs/2502.07359 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Towards energy-insensitive and robust neutron/gamma classification: A learning-based frequency-domain parametric approach
Neutron/gamma discrimination has been intensively researched in recent years, due to its unique scientific value and widespread applications. With the advancement of detection materials and algorithms, nowadays we can achieve fairly good discrimination. However, further improvements rely on better utilization of detector raw signals, especially energy-independent pulse characteristics. We begin by discussing why figure-of-merit (FoM) is not a comprehensive criterion for high-precision neutron/g…

Ranking-based Fusion Algorithms for Extreme Multi-label Text Classification (XMTC)
Celso Fran\c{c}a, Gestefane Rabbi, Thiago Salles, Washington Cunha, Leonardo Rocha, Marcos Andr\'e Gon\c{c}alves
https://arxiv.org/abs/2507.03761

Ranking-based Fusion Algorithms for Extreme Multi-label Text Classification (XMTC)
In the context of Extreme Multi-label Text Classification (XMTC), where labels are assigned to text instances from a large label space, the long-tail distribution of labels presents a significant challenge. Labels can be broadly categorized into frequent, high-coverage \textbf{head labels} and infrequent, low-coverage \textbf{tail labels}, complicating the task of balancing effectiveness across all labels. To address this, combining predictions from multiple retrieval methods, such as sparse re…

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SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Sarah Pungitore, Shashank Yadav, Molly Douglas, Jarrod Mosier, Vignesh Subbian
https://arxiv.org/abs/2506.16359

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping
Objective: Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review, limited automation, and difficulties in adapting algorithms across sources. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts. To suppor…

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
Azhar Ikhtiarudin, Aditi Das, Param Thakkar, Akash Kundu
https://arxiv.org/abs/2507.12189

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
We introduce BenchRL-QAS, a unified benchmarking framework for systematically evaluating reinforcement learning (RL) algorithms in quantum architecture search (QAS) across diverse variational quantum algorithm tasks and system sizes ranging from 2- to 8-qubit. Our study benchmarks nine RL agents including both value-based and policy-gradient methods on representative quantum problems such as variational quantum eigensolver, variational quantum state diagonalization, quantum classification, and …

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
Md Min-Ha-Zul Abedin, Tazqia Mehrub
https://arxiv.org/abs/2507.16952

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable (PE) files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra Trees, HistGradientBoosting, k-Nearest Neighbors (KNN), and TabNet, under three preprocessing settings: original feature space, Principal Component Analysis (PCA), and Linear Discriminant Analysis (L…

Multipass Linear Sketches for Geometric LP-Type Problems
N. Efe \c{C}ekirge, William Gay, David P. Woodruff
https://arxiv.org/abs/2507.11484 https://

Multipass Linear Sketches for Geometric LP-Type Problems
LP-type problems such as the Minimum Enclosing Ball (MEB), Linear Support Vector Machine (SVM), Linear Programming (LP), and Semidefinite Programming (SDP) are fundamental combinatorial optimization problems, with many important applications in machine learning applications such as classification, bioinformatics, and noisy learning. We study LP-type problems in several streaming and distributed big data models, giving $\varepsilon$-approximation linear sketching algorithms with a focus on the h…

A Robust Approach to Detect Intersections between Triangles with Different Numerical Representations
Luca Garau, Gianmarco Cherchi
https://arxiv.org/abs/2507.08478

A Robust Approach to Detect Intersections between Triangles with Different Numerical Representations
The detection and classification of intersections between triangles are crucial tasks in a wide range of applications within Computer Graphics and Geometry Processing, including mesh Arrangements, mesh Booleans, and generic mesh processing and fixing tasks. Existing methods are hard-coded and deeply integrated into specific algorithms, and significant efforts are usually required to integrate them into new pipelines or to extend them to different numerical representations. This paper presents a…

Machine learning approaches for automatic cleaning of investigative drilling data
Fei Huang (Flinders University), Hongyu Qin (Flinders University), Masoud Manafi (Civil Group), Ben Juett (Civil Group), Ben Evans (Civil Group)
https://arxiv.org/abs/2506.14289

Machine learning approaches for automatic cleaning of investigative drilling data
Investigative drilling (ID) is an innovative measurement while drilling (MWD) technique that has been implemented in various site investigation projects across Australia. While the automated drilling feature of ID substantially reduces noise within drilling data streams, data cleaning remains essential for removing anomalies to enable accurate strata classification and prediction of soil and rock properties. This study employed three machine learning algorithms--IsoForest, one-class SVM, and DB…

Runtime Analysis of Evolutionary NAS for Multiclass Classification
Zeqiong Lv, Chao Qian, Yun Liu, Jiahao Fan, Yanan Sun
https://arxiv.org/abs/2506.06019 h…

Runtime Analysis of Evolutionary NAS for Multiclass Classification
Evolutionary neural architecture search (ENAS) is a key part of evolutionary machine learning, which commonly utilizes evolutionary algorithms (EAs) to automatically design high-performing deep neural architectures. During past years, various ENAS methods have been proposed with exceptional performance. However, the theory research of ENAS is still in the infant. In this work, we step for the runtime analysis, which is an essential theory aspect of EAs, of ENAS upon multiclass classification pr…

AICO: Feature Significance Tests for Supervised Learning
Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn
https://arxiv.org/abs/2506.23396 https:…

AICO: Feature Significance Tests for Supervised Learning
The opacity of many supervised learning algorithms remains a key challenge, hindering scientific discovery and limiting broader deployment -- particularly in high-stakes domains. This paper develops model- and distribution-agnostic significance tests to assess the influence of input features in any regression or classification algorithm. Our method evaluates a feature's incremental contribution to model performance by masking its values across samples. Under the null hypothesis, the distributio…

This https://arxiv.org/abs/2312.01602 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Time Series Similarity Measures and Quantum Temporal Kernels
This article presents a quantum computing approach to designing of similarity measures and kernels for classification of stochastic symbolic time series. In the area of machine learning, kernels are important components of various similarity-based classification, clustering, and regression algorithms. An effective strategy for devising problem-specific kernels is leveraging existing generative models of the example space. In this study we assume that a quantum generative model, known as quantum…

On the minimality of pancake decomposition of surface germs
Davi Lopes Medeiros, Euripedes Carvalho da Silva, Emanoel Souza
https://arxiv.org/abs/2505.23976

On the minimality of pancake decomposition of surface germs
The abnormal surfaces called snakes and circular snakes, defined in \cite{GabrielovSouza}, are special types of surface germs capturing the outer Lipschitz phenomena relevant to the outer classification problem. We provide algorithms to obtain a minimal pancake decomposition, i.e., where the number of pancakes is minimal, for snakes and circular snakes. We call a pancake decomposition obtained from our algorithm a greedy pancake decomposition. We also prove that greedy pancake decompositions of…

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…