Tootfinder

Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications
Jiacheng Wang, Changyuan Zhao, Zehui Xiong, Tao Xiang, Dusit Niyato, Xianbin Wang, Shiwen Mao, Dong In Kim
https://arxiv.org/abs/2507.19837

Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications
AI-enabled wireless communications have attracted tremendous research interest in recent years, particularly with the rise of novel paradigms such as low-altitude integrated sensing and communication (ISAC) networks. Within these systems, feature engineering plays a pivotal role by transforming raw wireless data into structured representations suitable for AI models. Hence, this paper offers a comprehensive investigation of feature engineering techniques in AI-driven wireless communications. Sp…

How Usable is Automated Feature Engineering for Tabular Data?
Bastian Sch\"afer, Lennart Purucker, Maciej Janowski, Frank Hutter
https://arxiv.org/abs/2508.13932 https://…

How Usable is Automated Feature Engineering for Tabular Data?
Tabular data, consisting of rows and columns, is omnipresent across various machine learning applications. Each column represents a feature, and features can be combined or transformed to create new, more informative features. Such feature engineering is essential to achieve peak performance in machine learning. Since manual feature engineering is expensive and time-consuming, a substantial effort has been put into automating it. Yet, existing automated feature engineering (AutoFE) methods have…

This week's ISE 2025 lecture was focussed on artificial neural networks. In particular, we were discussing how to get rid of manual feature engineering and doing representation learning from raw data with convolutional neural networks.
#AI #ArtificialNeuralNetworks

UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
Jonas Lau, Annie Tran
https://arxiv.org/abs/2506.15294 h…

UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
This paper discusses a popular UX research activity, feature prioritization, using the User Experience Research Point of View (UXR PoV) Playbook framework. We describe an application of multinomial logistic regression, frequently marketed as MaxDiff, for prioritizing product features in consumer product development. It addresses challenges of traditional surveying techniques. We propose a solution using MaxDiff to generate a reliable preference list with a reasonable sample size. We also adapt …

A Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems
Shuchang Wang, Xiaopeng Qiu, Yingxing Xue, Yanfu Li, Wei Yang
https://arxiv.org/abs/2506.14129

A Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems
Search-based software engineering (SBSE) addresses critical optimization challenges in software engineering, including the next release problem (NRP) and feature selection problem (FSP). While traditional heuristic approaches and integer linear programming (ILP) methods have demonstrated efficacy for small to medium-scale problems, their scalability to large-scale instances remains unknown. Here, we introduce quantum annealing (QA) as a subroutine to tackling multi-objective SBSE problems, leve…

ELATE: Evolutionary Language model for Automated Time-series Engineering
Andrew Murray, Danial Dervovic, Michael Cashmore
https://arxiv.org/abs/2508.14667 https://

ELATE: Evolutionary Language model for Automated Time-series Engineering
Time-series prediction involves forecasting future values using machine learning models. Feature engineering, whereby existing features are transformed to make new ones, is critical for enhancing model performance, but is often manual and time-intensive. Existing automation attempts rely on exhaustive enumeration, which can be computationally costly and lacks domain-specific insights. We introduce ELATE (Evolutionary Language model for Automated Time-series Engineering), which leverages a langu…

Language-Guided Tuning: Enhancing Numeric Optimization with Textual Feedback
Yuxing Lu, Yucheng Hu, Nan Sun, Xukai Zhao
https://arxiv.org/abs/2508.15757 https://

Language-Guided Tuning: Enhancing Numeric Optimization with Textual Feedback
Configuration optimization remains a critical bottleneck in machine learning, requiring coordinated tuning across model architecture, training strategy, feature engineering, and hyperparameters. Traditional approaches treat these dimensions independently and lack interpretability, while recent automated methods struggle with dynamic adaptability and semantic reasoning about optimization decisions. We introduce Language-Guided Tuning (LGT), a novel framework that employs multi-agent Large Langua…

Analyzing PDFs like Binaries: Adversarially Robust PDF Malware Analysis via Intermediate Representation and Language Model
Side Liu, Jiang Ming, Guodong Zhou, Xinyi Liu, Jianming Fu, Guojun Peng
https://arxiv.org/abs/2506.17162

Analyzing PDFs like Binaries: Adversarially Robust PDF Malware Analysis via Intermediate Representation and Language Model
Malicious PDF files have emerged as a persistent threat and become a popular attack vector in web-based attacks. While machine learning-based PDF malware classifiers have shown promise, these classifiers are often susceptible to adversarial attacks, undermining their reliability. To address this issue, recent studies have aimed to enhance the robustness of PDF classifiers. Despite these efforts, the feature engineering underlying these studies remains outdated. Consequently, even with the appli…

Discovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener
Michael A. Kraus, Helen Bartsch
https://arxiv.org/abs/2507.02005

Discovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener
This research introduces a unified approach combining Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to predict fatigue strength in welded transverse stiffener details. It integrates expert-driven feature engineering with algorithmic feature creation to enhance accuracy and explainability. Based on the extensive fatigue test database regression models - gradient boosting, random forests, and neural networks - were trained using AutoML under three feature sc…

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Shahana Yasmin Chowdhury, Bithi Banik, Md Tamjidul Hoque, Shreya Banerjee
https://arxiv.org/abs/2507.07046

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering
Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achieves high accuracy on individual datasets, including 97.83% on RAVDESS, 97.02% on SAVEE, 95.10% for …

We've Issued Our First IP Address Certificate
#LetsEncrypt

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms
Zhaokun Jiang, Ziyin Zhang
https://arxiv.org/abs/2508.10860

From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms
Recent advancements in machine learning have spurred growing interests in automated interpreting quality assessment. Nevertheless, existing research suffers from insufficient examination of language use quality, unsatisfactory modeling effectiveness due to data scarcity and imbalance, and a lack of efforts to explain model predictions. To address these gaps, we propose a multi-dimensional modeling framework that integrates feature engineering, data augmentation, and explainable machine learning…

A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
Amro M. Farid, Amirreza Hosseini, John C. Little
https://arxiv.org/abs/2505.24046

A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
A defining feature of twenty first century engineering challenges is their inherent complexity, demanding the convergence of knowledge across diverse disciplines. Establishing consistent methodological foundations for engineering systems remains a challenge -- one that both systems engineering and network science have sought to address. Model-based systems engineering (MBSE) has recently emerged as a practical, interdisciplinary approach for developing complex systems from concept through imple…

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Remote fetch replies is set as experimental and also asynchronous, unclear if it would be ready for the final release. https://mastodon.social/@MastodonEngineering/114625074809479231

Deep Generative Models in Condition and Structural Health Monitoring: Opportunities, Limitations and Future Outlook
Xin Yang, Chen Fang, Yunlai Liao, Jian Yang, Konstantinos Gryllias, Dimitrios Chronopoulos
https://arxiv.org/abs/2507.15026

Deep Generative Models in Condition and Structural Health Monitoring: Opportunities, Limitations and Future Outlook
Condition and structural health monitoring (CM/SHM) is a pivotal component of predictive maintenance (PdM) strategies across diverse industrial sectors, including mechanical rotating machinery, airplane composite wings, offshore wind turbines, and civil engineering structures. Conventional deep learning models, while effective in fault diagnosis and anomaly detection through supervised feature extraction and rule-based data augmentation, often struggle with operational variability, imbalanced o…

Measuring the splashback feature: Dependence on halo properties and history
Qiaorong S. Yu (Oxford Math, Oxford Physics), Stephanie O'Neil (UPenn, Princeton, MIT), Xuejian Shen (MIT), Mark Vogelsberger (MIT), Sownak Bose (Durham), Boryana Hadzhyska (Lawrence Berkeley Lab, UC Berkeley, Cambridge Astronomy, Kavli Institute Cambridge), Lars Hernquist (CfA Harvard), Rahul Kannan (York), Monica Wu (NYU Math), Ziang Wu (NYU Math, NYU Engineering)

Measuring the splashback feature: Dependence on halo properties and history
In this study, we define the novel splashback depth $\mathcal{D}$ and width $\mathcal{W}$ to examine how the splashback features of dark matter haloes are affected by the physical properties of haloes themselves. We use the largest simulation run in the hydrodynamic MillenniumTNG project. By stacking haloes in bins of halo mass, redshift, mass-dependent properties such as peak height and concentration, and halo formation history, we measure the shape of the logarithmic slope of the density prof…

Inducing ferromagnetism by structural engineering in a strongly spin-orbit coupled oxide
Ji Soo Lim, Carmine Autieri, Merit Spring, Martin Kamp, Amar Fakhredine, Pavel Potapov, Daniel Wolf, Sergii Pylypenko, Axel Lubk, Johannes Schultz, Nicolas Perez, B\"orge Mehlhorn, Louis Veyrat, Mario Cuoco, Fadi Choueikan, Philippe Ohresser, Bernd B\"uchner, Giorgio Sangiovanni, Ralph Claessen, Michael Sing

Inducing ferromagnetism by structural engineering in a strongly spin-orbit coupled oxide
Magnetic materials with strong spin-orbit coupling (SOC) are essential for the advancement of spin-orbitronic devices, as they enable efficient spin-charge conversion, complex magnetic structures, spin-valley physics, topological phases and other exotic phenomena. 5d transition-metal oxides such as SrIrO3 feature large SOC, but usually show paramagnetic behavior due to broad bands and a low density of states at the Fermi level, accompanied by a relatively low Coulomb repulsion. Here, we unveil …

On the structure and dynamics of secondary flows over multi-column roughness in turbulent boundary layers
A. S. Sathe, W. Anderson, M. Calaf, M. G. Giometto
https://arxiv.org/abs/2508.05889

On the structure and dynamics of secondary flows over multi-column roughness in turbulent boundary layers
Secondary flows induced by spanwise heterogeneous surface roughness play a crucial role in determining engineering-relevant metrics such as surface drag, convective heat transfer, and the transport of airborne scalars. While much of the existing literature has focused on idealized configurations with regularly spaced roughness elements, real-world surfaces often feature irregularities, clustering, and topographic complexity for which the secondary flow response remains poorly understood. To add…

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Abhiram Bhupatiraju, Sung Bum Ahn
https://arxiv.org/abs/2507.22220

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics, capturing non-linear patterns is essential for long-term planning. This paper presents a comparative analysis of machine learning models, Linear Regression, XGBoost, LightGBM, and Long Short-Term Memory (LSTM), for forecasting system-wide electricity load up to one year in advance. Midterm forecasting has shown to be …

Creating a customisable freely-accessible Socratic AI physics tutor
Eugenio Tufino, Bor Gregorcic
https://arxiv.org/abs/2507.05795 https://arxiv.org/pdf/25…

Creating a customisable freely-accessible Socratic AI physics tutor
This paper explores role engineering as an effective paradigm for customizing Large Language Models (LLMs) into specialized AI tutors for physics education. We demonstrate this methodology by designing a Socratic physics problem-solving tutor using Google's Gemini Gems feature, defining its pedagogical behavior through a detailed 'script' that specifies its role and persona. We present two illustrative use cases: the first demonstrates the Gem's multimodal ability to analyze a student's hand-dr…

Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone
Michael Cohoon, Debbie Furman
https://arxiv.org/abs/2507.22064

Machine Learning Experiences: A story of learning AI for use in enterprise software testing that can be used by anyone
This paper details the machine learning (ML) journey of a group of people focused on software testing. It tells the story of how this group progressed through a ML workflow (similar to the CRISP-DM process). This workflow consists of the following steps and can be used by anyone applying ML techniques to a project: gather the data; clean the data; perform feature engineering on the data; splitting the data into two sets, one for training and one for testing; choosing a machine learning model; t…

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance
Luciano Ribeiro Galv\~ao, Rafael de Andrade Moral
https://arxiv.org/abs/2506.05609

Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance
In this study, machine learning models were tested to predict whether or not a customer of an insurance company would purchase a travel insurance product. For this purpose, secondary data provided by an open-source website that compiles databases from statistical modeling competitions were used. The dataset used presents approximately 2,700 records from an unidentified company in the tourism insurance sector. Initially, the feature engineering stage was carried out, which were selected through …

Benchmarking Large Language Models for Polymer Property Predictions
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad
https://arxiv.org/abs/2506.02129

Benchmarking Large Language Models for Polymer Property Predictions
Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune …