pwc-archive/papers-with-abstracts huggingface dataset:
AI Approach for Predicting Superhyrophobicity of Thermal Sprayed Copper Coated Aluminum Surfaces
Authors: Mahule Roy
Date: 2025-05-27 00:00:00
Proceeding: Journal of Diagnosis & Case Reports 2025 5

Wettability, characterized by the contact angle of a liquid on a surface, is a critical property that influences numerous natural and industrial applications. In this study, I have developed a CNN-based model to predict the hydrophobicity or super-hydrophobicity of copper-coated aluminum surfaces treated with various reagents or etchants. The data set has been created by analyzing copper-coated aluminum samples with a 3D non-contact profilometer, and contact angle measurements were done to correlate surface properties with the resultant contact angle values. After reagent treatments, the approach had been to preprocess 3D profilometer images to extract surface morphology and structure features. These images and associated contact angle measurements were used as inputs to train the CNN model to classify whether the treated surfaces are hydrophobic or super-hydrophobic. Although the model may initially
have limited training accuracy, this study demonstrates the potential of deep learning to predict wettability based on surface characteristics. The results also highlight the need for improvements, such as data set expansion, the inclusion of more varied reagent treatments, and the exploration of hybrid modeling approaches to enhance the model
SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation
Authors: Janghyeon Yun, Sang-goo Lee
Date: 2025-06-09 00:00:00
Tasks: Natural Language Queries, Text to SQL, Text-To-SQL

Text-to-SQL enables non-experts to retrieve data from databases by converting natural language queries into SQL. However, state-of-the-art text-to-SQL studies rely on the BIRD dataset, which assumes that evidence is provided along with questions. Although BIRD facilitates research advancements, it assumes that users have expertise and domain knowledge, contradicting the fundamental goal of text-to-SQL. In addition, human-generated evidence in BIRD contains defects, including missing or erroneous evidence, which affects model performance. To address this issue, we propose SEED (System for Evidence Extraction and Domain knowledge generation), an approach that automatically generates evidence to improve performance and practical usability in real-world scenarios. SEED systematically analyzes database schema, description files, and values to extract relevant information. We evaluated SEED on BIRD and Spider, demonstrating that it significantly improves SQL generation accuracy in the no-evidence scenario, and in some cases, even outperforms the setting where BIRD evidence is provided. Our results highlight that SEED-generated evidence not only bridges the gap between research and real-world deployment but also improves the adaptability and robustness of text-to-SQL models. Our code is available at https://github.com/felix01189/SEED
Improving deep learning in arrhythmia Detection: The application of modular quality and quantity controllers in data augmentation
Authors: Mohammad Usef Khosravi Khaliran, Iman Zabbah, Mehrbod Faraji, Reza Ebrahimpour
Date: 2024-05-10 00:00:00
Proceeding: Biomedical Signal Processing and Control 2024 5
Tasks: Arrhythmia Detection, Data Augmentation

Among the most prevalent diseases with significant fatality rates are cardiac disorders. In recent years, the application of deep learning in diagnosing various cardiac conditions, namely arrhythmia, has gained widespread attention. Nevertheless, deep neural networks struggle to detect arrhythmia due to skewed datasets and a lack of data in different classes. If used effectively, data augmentation can address this gap by adding further synthetic samples in the correct distribution of the corresponding skewed classes. To achieve this, we have instituted the Modular Distribution and Volume Controller method, abbreviated as MDVC. Our method concentrates on both qualitative and quantitative aspects of data augmentation to elevate efficiency and create a significant and varied amount of synthetic samples. Seven distinct methods of data augmentation are utilized in fusion to synthesize samples. Subsequently, the distribution controller determines the most advantageous distribution of artificial samples for each data augmentation technique, emphasizing the dispersion and collision of different classes. The maximum overall data augmentation volume, the volume of each class, and the volume of each data augmentation technique are defined by the volume controller through the novel x, α, and β parameters. Classifying the 17 classes of the MIT-BIH dataset using the MDVC yielded an accuracy of 98.9 % using a 10-fold cross-validation strategy; thus, we have outperformed state-of-the-art data augmentation techniques such as RandAugment and α-trim by 1.3 % and 0.8 %, respectively.
Self-Supervised Multiview Xray Matching
Authors: Mohamad Dabboussi, Malo Huard, Yann Gousseau, Pietro Gori
Date: 2025-06-30 00:00:00
Tasks: Fracture detection

Accurate interpretation of multi-view radiographs is crucial for diagnosing fractures, muscular injuries, and other anomalies. While significant advances have been made in AI-based analysis of single images, current methods often struggle to establish robust correspondences between different X-ray views, an essential capability for precise clinical evaluations. In this work, we present a novel self-supervised pipeline that eliminates the need for manual annotation by automatically generating a many-to-many correspondence matrix between synthetic X-ray views. This is achieved using digitally reconstructed radiographs (DRR), which are automatically derived from unannotated CT volumes. Our approach incorporates a transformer-based training phase to accurately predict correspondences across two or more X-ray views. Furthermore, we demonstrate that learning correspondences among synthetic X-ray views can be leveraged as a pretraining strategy to enhance automatic multi-view fracture detection on real data. Extensive evaluations on both synthetic and real X-ray datasets show that incorporating correspondences improves performance in multi-view fracture classification.
Large Language Models Don't Make Sense of Word Problems. A Scoping Review from a Mathematics Education Perspective
Authors: Anselm R. Strohmaier, Wim Van Dooren, Kathrin Seßler, Brian Greer, Lieven Verschaffel
Date: 2025-06-30 00:00:00
Tasks: Mathematical Reasoning

The progress of Large Language Models (LLMs) like ChatGPT raises the question of how they can be integrated into education. One hope is that they can support mathematics learning, including word-problem solving. Since LLMs can handle textual input with ease, they appear well-suited for solving mathematical word problems. Yet their real competence, whether they can make sense of the real-world context, and the implications for classrooms remain unclear. We conducted a scoping review from a mathematics-education perspective, including three parts: a technical overview, a systematic review of word problems used in research, and a state-of-the-art empirical evaluation of LLMs on mathematical word problems. First, in the technical overview, we contrast the conceptualization of word problems and their solution processes between LLMs and students. In computer-science research this is typically labeled mathematical reasoning, a term that does not align with usage in mathematics education. Second, our literature review of 213 studies shows that the most popular word-problem corpora are dominated by s-problems, which do not require a consideration of realities of their real-world context. Finally, our evaluation of GPT-3.5-turbo, GPT-4o-mini, GPT-4.1, and o3 on 287 word problems shows that most recent LLMs solve these s-problems with near-perfect accuracy, including a perfect score on 20 problems from PISA. LLMs still showed weaknesses in tackling problems where the real-world context is problematic or non-sensical. In sum, we argue based on all three aspects that LLMs have mastered a superficial solution process but do not make sense of word problems, which potentially limits their value as instructional tools in mathematics classrooms.
Geometric Gaussian Approximations of Probability Distributions
Authors: Nathaël Da Costa, Bálint Mucsányi, Philipp Hennig
Date: 2025-07-01 00:00:00

Approximating complex probability distributions, such as Bayesian posterior distributions, is of central interest in many applications. We study the expressivity of geometric Gaussian approximations. These consist of approximations by Gaussian pushforwards through diffeomorphisms or Riemannian exponential maps. We first review these two different kinds of geometric Gaussian approximations. Then we explore their relationship to one another. We further provide a constructive proof that such geometric Gaussian approximations are universal, in that they can capture any probability distribution. Finally, we discuss whether, given a family of probability distributions, a common diffeomorphism can be found to obtain uniformly high-quality geometric Gaussian approximations for that family.
LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs
Authors: Boyuan Sun, Jiaxing Zhao, Xihan Wei, Qibin Hou
Date: 2025-06-27 00:00:00
Tasks: Question Answering, Video Question Answering, Video Understanding

In this paper, we present LLaVA-Scissor, a training-free token compression strategy designed for video multimodal large language models. Previous methods mostly attempt to compress tokens based on attention scores, but fail to effectively capture all semantic regions and often lead to token redundancy. Differently, we propose to leverage the Semantic Connected Components (SCC) approach that assigns tokens to distinct semantic regions within the token set, ensuring comprehensive semantic coverage. The outcome is a two-step spatio-temporal token compression strategy that utilizes SCC in both spatial and temporal domains. This strategy can effectively compress tokens by representing the entire video with a set of non-overlapping semantic tokens. We conduct extensive evaluations of the token compression capabilities of LLaVA-Scissor across diverse video understanding benchmarks, including video question answering, long video understanding, and comprehensive multi-choices benchmarks. Experimental results show that the proposed LLaVA-Scissor outperforms other token compression methods, achieving superior performance in various video understanding benchmarks, particularly at low token retention ratios. Project page: https://github.com/HumanMLLM/LLaVA-Scissor.
SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
Authors: Devin Y. De Silva, Sandareka Wickramanayake, Dulani Meedeniya, Sanka Rasnayaka
Date: 2025-06-25 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Zero-Shot Learning

Human Activity Recognition (HAR), which uses data from Inertial Measurement Unit (IMU) sensors, has many practical applications in healthcare and assisted living environments. However, its use in real-world scenarios has been limited by the lack of comprehensive IMU-based HAR datasets that cover a wide range of activities and the lack of transparency in existing HAR models. Zero-shot HAR (ZS-HAR) overcomes the data limitations, but current models struggle to explain their decisions, making them less transparent. This paper introduces a novel IMU-based ZS-HAR model called the Self-Explainable Zero-shot Human Activity Recognition Network (SEZ-HARN). It can recognize activities not encountered during training and provide skeleton videos to explain its decision-making process. We evaluate the effectiveness of the proposed SEZ-HARN on four benchmark datasets PAMAP2, DaLiAc, HTD-MHAD and MHealth and compare its performance against three state-of-the-art black-box ZS-HAR models. The experiment results demonstrate that SEZ-HARN produces realistic and understandable explanations while achieving competitive Zero-shot recognition accuracy. SEZ-HARN achieves a Zero-shot prediction accuracy within 3\% of the best-performing black-box model on PAMAP2 while maintaining comparable performance on the other three datasets.
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories
Authors: Abhishek Vivekanandan, Christian Hubschneider, J. Marius Zöllner
Date: 2025-06-03 00:00:00
Tasks: Autonomous Navigation, Contrastive Learning, Motion Forecasting, Retrieval, Triplet

The ability to retrieve semantically and directionally similar short-range trajectories with both accuracy and efficiency is foundational for downstream applications such as motion forecasting and autonomous navigation. However, prevailing approaches often depend on computationally intensive heuristics or latent anchor representations that lack interpretability and controllability. In this work, we propose a novel framework for learning fixed-dimensional embeddings for short trajectories by leveraging a Transformer encoder trained with a contrastive triplet loss that emphasize the importance of discriminative feature spaces for trajectory data. We analyze the influence of Cosine and FFT-based similarity metrics within the contrastive learning paradigm, with a focus on capturing the nuanced directional intent that characterizes short-term maneuvers. Our empirical evaluation on the Argoverse 2 dataset demonstrates that embeddings shaped by Cosine similarity objectives yield superior clustering of trajectories by both semantic and directional attributes, outperforming FFT-based baselines in retrieval tasks. Notably, we show that compact Transformer architectures, even with low-dimensional embeddings (e.g., 16 dimensions, but qualitatively down to 4), achieve a compelling balance between retrieval performance (minADE, minFDE) and computational overhead, aligning with the growing demand for scalable and interpretable motion priors in real-time systems. The resulting embeddings provide a compact, semantically meaningful, and efficient representation of trajectory data, offering a robust alternative to heuristic similarity measures and paving the way for more transparent and controllable motion forecasting pipelines.
MambAttention: Mamba with Multi-Head Attention for Generalizable Single-Channel Speech Enhancement
Authors: Nikolai Lund Kühne, Jesper Jensen, Jan Østergaard, Zheng-Hua Tan
Date: 2025-07-01 00:00:00
Tasks: Automatic Speech Recognition, Mamba, Speech Enhancement, speech-recognition, Speech Recognition

With the advent of new sequence models like Mamba and xLSTM, several studies have shown that these models match or outperform state-of-the-art models in single-channel speech enhancement, automatic speech recognition, and self-supervised audio representation learning. However, prior research has demonstrated that sequence models like LSTM and Mamba tend to overfit to the training set. To address this issue, previous works have shown that adding self-attention to LSTMs substantially improves generalization performance for single-channel speech enhancement. Nevertheless, neither the concept of hybrid Mamba and time-frequency attention models nor their generalization performance have been explored for speech enhancement. In this paper, we propose a novel hybrid architecture, MambAttention, which combines Mamba and shared time- and frequency-multi-head attention modules for generalizable single-channel speech enhancement. To train our model, we introduce VoiceBank+Demand Extended (VB-DemandEx), a dataset inspired by VoiceBank+Demand but with more challenging noise types and lower signal-to-noise ratios. Trained on VB-DemandEx, our proposed MambAttention model significantly outperforms existing state-of-the-art LSTM-, xLSTM-, Mamba-, and Conformer-based systems of similar complexity across all reported metrics on two out-of-domain datasets: DNS 2020 and EARS-WHAM_v2, while matching their performance on the in-domain dataset VB-DemandEx. Ablation studies highlight the role of weight sharing between the time- and frequency-multi-head attention modules for generalization performance. Finally, we explore integrating the shared time- and frequency-multi-head attention modules with LSTM and xLSTM, which yields a notable performance improvement on the out-of-domain datasets. However, our MambAttention model remains superior on both out-of-domain datasets across all reported evaluation metrics.
Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systems
Authors: Pedro R. Pires, Tiago A. Almeida
Date: 2025-06-27 00:00:00
Tasks: Efficient Neural Network, Recommendation Systems

Over the past decade, recommender systems have experienced a surge in popularity. Despite notable progress, they grapple with challenging issues, such as high data dimensionality and sparseness. Representing users and items as low-dimensional embeddings learned via neural networks has become a leading solution. However, while recent studies show promising results, many approaches rely on complex architectures or require content data, which may not always be available. This paper presents Interact2Vec, a novel neural network-based model that simultaneously learns distributed embeddings for users and items while demanding only implicit feedback. The model employs state-of-the-art strategies that natural language processing models commonly use to optimize the training phase and enhance the final embeddings. Two types of experiments were conducted regarding the extrinsic and intrinsic quality of the model. In the former, we benchmarked the recommendations generated by Interact2Vec's embeddings in a top-$N$ ranking problem, comparing them with six other recommender algorithms. The model achieved the second or third-best results in 30\% of the datasets, being competitive with other recommenders, and has proven to be very efficient with an average training time reduction of 274\% compared to other embedding-based models. Later, we analyzed the intrinsic quality of the embeddings through similarity tables. Our findings suggest that Interact2Vec can achieve promising results, especially on the extrinsic task, and is an excellent embedding-generator model for scenarios of scarce computing resources, enabling the learning of item and user embeddings simultaneously and efficiently.
CaloHadronic: a diffusion model for the generation of hadronic showers
Authors: Thorsten Buss, Frank Gaede, Gregor Kasieczka, Anatolii Korol, Katja Krüger, Peter McKeown, Martina Mozzanica
Date: 2025-06-26 00:00:00

Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint. Recent developments have shown how diffusion based generative shower simulation approaches that do not rely on a fixed structure, but instead generate geometry-independent point clouds, are very efficient. We present a transformer-based extension to previous architectures which were developed for simulating electromagnetic showers in the highly granular electromagnetic calorimeter of the International Large Detector, ILD. The attention mechanism now allows us to generate complex hadronic showers with more pronounced substructure across both the electromagnetic and hadronic calorimeters. This is the first time that machine learning methods are used to holistically generate showers across the electromagnetic and hadronic calorimeter in highly granular imaging calorimeter systems.
SundayPaulneo
Authors: Sundaypaulneo
Date: 2012-03-24 00:00:00
Proceeding: fhg 2012 3
Tasks: Marketing

'''Early life'''

 '''SundayPaulneo''' Chidiebube who is born on / 24/ 2012/ March in ilorin, My parent are Mr. and Mrs. Sunday who are native of Ebonyi, but they live in ilorin kwara state, Nigeria. I went to Gifted Kiddies Model Nursery and Primary School in ilorin, when i was in pry 5 i graduated from primary school and still continue with the Secondary School, Which is Gifted Model Secondary School . When i was in JSS3 class, i was introduced to a great business know as Network Marketing ([Neolife][1]) as he joined the business he has became an expert freelancer working on Fiverr, Upwork, PeoplePerHour, kwork, Latium and many freelance platform with many project ( Social Media marketer, Programming projects, Digital Marketing and so on) but he sometimes specialized on Digital Marketing service: Google knowledge panel, Tiktok promotion, Instagram promotion Google review removing and adding, TikTok promotion and followers, IMDb page creation, wikipedia page creation, crunchbase and so on. He has been doing this for the past 2 years.

'''Career'''

'''SundayPaulneo''' start his career in the years 2025 , Because he join a great and awesome business opportunity call [2 in 1 business opportunity] Neolife. And he is also an expert in online business that he specialize on Digital marketing, social media marketing, Seo expert, Shopify marketing, dropshipping, promotion and so on. And he is working on some freelancer platform like [Upwork][3], [fiverr][4], [kwork][5], [PeoplePerHour][6], [Latium] [7]and so on
Potential Customer Lifetime Value in Financial Institutions: The Usage Of Open Banking Data to Improve CLV Estimation
Authors: João B. G. de Brito, Rodrigo Heldt, Cleo S. Silveira, Matthias Bogaert, Guilherme B. Bucco, Fernando B. Luce, João L. Becker, Filipe J. Zabala, Michel J. Anzanello
Date: 2025-06-28 00:00:00
Tasks: Marketing

Financial institutions increasingly adopt customer-centric strategies to enhance profitability and build long-term relationships. While Customer Lifetime Value (CLV) is a core metric, its calculations often rely solely on single-entity data, missing insights from customer activities across multiple firms. This study introduces the Potential Customer Lifetime Value (PCLV) framework, leveraging Open Banking (OB) data to estimate customer value comprehensively. We predict retention probability and estimate Potential Contribution Margins (PCM) from competitor data, enabling PCLV calculation. Results show that OB data can be used to estimate PCLV per competitor, indicating a potential upside of 21.06% over the Actual CLV. PCLV offers a strategic tool for managers to strengthen competitiveness by leveraging OB data and boost profitability by driving marketing efforts at the individual customer level to increase the Actual CLV.
Integrating Traditional and Deep Learning Methods to Detect Tree Crowns in Satellite Images
Authors: Ozan Durgut, Beril Kallfelz-Sirmacek, Cem Unsalan
Date: 2025-07-02 00:00:00

Global warming, loss of biodiversity, and air pollution are among the most significant problems facing Earth. One of the primary challenges in addressing these issues is the lack of monitoring forests to protect them. To tackle this problem, it is important to leverage remote sensing and computer vision methods to automate monitoring applications. Hence, automatic tree crown detection algorithms emerged based on traditional and deep learning methods. In this study, we first introduce two different tree crown detection methods based on these approaches. Then, we form a novel rule-based approach that integrates these two methods to enhance robustness and accuracy of tree crown detection results. While traditional methods are employed for feature extraction and segmentation of forested areas, deep learning methods are used to detect tree crowns in our method. With the proposed rule-based approach, we post-process these results, aiming to increase the number of detected tree crowns through neighboring trees and localized operations. We compare the obtained results with the proposed method in terms of the number of detected tree crowns and report the advantages, disadvantages, and areas for improvement of the obtained outcomes.